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547 -0.00019272613493773 + 548 -0.0018686456399957 + 549 0.00020286340994351 + 550 0.00052631265775171 + 551 4.670196662812e-05 + 552 0.00084087074508056 diff --git a/examples/tetracene_dimer/Cfrags.npy b/examples/tetracene_dimer/Cfrags.npy new file mode 100644 index 0000000..c1259bf Binary files /dev/null and b/examples/tetracene_dimer/Cfrags.npy differ diff --git a/examples/tetracene_dimer/cis_sa_density_mat18.npy b/examples/tetracene_dimer/cis_sa_density_mat18.npy new file mode 100644 index 0000000..8dd9e4a Binary files /dev/null and b/examples/tetracene_dimer/cis_sa_density_mat18.npy differ diff --git a/examples/tetracene_dimer/cmf.jl b/examples/tetracene_dimer/cmf.jl new file mode 100644 index 0000000..680dee9 --- /dev/null +++ b/examples/tetracene_dimer/cmf.jl @@ -0,0 +1,43 @@ +using ClusterMeanField +using RDM +using QCBase +using InCoreIntegrals +using PyCall +using ActiveSpaceSolvers +using LinearAlgebra +using Printf +using NPZ +using JLD2 + +h0 = npzread("./integrals_h0_12.npy") +h1 = npzread("./integrals_h1_12.npy") +h2 = npzread("./integrals_h2_12.npy") + +ints = InCoreInts(h0, h1, h2) + + + +na = 6 +nb = 6 + +clusters = [[1,2,3,4,5,6],[7,8,9,10,11,12]] +init_fspace = [(3,3),(3,3)] + + +rdm1 = RDM1(n_orb(ints)) + +# define clusters +clusters = [MOCluster(i,collect(clusters[i])) for i = 1:length(clusters)] +display(clusters) +ansatze = [FCIAnsatz(6, 3, 3),FCIAnsatz(6, 3, 3)] +@time e_cmf, U, d1 = ClusterMeanField.cmf_oo_newton(ints, clusters, init_fspace,ansatze,rdm1, maxiter_oo = 400, + tol_oo=1e-8, + tol_d1=1e-9, + tol_ci=1e-11, + verbose=4, + zero_intra_rots = true, + sequential=true) + +ints = orbital_rotation(ints, U) + +@save "data_cmf_TD_12.jld2" clusters init_fspace ints d1 e_cmf U diff --git a/examples/tetracene_dimer/cmf.jl.out b/examples/tetracene_dimer/cmf.jl.out new file mode 100644 index 0000000..998a0ad --- /dev/null +++ b/examples/tetracene_dimer/cmf.jl.out @@ -0,0 +1,170 @@ +IDX001:DIM4096:001|002|003|004|005|006| +IDX002:DIM4096:007|008|009|010|011|012| + Solve OO-CMF with newton + + ------------------------------------------ + CMF CI Iter: 1 + ------------------------------------------ +Overwritten attributes conv_tol_residual of + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.24940798 + MOCluster 2 E = -7.20140412 + CMF-CI Curr: Elec -11.48741462 Total -1377.27916386 + CMF-CI Energy: -1377.27916386 | Change: RDM: 4.8e+00 Energy -1.4e+03 + + + ------------------------------------------ + CMF CI Iter: 2 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20682657 + MOCluster 2 E = -7.20656910 + CMF-CI Curr: Elec -11.53062786 Total -1377.32237709 + CMF-CI Energy: -1377.32237709 | Change: RDM: 7.0e-01 Energy -4.3e-02 + + + ------------------------------------------ + CMF CI Iter: 3 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20650046 + MOCluster 2 E = -7.20658890 + CMF-CI Curr: Elec -11.53062855 Total -1377.32237778 + CMF-CI Energy: -1377.32237778 | Change: RDM: 2.8e-03 Energy -6.9e-07 + + + ------------------------------------------ + CMF CI Iter: 4 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20649911 + MOCluster 2 E = -7.20658898 + CMF-CI Curr: Elec -11.53062855 Total -1377.32237778 + CMF-CI Energy: -1377.32237778 | Change: RDM: 1.2e-05 Energy -1.2e-11 + + + ------------------------------------------ + CMF CI Iter: 5 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20649910 + MOCluster 2 E = -7.20658898 + CMF-CI Curr: Elec -11.53062855 Total -1377.32237778 + CMF-CI Energy: -1377.32237778 | Change: RDM: 5.3e-08 Energy 0.0e+00 + + + ------------------------------------------ + CMF CI Iter: 6 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20649910 + MOCluster 2 E = -7.20658898 + CMF-CI Curr: Elec -11.53062855 Total -1377.32237778 + CMF-CI Energy: -1377.32237778 | Change: RDM: 2.7e-10 Energy 0.0e+00 + +*CMF-CI: Elec -11.53062855 Total -1377.32237778 + Energy per Iteration: + Elec: -11.48741462 Total: -1377.27916386 + Elec: -11.53062786 Total: -1377.32237709 + Elec: -11.53062855 Total: -1377.32237778 + Elec: -11.53062855 Total: -1377.32237778 + Elec: -11.53062855 Total: -1377.32237778 + Elec: -11.53062855 Total: -1377.32237778 + Step: 1 E: -1377.322377780242 G: 1.65e-02 step_size: 1.66e-02 + + ------------------------------------------ + CMF CI Iter: 1 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670775 + MOCluster 2 E = -7.20679617 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 4.0e-02 Energy -1.4e+03 + + + ------------------------------------------ + CMF CI Iter: 2 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670654 + MOCluster 2 E = -7.20679622 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 1.6e-05 Energy -2.4e-11 + + + ------------------------------------------ + CMF CI Iter: 3 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670654 + MOCluster 2 E = -7.20679622 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 1.1e-07 Energy 0.0e+00 + + + ------------------------------------------ + CMF CI Iter: 4 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670654 + MOCluster 2 E = -7.20679622 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 7.5e-10 Energy 0.0e+00 + +*CMF-CI: Elec -11.53075365 Total -1377.32250289 + Energy per Iteration: + Elec: -11.53075365 Total: -1377.32250289 + Elec: -11.53075365 Total: -1377.32250289 + Elec: -11.53075365 Total: -1377.32250289 + Elec: -11.53075365 Total: -1377.32250289 + Step: 2 E: -1377.322502887952 G: 1.84e-06 step_size: 3.77e-06 + + ------------------------------------------ + CMF CI Iter: 1 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670656 + MOCluster 2 E = -7.20679623 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 4.7e-06 Energy -1.4e+03 + + + ------------------------------------------ + CMF CI Iter: 2 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670655 + MOCluster 2 E = -7.20679623 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 2.6e-09 Energy 2.3e-13 + + + ------------------------------------------ + CMF CI Iter: 3 + ------------------------------------------ + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + FCIAnsatz:: #Orbs = 6 #α = 3 #β = 3 Dimension: 400 + MOCluster 1 E = -7.20670655 + MOCluster 2 E = -7.20679623 + CMF-CI Curr: Elec -11.53075365 Total -1377.32250289 + CMF-CI Energy: -1377.32250289 | Change: RDM: 1.9e-11 Energy -2.3e-13 + +*CMF-CI: Elec -11.53075365 Total -1377.32250289 + Energy per Iteration: + Elec: -11.53075365 Total: -1377.32250289 + Elec: -11.53075365 Total: -1377.32250289 + Elec: -11.53075365 Total: -1377.32250289 + Step: 3 E: -1377.322502887954 G: 6.49e-10 + 64.409905 seconds (131.48 M allocations: 7.043 GiB, 4.77% gc time, 94.36% compilation time: 0% of which was recompilation) diff --git a/examples/tetracene_dimer/data_cmf_TD_12.jld2 b/examples/tetracene_dimer/data_cmf_TD_12.jld2 new file mode 100644 index 0000000..d76da49 Binary files /dev/null and b/examples/tetracene_dimer/data_cmf_TD_12.jld2 differ diff --git a/examples/tetracene_dimer/del.jl b/examples/tetracene_dimer/del.jl new file mode 100644 index 0000000..6fc52fd --- /dev/null +++ b/examples/tetracene_dimer/del.jl @@ -0,0 +1,30 @@ +e_ci, v = FermiCG.ci_solve(ci_vector, cluster_ops, clustered_ham); +@save "data_ci.jld2" v e_ci + +#v = BSTstate(v,R=2) + +v1 = BSTstate(v,R=1) +v2 = BSTstate(v,R=1) +FermiCG.set_vector!(v2, FermiCG.get_vector(v)[:,8]) +σ1 = FermiCG.build_compressed_1st_order_state(v1, cluster_ops, clustered_ham, + nbody=4, + thresh=1e-7, + compress_twice=false) + +σ2 = FermiCG.build_compressed_1st_order_state(v2, cluster_ops, clustered_ham, + nbody=4, + thresh=1e-7, + compress_twice=false) + +e_ci, v = FermiCG.ci_solve(v1, cluster_ops, clustered_ham); +e_ci, v = FermiCG.ci_solve(v2, cluster_ops, clustered_ham); +display(FermiCG.nonorth_overlap(σ1,v1)) +display(FermiCG.nonorth_overlap(σ2,v2)) + +#σ = FermiCG.compress(σ, thresh=1e-2) +# +#FermiCG.zero!(σ) +#FermiCG.nonorth_add!(v, σ) +# +#e_ci, v = FermiCG.ci_solve(v, cluster_ops, clustered_ham); +# diff --git a/examples/tetracene_dimer/del2.jl b/examples/tetracene_dimer/del2.jl new file mode 100644 index 0000000..423d3dd --- /dev/null +++ b/examples/tetracene_dimer/del2.jl @@ -0,0 +1,54 @@ + +using QCBase +using Printf +using FermiCG +using NPZ +using InCoreIntegrals +using RDM +using JLD2 + +if false +@load "data_cmf_TD_12.jld2" +M = 100 +#@load "cmf_op_TD_with_ops.jld2" +display(clusters) +display(init_fspace) +ref_fspace = FockConfig(init_fspace) +ecore = ints.h0 + +cluster_bases = FermiCG.compute_cluster_eigenbasis_spin(ints, clusters, d1, [5,5], ref_fspace, max_roots=M, verbose=1); + +clustered_ham = FermiCG.extract_ClusteredTerms(ints, clusters) +cluster_ops = FermiCG.compute_cluster_ops(cluster_bases, ints); + +FermiCG.add_cmf_operators!(cluster_ops, cluster_bases, ints, d1.a, d1.b); +@save "cmf_op_TD_with_ops.jld2" clusters init_fspace ints cluster_bases cluster_ops clustered_ham +nroots = 8 + +vcmf = BSTstate(clusters,FermiCG.FockConfig(init_fspace), cluster_bases, R=1); +end + +σ = FermiCG.build_compressed_1st_order_state(vcmf, cluster_ops, clustered_ham, + nbody=4, + thresh=1e-3) +σ = FermiCG.compress(σ, thresh=1e-3) +v2 = BSTstate(σ,R=3) +FermiCG.eye!(v2) +e_ci, v2 = FermiCG.ci_solve(v2, cluster_ops, clustered_ham); + +σ = FermiCG.build_compressed_1st_order_state(v2, cluster_ops, clustered_ham, + nbody=4, + thresh=1e-3) +σ = FermiCG.compress(σ, thresh=1e-4) +FermiCG.zero!(σ) +FermiCG.nonorth_add!(v2, σ) +e_ci, v2 = FermiCG.ci_solve(v2, cluster_ops, clustered_ham); + +σ = FermiCG.build_compressed_1st_order_state(v2, cluster_ops, clustered_ham, + nbody=4, + thresh=1e-3) +σ = FermiCG.compress(σ, thresh=1e-2) +FermiCG.zero!(σ) +FermiCG.nonorth_add!(v2, σ) +e_ci, v2 = FermiCG.ci_solve(v2, cluster_ops, clustered_ham); + diff --git a/examples/tetracene_dimer/density_mat18.npy b/examples/tetracene_dimer/density_mat18.npy new file mode 100644 index 0000000..a061529 Binary files /dev/null and b/examples/tetracene_dimer/density_mat18.npy differ diff --git a/examples/tetracene_dimer/dimer.xyz b/examples/tetracene_dimer/dimer.xyz new file mode 100644 index 0000000..7d9cecd --- /dev/null +++ b/examples/tetracene_dimer/dimer.xyz @@ -0,0 +1,62 @@ +60 + +C 3.86480 -1.02720 1.27180 +C 3.60170 -0.34020 0.07040 +C 2.93000 -1.06360 2.30730 +C 4.54410 -0.28950 -0.97720 +C 2.32170 0.33090 -0.09430 +C 3.20030 -1.73210 3.54060 +C 1.65210 -0.39240 2.14450 +C 4.27330 0.38740 -2.16700 +C 2.05960 1.01870 -1.29550 +C 1.37970 0.28170 0.95340 +C 2.27260 -1.73270 4.55160 +C 0.71620 -0.41900 3.22490 +C 2.99590 1.05910 -2.32920 +C 5.20900 0.41800 -3.24760 +C 1.01870 -1.06880 4.39470 +C 2.72630 1.73410 -3.55840 +C 4.90670 1.07300 -4.41500 +C 3.65410 1.73950 -4.56900 +C -3.86650 1.01870 -1.29550 +C -2.93020 1.05910 -2.32920 +C -3.60440 0.33090 -0.09430 +C -3.19980 1.73410 -3.55840 +C -1.65280 0.38740 -2.16700 +C -2.32430 -0.34020 0.07040 +C -4.54630 0.28170 0.95340 +C -2.27200 1.73950 -4.56900 +C -0.71710 0.41800 -3.24760 +C -1.38200 -0.28950 -0.97720 +C -2.06130 -1.02720 1.27180 +C -4.27400 -0.39240 2.14450 +C -1.01930 1.07300 -4.41500 +C -2.99610 -1.06360 2.30730 +C -5.20980 -0.41900 3.22490 +C -2.72580 -1.73210 3.54060 +C -4.90730 -1.06880 4.39470 +C -3.65350 -1.73270 4.55160 +H 4.82300 -1.53290 1.39770 +H 5.49910 -0.80290 -0.85660 +H 4.15900 -2.23700 3.66390 +H 1.10180 1.52560 -1.42170 +H 0.42460 0.79440 0.83100 +H 2.50000 -2.24040 5.48840 +H -0.23700 0.09640 3.10140 +H 6.16210 -0.09790 -3.12730 +H 0.29870 -1.07700 5.21470 +H 1.76850 2.24120 -3.67870 +H 5.62580 1.08320 -5.23570 +H 3.42730 2.25190 -5.50340 +H -4.82430 1.52560 -1.42170 +H -4.15760 2.24120 -3.67870 +H -5.50150 0.79440 0.83100 +H -2.49880 2.25190 -5.50340 +H 0.23610 -0.09790 -3.12730 +H -0.42700 -0.80290 -0.85660 +H -1.10300 -1.53290 1.39770 +H -0.30030 1.08320 -5.23570 +H -6.16310 0.09640 3.10140 +H -1.76710 -2.23700 3.66390 +H -5.62740 -1.07700 5.21470 +H -3.42610 -2.24040 5.48840 diff --git a/examples/tetracene_dimer/fock_mat18.npy b/examples/tetracene_dimer/fock_mat18.npy new file mode 100644 index 0000000..ee15f5a Binary files /dev/null and b/examples/tetracene_dimer/fock_mat18.npy differ diff --git a/examples/tetracene_dimer/integrals_h0_12.npy b/examples/tetracene_dimer/integrals_h0_12.npy new file mode 100644 index 0000000..a79b1f0 Binary files /dev/null and b/examples/tetracene_dimer/integrals_h0_12.npy differ diff --git a/examples/tetracene_dimer/integrals_h1_12.npy b/examples/tetracene_dimer/integrals_h1_12.npy new file mode 100644 index 0000000..746e83a Binary files /dev/null and b/examples/tetracene_dimer/integrals_h1_12.npy differ diff --git a/examples/tetracene_dimer/integrals_h2_12.npy b/examples/tetracene_dimer/integrals_h2_12.npy new file mode 100644 index 0000000..cbd0fc2 Binary files /dev/null and b/examples/tetracene_dimer/integrals_h2_12.npy differ diff --git a/examples/tetracene_dimer/mo_coeffs_act_12.npy b/examples/tetracene_dimer/mo_coeffs_act_12.npy new file mode 100644 index 0000000..2a739ae Binary files /dev/null and b/examples/tetracene_dimer/mo_coeffs_act_12.npy differ diff --git a/examples/tetracene_dimer/mo_coeffs_doc_12.npy b/examples/tetracene_dimer/mo_coeffs_doc_12.npy new file mode 100644 index 0000000..fe06739 Binary files /dev/null and b/examples/tetracene_dimer/mo_coeffs_doc_12.npy differ diff --git a/examples/tetracene_dimer/nat_orb_active.out b/examples/tetracene_dimer/nat_orb_active.out new file mode 100644 index 0000000..115ee65 --- /dev/null +++ b/examples/tetracene_dimer/nat_orb_active.out @@ -0,0 +1,571 @@ +Initial guess from minao. +(552, 552) +(552, 552) + Number of electrons found 240.00000000 + Number of electrons found 240.00000000 + 0 = 1.99999999 DOcc + 1 = 1.99999999 DOcc + 2 = 1.99999999 DOcc + 3 = 1.99999999 DOcc + 4 = 1.99999999 DOcc + 5 = 1.99999999 DOcc + 6 = 1.99999999 DOcc + 7 = 1.99999999 DOcc + 8 = 1.99999999 DOcc + 9 = 1.99999999 DOcc + 10 = 1.99999999 DOcc + 11 = 1.99999999 DOcc + 12 = 1.99999999 DOcc + 13 = 1.99999999 DOcc + 14 = 1.99999999 DOcc + 15 = 1.99999999 DOcc + 16 = 1.99999999 DOcc + 17 = 1.99999999 DOcc + 18 = 1.99999999 DOcc + 19 = 1.99999999 DOcc + 20 = 1.99999999 DOcc + 21 = 1.99999999 DOcc + 22 = 1.99999998 DOcc + 23 = 1.99999998 DOcc + 24 = 1.99999998 DOcc + 25 = 1.99999998 DOcc + 26 = 1.99999998 DOcc + 27 = 1.99999998 DOcc + 28 = 1.99999996 DOcc + 29 = 1.99999996 DOcc + 30 = 1.99999996 DOcc + 31 = 1.99999996 DOcc + 32 = 1.99999996 DOcc + 33 = 1.99999996 DOcc + 34 = 1.99999995 DOcc + 35 = 1.99999995 DOcc + 36 = 1.99998370 DOcc + 37 = 1.99998366 DOcc + 38 = 1.99998308 DOcc + 39 = 1.99998304 DOcc + 40 = 1.99997908 DOcc + 41 = 1.99997906 DOcc + 42 = 1.99997841 DOcc + 43 = 1.99997839 DOcc + 44 = 1.99997676 DOcc + 45 = 1.99997668 DOcc + 46 = 1.99997508 DOcc + 47 = 1.99997495 DOcc + 48 = 1.99997489 DOcc + 49 = 1.99997483 DOcc + 50 = 1.99997458 DOcc + 51 = 1.99997443 DOcc + 52 = 1.99997328 DOcc + 53 = 1.99997319 DOcc + 54 = 1.99997015 DOcc + 55 = 1.99996998 DOcc + 56 = 1.99996890 DOcc + 57 = 1.99996886 DOcc + 58 = 1.99996877 DOcc + 59 = 1.99996852 DOcc + 60 = 1.99996606 DOcc + 61 = 1.99996538 DOcc + 62 = 1.99995823 DOcc + 63 = 1.99995812 DOcc + 64 = 1.99995797 DOcc + 65 = 1.99995773 DOcc + 66 = 1.99995180 DOcc + 67 = 1.99995169 DOcc + 68 = 1.99994977 DOcc + 69 = 1.99994969 DOcc + 70 = 1.99993347 DOcc + 71 = 1.99993252 DOcc + 72 = 1.99991953 DOcc + 73 = 1.99991926 DOcc + 74 = 1.99991864 DOcc + 75 = 1.99991818 DOcc + 76 = 1.99991712 DOcc + 77 = 1.99991654 DOcc + 78 = 1.99991499 DOcc + 79 = 1.99991114 DOcc + 80 = 1.99989744 DOcc + 81 = 1.99989736 DOcc + 82 = 1.99988562 DOcc + 83 = 1.99988094 DOcc + 84 = 1.99987964 DOcc + 85 = 1.99987754 DOcc + 86 = 1.99987366 DOcc + 87 = 1.99987056 DOcc + 88 = 1.99986805 DOcc + 89 = 1.99984167 DOcc + 90 = 1.99981680 DOcc + 91 = 1.99981659 DOcc + 92 = 1.99978492 DOcc + 93 = 1.99978434 DOcc + 94 = 1.99968524 DOcc + 95 = 1.99968508 DOcc + 96 = 1.99964807 DOcc + 97 = 1.99964803 DOcc + 98 = 1.99962864 DOcc + 99 = 1.99962644 DOcc + 100 = 1.99943855 DOcc + 101 = 1.99943816 DOcc + 102 = 1.99910661 DOcc + 103 = 1.99909456 DOcc + 104 = 1.99894634 DOcc + 105 = 1.99893566 DOcc + 106 = 1.99782479 DOcc + 107 = 1.99781008 DOcc + 108 = 1.99552199 DOcc + 109 = 1.99537297 DOcc + 110 = 1.99447050 DOcc + 111 = 1.99445635 DOcc + 112 = 1.99386765 DOcc + 113 = 1.99368909 DOcc + 114 = 1.98342687 Active + 115 = 1.98331788 Active + 116 = 1.98026160 Active + 117 = 1.97986518 Active + 118 = 1.66473634 Active + 119 = 1.65634281 Active + 120 = 0.35470546 Active + 121 = 0.32369139 Active + 122 = 0.01986849 Active + 123 = 0.01975496 Active + 124 = 0.01666494 Active + 125 = 0.01664332 Active + 126 = 0.00641013 Virt + 127 = 0.00629419 Virt + 128 = 0.00557441 Virt + 129 = 0.00547335 Virt + 130 = 0.00468513 Virt + 131 = 0.00450826 Virt + 132 = 0.00211205 Virt + 133 = 0.00209116 Virt + 134 = 0.00104439 Virt + 135 = 0.00104245 Virt + 136 = 0.00089314 Virt + 137 = 0.00088586 Virt + 138 = 0.00056017 Virt + 139 = 0.00055999 Virt + 140 = 0.00037590 Virt + 141 = 0.00037454 Virt + 142 = 0.00034987 Virt + 143 = 0.00034832 Virt + 144 = 0.00031170 Virt + 145 = 0.00031128 Virt + 146 = 0.00022051 Virt + 147 = 0.00021949 Virt + 148 = 0.00018317 Virt + 149 = 0.00018248 Virt + 150 = 0.00015586 Virt + 151 = 0.00013459 Virt + 152 = 0.00012927 Virt + 153 = 0.00012570 Virt + 154 = 0.00011833 Virt + 155 = 0.00011651 Virt + 156 = 0.00011371 Virt + 157 = 0.00011161 Virt + 158 = 0.00010618 Virt + 159 = 0.00010266 Virt + 160 = 0.00009801 Virt + 161 = 0.00009744 Virt + 162 = 0.00009319 Virt + 163 = 0.00008986 Virt + 164 = 0.00008156 Virt + 165 = 0.00008124 Virt + 166 = 0.00007267 Virt + 167 = 0.00007195 Virt + 168 = 0.00007041 Virt + 169 = 0.00007040 Virt + 170 = 0.00006854 Virt + 171 = 0.00006843 Virt + 172 = 0.00005151 Virt + 173 = 0.00005088 Virt + 174 = 0.00004779 Virt + 175 = 0.00004760 Virt + 176 = 0.00004397 Virt + 177 = 0.00004365 Virt + 178 = 0.00004250 Virt + 179 = 0.00004232 Virt + 180 = 0.00003907 Virt + 181 = 0.00003848 Virt + 182 = 0.00003745 Virt + 183 = 0.00003709 Virt + 184 = 0.00003254 Virt + 185 = 0.00003236 Virt + 186 = 0.00003048 Virt + 187 = 0.00003011 Virt + 188 = 0.00002994 Virt + 189 = 0.00002968 Virt + 190 = 0.00002867 Virt + 191 = 0.00002843 Virt + 192 = 0.00002314 Virt + 193 = 0.00002303 Virt + 194 = 0.00002236 Virt + 195 = 0.00002142 Virt + 196 = 0.00002003 Virt + 197 = 0.00001968 Virt + 198 = 0.00001963 Virt + 199 = 0.00001864 Virt + 200 = 0.00001840 Virt + 201 = 0.00001819 Virt + 202 = 0.00001805 Virt + 203 = 0.00001785 Virt + 204 = 0.00001760 Virt + 205 = 0.00001748 Virt + 206 = 0.00001647 Virt + 207 = 0.00001624 Virt + 208 = 0.00001568 Virt + 209 = 0.00001546 Virt + 210 = 0.00001500 Virt + 211 = 0.00001495 Virt + 212 = 0.00001422 Virt + 213 = 0.00001387 Virt + 214 = 0.00001328 Virt + 215 = 0.00001323 Virt + 216 = 0.00001270 Virt + 217 = 0.00001263 Virt + 218 = 0.00001234 Virt + 219 = 0.00001202 Virt + 220 = 0.00001086 Virt + 221 = 0.00001047 Virt + 222 = 0.00001028 Virt + 223 = 0.00000999 Virt + 224 = 0.00000955 Virt + 225 = 0.00000894 Virt + 226 = 0.00000817 Virt + 227 = 0.00000783 Virt + 228 = 0.00000781 Virt + 229 = 0.00000777 Virt + 230 = 0.00000708 Virt + 231 = 0.00000703 Virt + 232 = 0.00000647 Virt + 233 = 0.00000623 Virt + 234 = 0.00000568 Virt + 235 = 0.00000546 Virt + 236 = 0.00000518 Virt + 237 = 0.00000515 Virt + 238 = 0.00000505 Virt + 239 = 0.00000501 Virt + 240 = 0.00000485 Virt + 241 = 0.00000483 Virt + 242 = 0.00000394 Virt + 243 = 0.00000378 Virt + 244 = 0.00000352 Virt + 245 = 0.00000344 Virt + 246 = 0.00000306 Virt + 247 = 0.00000293 Virt + 248 = 0.00000280 Virt + 249 = 0.00000280 Virt + 250 = 0.00000277 Virt + 251 = 0.00000267 Virt + 252 = 0.00000265 Virt + 253 = 0.00000249 Virt + 254 = 0.00000240 Virt + 255 = 0.00000220 Virt + 256 = 0.00000209 Virt + 257 = 0.00000202 Virt + 258 = 0.00000199 Virt + 259 = 0.00000191 Virt + 260 = 0.00000182 Virt + 261 = 0.00000181 Virt + 262 = 0.00000173 Virt + 263 = 0.00000163 Virt + 264 = 0.00000138 Virt + 265 = 0.00000129 Virt + 266 = 0.00000121 Virt + 267 = 0.00000118 Virt + 268 = 0.00000112 Virt + 269 = 0.00000111 Virt + 270 = 0.00000110 Virt + 271 = 0.00000107 Virt + 272 = 0.00000100 Virt + 273 = 0.00000095 Virt + 274 = 0.00000078 Virt + 275 = 0.00000070 Virt + 276 = 0.00000069 Virt + 277 = 0.00000064 Virt + 278 = 0.00000063 Virt + 279 = 0.00000061 Virt + 280 = 0.00000060 Virt + 281 = 0.00000057 Virt + 282 = 0.00000054 Virt + 283 = 0.00000047 Virt + 284 = 0.00000044 Virt + 285 = 0.00000044 Virt + 286 = 0.00000041 Virt + 287 = 0.00000038 Virt + 288 = 0.00000034 Virt + 289 = 0.00000034 Virt + 290 = 0.00000033 Virt + 291 = 0.00000031 Virt + 292 = 0.00000030 Virt + 293 = 0.00000028 Virt + 294 = 0.00000027 Virt + 295 = 0.00000026 Virt + 296 = 0.00000024 Virt + 297 = 0.00000024 Virt + 298 = 0.00000022 Virt + 299 = 0.00000021 Virt + 300 = 0.00000020 Virt + 301 = 0.00000019 Virt + 302 = 0.00000018 Virt + 303 = 0.00000018 Virt + 304 = 0.00000017 Virt + 305 = 0.00000016 Virt + 306 = 0.00000016 Virt + 307 = 0.00000015 Virt + 308 = 0.00000014 Virt + 309 = 0.00000014 Virt + 310 = 0.00000013 Virt + 311 = 0.00000013 Virt + 312 = 0.00000012 Virt + 313 = 0.00000012 Virt + 314 = 0.00000012 Virt + 315 = 0.00000012 Virt + 316 = 0.00000011 Virt + 317 = 0.00000011 Virt + 318 = 0.00000010 Virt + 319 = 0.00000010 Virt + 320 = 0.00000010 Virt + 321 = 0.00000009 Virt + 322 = 0.00000009 Virt + 323 = 0.00000009 Virt + 324 = 0.00000007 Virt + 325 = 0.00000007 Virt + 326 = 0.00000007 Virt + 327 = 0.00000007 Virt + 328 = 0.00000006 Virt + 329 = 0.00000006 Virt + 330 = 0.00000006 Virt + 331 = 0.00000006 Virt + 332 = 0.00000005 Virt + 333 = 0.00000005 Virt + 334 = 0.00000005 Virt + 335 = 0.00000004 Virt + 336 = 0.00000004 Virt + 337 = 0.00000004 Virt + 338 = 0.00000004 Virt + 339 = 0.00000003 Virt + 340 = 0.00000003 Virt + 341 = 0.00000003 Virt + 342 = 0.00000002 Virt + 343 = 0.00000002 Virt + 344 = 0.00000002 Virt + 345 = 0.00000002 Virt + 346 = 0.00000002 Virt + 347 = 0.00000002 Virt + 348 = 0.00000001 Virt + 349 = 0.00000001 Virt + 350 = 0.00000001 Virt + 351 = 0.00000001 Virt + 352 = 0.00000001 Virt + 353 = 0.00000001 Virt + 354 = 0.00000001 Virt + 355 = 0.00000001 Virt + 356 = 0.00000001 Virt + 357 = 0.00000001 Virt + 358 = 0.00000001 Virt + 359 = 0.00000001 Virt + 360 = 0.00000001 Virt + 361 = 0.00000001 Virt + 362 = 0.00000001 Virt + 363 = 0.00000001 Virt + 364 = 0.00000001 Virt + 365 = 0.00000001 Virt + 366 = 0.00000000 Virt + 367 = 0.00000000 Virt + 368 = 0.00000000 Virt + 369 = 0.00000000 Virt + 370 = 0.00000000 Virt + 371 = 0.00000000 Virt + 372 = 0.00000000 Virt + 373 = 0.00000000 Virt + 374 = 0.00000000 Virt + 375 = 0.00000000 Virt + 376 = 0.00000000 Virt + 377 = 0.00000000 Virt + 378 = 0.00000000 Virt + 379 = 0.00000000 Virt + 380 = 0.00000000 Virt + 381 = 0.00000000 Virt + 382 = 0.00000000 Virt + 383 = 0.00000000 Virt + 384 = 0.00000000 Virt + 385 = 0.00000000 Virt + 386 = 0.00000000 Virt + 387 = 0.00000000 Virt + 388 = 0.00000000 Virt + 389 = 0.00000000 Virt + 390 = 0.00000000 Virt + 391 = 0.00000000 Virt + 392 = 0.00000000 Virt + 393 = 0.00000000 Virt + 394 = 0.00000000 Virt + 395 = 0.00000000 Virt + 396 = 0.00000000 Virt + 397 = 0.00000000 Virt + 398 = 0.00000000 Virt + 399 = 0.00000000 Virt + 400 = 0.00000000 Virt + 401 = 0.00000000 Virt + 402 = 0.00000000 Virt + 403 = 0.00000000 Virt + 404 = 0.00000000 Virt + 405 = 0.00000000 Virt + 406 = 0.00000000 Virt + 407 = 0.00000000 Virt + 408 = 0.00000000 Virt + 409 = 0.00000000 Virt + 410 = 0.00000000 Virt + 411 = 0.00000000 Virt + 412 = 0.00000000 Virt + 413 = 0.00000000 Virt + 414 = 0.00000000 Virt + 415 = 0.00000000 Virt + 416 = 0.00000000 Virt + 417 = 0.00000000 Virt + 418 = 0.00000000 Virt + 419 = 0.00000000 Virt + 420 = 0.00000000 Virt + 421 = 0.00000000 Virt + 422 = 0.00000000 Virt + 423 = 0.00000000 Virt + 424 = 0.00000000 Virt + 425 = 0.00000000 Virt + 426 = 0.00000000 Virt + 427 = 0.00000000 Virt + 428 = 0.00000000 Virt + 429 = 0.00000000 Virt + 430 = 0.00000000 Virt + 431 = 0.00000000 Virt + 432 = 0.00000000 Virt + 433 = 0.00000000 Virt + 434 = 0.00000000 Virt + 435 = 0.00000000 Virt + 436 = 0.00000000 Virt + 437 = 0.00000000 Virt + 438 = 0.00000000 Virt + 439 = 0.00000000 Virt + 440 = 0.00000000 Virt + 441 = 0.00000000 Virt + 442 = 0.00000000 Virt + 443 = 0.00000000 Virt + 444 = 0.00000000 Virt + 445 = 0.00000000 Virt + 446 = 0.00000000 Virt + 447 = 0.00000000 Virt + 448 = 0.00000000 Virt + 449 = 0.00000000 Virt + 450 = 0.00000000 Virt + 451 = 0.00000000 Virt + 452 = 0.00000000 Virt + 453 = 0.00000000 Virt + 454 = 0.00000000 Virt + 455 = 0.00000000 Virt + 456 = 0.00000000 Virt + 457 = 0.00000000 Virt + 458 = 0.00000000 Virt + 459 = 0.00000000 Virt + 460 = 0.00000000 Virt + 461 = 0.00000000 Virt + 462 = 0.00000000 Virt + 463 = 0.00000000 Virt + 464 = 0.00000000 Virt + 465 = 0.00000000 Virt + 466 = 0.00000000 Virt + 467 = 0.00000000 Virt + 468 = 0.00000000 Virt + 469 = 0.00000000 Virt + 470 = 0.00000000 Virt + 471 = 0.00000000 Virt + 472 = 0.00000000 Virt + 473 = 0.00000000 Virt + 474 = 0.00000000 Virt + 475 = 0.00000000 Virt + 476 = 0.00000000 Virt + 477 = 0.00000000 Virt + 478 = 0.00000000 Virt + 479 = 0.00000000 Virt + 480 = 0.00000000 Virt + 481 = 0.00000000 Virt + 482 = 0.00000000 Virt + 483 = 0.00000000 Virt + 484 = 0.00000000 Virt + 485 = 0.00000000 Virt + 486 = 0.00000000 Virt + 487 = 0.00000000 Virt + 488 = 0.00000000 Virt + 489 = 0.00000000 Virt + 490 = 0.00000000 Virt + 491 = 0.00000000 Virt + 492 = 0.00000000 Virt + 493 = 0.00000000 Virt + 494 = 0.00000000 Virt + 495 = 0.00000000 Virt + 496 = 0.00000000 Virt + 497 = 0.00000000 Virt + 498 = 0.00000000 Virt + 499 = 0.00000000 Virt + 500 = 0.00000000 Virt + 501 = 0.00000000 Virt + 502 = 0.00000000 Virt + 503 = 0.00000000 Virt + 504 = 0.00000000 Virt + 505 = 0.00000000 Virt + 506 = 0.00000000 Virt + 507 = 0.00000000 Virt + 508 = 0.00000000 Virt + 509 = 0.00000000 Virt + 510 = 0.00000000 Virt + 511 = 0.00000000 Virt + 512 = 0.00000000 Virt + 513 = 0.00000000 Virt + 514 = 0.00000000 Virt + 515 = 0.00000000 Virt + 516 = 0.00000000 Virt + 517 = 0.00000000 Virt + 518 = 0.00000000 Virt + 519 = 0.00000000 Virt + 520 = 0.00000000 Virt + 521 = 0.00000000 Virt + 522 = 0.00000000 Virt + 523 = 0.00000000 Virt + 524 = 0.00000000 Virt + 525 = 0.00000000 Virt + 526 = 0.00000000 Virt + 527 = 0.00000000 Virt + 528 = 0.00000000 Virt + 529 = 0.00000000 Virt + 530 = 0.00000000 Virt + 531 = 0.00000000 Virt + 532 = 0.00000000 Virt + 533 = 0.00000000 Virt + 534 = 0.00000000 Virt + 535 = 0.00000000 Virt + 536 = 0.00000000 Virt + 537 = 0.00000000 Virt + 538 = 0.00000000 Virt + 539 = 0.00000000 Virt + 540 = 0.00000000 Virt + 541 = 0.00000000 Virt + 542 = 0.00000000 Virt + 543 = 0.00000000 Virt + 544 = 0.00000000 Virt + 545 = 0.00000000 Virt + 546 = 0.00000000 Virt + 547 = 0.00000000 Virt + 548 = 0.00000000 Virt + 549 = 0.00000000 Virt + 550 = 0.00000000 Virt + 551 = 0.00000000 Virt + Number of active orbitals: 12 + Number of doc orbitals: 114 +(552, 114) +(552, 12) +Set conv_tol_grad to 0.000316228 +macro= 1 f(x)= 0.59801731478907 delta_f= 0.598017 |g|= 0.0322518 1 KF 4 Hx +macro= 2 f(x)= 1.0365671249008 delta_f= 0.43855 |g|= 0.261581 4 KF 22 Hx +macro= 3 f(x)= 2.3560840259996 delta_f= 1.31952 |g|= 0.53539 4 KF 21 Hx +macro= 4 f(x)= 3.1896743614071 delta_f= 0.83359 |g|= 0.43101 4 KF 21 Hx +macro= 5 f(x)= 3.2555163461128 delta_f= 0.065842 |g|= 0.262946 2 KF 3 Hx +macro= 6 f(x)= 3.2653802437187 delta_f= 0.0098639 |g|= 0.0951839 1 KF 3 Hx +macro= 7 f(x)= 3.2653815490682 delta_f= 1.30535e-06 |g|= 0.00104804 1 KF 3 Hx +macro= 8 f(x)= 3.2653815490682 delta_f= 3.10862e-15 |g|= 7.56935e-06 1 KF 1 Hx +macro X = 8 f(x)= 3.2653815490682 |g|= 7.56935e-06 16 intor 18 KF 78 Hx \ No newline at end of file diff --git a/examples/tetracene_dimer/nat_orb_active_space.py b/examples/tetracene_dimer/nat_orb_active_space.py new file mode 100644 index 0000000..1c2d080 --- /dev/null +++ b/examples/tetracene_dimer/nat_orb_active_space.py @@ -0,0 +1,192 @@ +from functools import reduce +import numpy as np +import scipy + +import pyscf +from pyscf import fci, tools +from pyscf import gto, scf, ao2mo, lo, tdscf, cc +from orbitalpartitioning import * + +def get_natural_orbital_active_space(rdm, S, thresh=.01): + + + Ssqrt = scipy.linalg.sqrtm((S+S.T)/2.0) + Sinvsqrt = scipy.linalg.inv(Ssqrt) + + print(" Number of electrons found %12.8f" %np.trace(S@rdm)) + + Dtot = Ssqrt.T @ rdm @ Ssqrt + #Dtot = Ssqrt.T @ ( da + db) @ Ssqrt + D_evals, D_evecs = np.linalg.eigh((Dtot+Dtot.T)/2.0) + + sorted_list = np.argsort(D_evals)[::-1] + D_evals = D_evals[sorted_list] + D_evecs = D_evecs[:,sorted_list] + + act_list = [] + doc_list = [] + + + for idx,n in enumerate(D_evals): + print(" %4i = %12.8f" %(idx,n),end="") + if n < 2.0 - thresh: + if n > thresh: + act_list.append(idx) + print(" Active") + else: + print(" Virt") + else: + doc_list.append(idx) + print(" DOcc") + + print(" Number of active orbitals: ", len(act_list)) + print(" Number of doc orbitals: ", len(doc_list)) + + D_evecs = Sinvsqrt @ D_evecs + Cdoc = D_evecs[:, doc_list] + Cact = D_evecs[:, act_list] + return Cdoc, Cact + + + + + +mol = gto.Mole() +mol.atom = ''' +C 3.86480 -1.02720 1.27180 +C 3.60170 -0.34020 0.07040 +C 2.93000 -1.06360 2.30730 +C 4.54410 -0.28950 -0.97720 +C 2.32170 0.33090 -0.09430 +C 3.20030 -1.73210 3.54060 +C 1.65210 -0.39240 2.14450 +C 4.27330 0.38740 -2.16700 +C 2.05960 1.01870 -1.29550 +C 1.37970 0.28170 0.95340 +C 2.27260 -1.73270 4.55160 +C 0.71620 -0.41900 3.22490 +C 2.99590 1.05910 -2.32920 +C 5.20900 0.41800 -3.24760 +C 1.01870 -1.06880 4.39470 +C 2.72630 1.73410 -3.55840 +C 4.90670 1.07300 -4.41500 +C 3.65410 1.73950 -4.56900 +C -3.86650 1.01870 -1.29550 +C -2.93020 1.05910 -2.32920 +C -3.60440 0.33090 -0.09430 +C -3.19980 1.73410 -3.55840 +C -1.65280 0.38740 -2.16700 +C -2.32430 -0.34020 0.07040 +C -4.54630 0.28170 0.95340 +C -2.27200 1.73950 -4.56900 +C -0.71710 0.41800 -3.24760 +C -1.38200 -0.28950 -0.97720 +C -2.06130 -1.02720 1.27180 +C -4.27400 -0.39240 2.14450 +C -1.01930 1.07300 -4.41500 +C -2.99610 -1.06360 2.30730 +C -5.20980 -0.41900 3.22490 +C -2.72580 -1.73210 3.54060 +C -4.90730 -1.06880 4.39470 +C -3.65350 -1.73270 4.55160 +H 4.82300 -1.53290 1.39770 +H 5.49910 -0.80290 -0.85660 +H 4.15900 -2.23700 3.66390 +H 1.10180 1.52560 -1.42170 +H 0.42460 0.79440 0.83100 +H 2.50000 -2.24040 5.48840 +H -0.23700 0.09640 3.10140 +H 6.16210 -0.09790 -3.12730 +H 0.29870 -1.07700 5.21470 +H 1.76850 2.24120 -3.67870 +H 5.62580 1.08320 -5.23570 +H 3.42730 2.25190 -5.50340 +H -4.82430 1.52560 -1.42170 +H -4.15760 2.24120 -3.67870 +H -5.50150 0.79440 0.83100 +H -2.49880 2.25190 -5.50340 +H 0.23610 -0.09790 -3.12730 +H -0.42700 -0.80290 -0.85660 +H -1.10300 -1.53290 1.39770 +H -0.30030 1.08320 -5.23570 +H -6.16310 0.09640 3.10140 +H -1.76710 -2.23700 3.66390 +H -5.62740 -1.07700 5.21470 +H -3.42610 -2.24040 5.48840 +''' + +np.save("xyz.npy", mol.atom) + +mol.basis = '6-31g*' +mol.spin = 0 +mol.build() + +mf = scf.RHF(mol).density_fit() +mf.verbose = 4 +mf.get_init_guess(mol, key='minao') +mf.conv_tol = 1e-9 + +# load precomputed data +C = np.load("./rhf_mo_coeffs18.npy") +avg_rdm1 = np.load("./cis_sa_density_mat18.npy") + +S = mf.get_ovlp() + +print(avg_rdm1.shape) +print(S.shape) + +print(" Number of electrons found %12.8f" %np.trace(S@avg_rdm1)) + +Cdoc, Cact = get_natural_orbital_active_space(avg_rdm1, S, thresh=.008) +print(Cdoc.shape) +print(Cact.shape) + +# localize +Cact = pyscf.lo.PM(mol).kernel(Cact, verbose=4); +pyscf.tools.molden.from_mo(mol, "Cact.molden", Cact) + +# Pseudo canonicalize fragments +frag1 = [0,3,5,7,8,11] +frag2 = [1,2,4,6,9,10] +Cfrags = [] +Cfrags.append(Cact[:,frag1]) +Cfrags.append(Cact[:,frag2]) + + +# Pseudo canonicalize fragments +F = np.load("./fock_mat18.npy") +Cact = canonicalize(Cfrags, F) +np.save("Cfrags", Cact) + +Cact = np.hstack(Cact) +pyscf.tools.molden.from_mo(mol, "Cact12_pseudo_can.molden", Cact) +# First get the density from the doubly occupied orbitals +# to include in our effective 1 body operator +d1_embed = 2 * Cdoc @ Cdoc.T + +h0 = pyscf.gto.mole.energy_nuc(mol) +h = pyscf.scf.hf.get_hcore(mol) +j, k = pyscf.scf.hf.get_jk(mol, d1_embed, hermi=1) + +h0 += np.trace(d1_embed @ ( h + .5*j - .25*k)) + +# Rotate 1electron terms to active space +h = Cact.T @ h @ Cact +j = Cact.T @ j @ Cact; +k = Cact.T @ k @ Cact; + +h1 = h + j - .5*k; + +# form 2e integrals in active space +nact = h.shape[0] +h2 = pyscf.ao2mo.kernel(mol, Cact, aosym="s4", compact=False) +h2.shape = (nact, nact, nact, nact) + +np.save("integrals_h0_12", h0) +np.save("integrals_h1_12", h1) +np.save("integrals_h2_12", h2) +np.save("mo_coeffs_act_12", Cact) +np.save("mo_coeffs_doc_12", Cdoc) + + + diff --git a/examples/tetracene_dimer/orbitalpartitioning.py b/examples/tetracene_dimer/orbitalpartitioning.py new file mode 100644 index 0000000..d72351c --- /dev/null +++ b/examples/tetracene_dimer/orbitalpartitioning.py @@ -0,0 +1,364 @@ +import numpy as np +import scipy +import warnings + +def spade_partitioning(Cocc, Pv, S): + pass + + +def dmet_clustering(Cocc, Cvir, frags, S): + full = [] + [full.extend(i) for i in frags] + nmo = Cocc.shape[1] + Cvir.shape[1] + + init_fspace = [] # Number of electrons in each cluster + clusters = [] + + Cenv_occ, Cact_occ, Cact_vir, Cenv_vir = dmet_active_space(Cocc, Cvir, full, S) + + nfrag = len(frags) + print(" Partition %4i orbitals into a total of %4i fragments" %(nmo, nfrag)) + + Clist = [] + + orb_idx = 0 + for f in frags: + _, fo, fv, _ = dmet_active_space(Cocc, Cvir, f, S) + Clist.append(np.hstack((fo,fv))) + + ndocc_f = fo.shape[1] + init_fspace.append((ndocc_f, ndocc_f)) + + nmo_f = fo.shape[1] + fv.shape[1] + clusters.append(list(range(orb_idx, orb_idx+nmo_f))) + orb_idx += nmo_f + + + Clist = sym_ortho(Clist, S) + + out = [Cenv_occ] + [out.append(i) for i in Clist] + out.append(Cenv_vir) + + print(" init_fspace = ", init_fspace) + print(" clusters = ", clusters) + + + return out, init_fspace, clusters + +def dmet_active_space(Cocc_in, Cvir_in, frag, S): + + X = scipy.linalg.sqrtm(S) + Xinv = np.linalg.inv(X) + + Cocc = X@Cocc_in + Cvir = X@Cvir_in + + nbas = S.shape[0] + assert(Cocc.shape[0] == nbas) + assert(Cvir.shape[0] == nbas) + nmo = Cocc.shape[1] + Cvir.shape[1] + + nfrag = len(frag) + + print(" Create DMET active space by projecting %4i MOs onto %4i fragment orbitals" %(nmo, nfrag)) + # Occupieds + _,s,V = np.linalg.svd(Cocc[frag,:], full_matrices=True) + nocc = 0 + for si in s: + if si > 1e-6: + nocc += 1 + else: + warnings.warn("Small singular values") + + Cact_occ = Cocc @ V[0:nocc,:].T + Cenv_occ = Cocc @ V[nocc:,:].T + + # Virtuals + _,s,V = np.linalg.svd(Cvir[frag,:], full_matrices=True) + nocc = 0 + for si in s: + if si > 1e-6: + nocc += 1 + else: + warnings.warn("Small singular values") + Cact_vir = Cvir @ V[0:nocc,:].T + Cenv_vir = Cvir @ V[nocc:,:].T + + # Un-Orthogonalize + Cact_occ = Xinv@Cact_occ + Cact_vir = Xinv@Cact_vir + Cenv_occ = Xinv@Cenv_occ + Cenv_vir = Xinv@Cenv_vir + + + + # print(Cenv.shape, Cact_occ.shape, Cact_vir.shape) + + print(" Dmet active space has the following dimensions:") + print(" Environment (occupied) : %5i" % Cenv_occ.shape[1]) + print(" Active (occupied) : %5i" % Cact_occ.shape[1]) + print(" Active (virtual) : %5i" % Cact_vir.shape[1]) + print(" Environment (virtual) : %5i" % Cenv_vir.shape[1]) + return Cenv_occ, Cact_occ, Cact_vir, Cenv_vir + + +def canonicalize(orbital_blocks, F): + """ + Given an AO Fock matrix, rotate each orbital block in `orbital_blocks` to diagonalize F + """ + out = [] + for obi, ob in enumerate(orbital_blocks): + fi = ob.T @ F @ ob + fi = .5 * ( fi + fi.T ) + e, U = np.linalg.eigh(fi) + perm = e.argsort() + e = e[perm] + U = U[:,perm] + out.append(ob @ U) + return out + +def extract_frontier_orbitals(orbital_blocks, F, dims): + """ + Given an AO Fock matrix, split each orbital block into 3 spaces, NDocc, NAct, Nvirt + + `dims` = [(NDocc, NAct, Nvirt), (NDocc, NAct, Nvirt), ... ] + `F`: the fock matrix + """ + NAOs = F.shape[0] + tmp = canonicalize(orbital_blocks, F) + env_blocks = [] + act_blocks = [] + vir_blocks = [] + for obi, ob in enumerate(tmp): + assert(ob.shape[0] == NAOs) + env_blocks.append(np.zeros((NAOs, 0))) + act_blocks.append(np.zeros((NAOs, 0))) + vir_blocks.append(np.zeros((NAOs, 0))) + for obi, ob in enumerate(tmp): + assert(np.sum(dims[obi]) == ob.shape[1]) + env_blocks[obi] = ob[:,0:dims[obi][0]] + act_blocks[obi] = ob[:,dims[obi][0]:dims[obi][0]+dims[obi][1]] + vir_blocks[obi] = ob[:,dims[obi][0]+dims[obi][1]:dims[obi][0]+dims[obi][1]+dims[obi][2]] + + return env_blocks, act_blocks, vir_blocks + +""" +The `svd_subspace_partitioning` function appears to perform Singular Value Decomposition (SVD) based + subspace partitioning of orbitals. Here is a breakdown of the logic behind the function: + +1. **Input Parameters:** + - `orbitals_blocks`: List of orbital blocks, where each block is a matrix of orbitals. Common scenarios include `[Occ, Virt]` or `[Occ, Sing, Virt]`. + - `Pv`: Projector matrix of shape `[AO, frag]` representing the orbitals to be partitioned. + - `S`: Overlap matrix of the basis set. + +2. **Initialization:** + - `nfrag`: Number of fragments (fragments correspond to the columns of `Pv`). + - `nbas`: Number of basis functions. + - `nmo`: Total number of molecular orbitals, initialized to 0. + - Loop over `orbitals_blocks` to check if the shape of each block matches the number of basis functions and update `nmo`. + +3. **Transformation Matrix X:** + - Calculate the square root of the overlap matrix `S` using `scipy.linalg.sqrtm` and store it in `X`. + +4. **Partitioning the Orbitals:** + - Construct the projector `P` by applying transformations to `Pv`. + - Use SVD on the transformed orbitals (`X @ P @ S @ ob`), where `ob` is an orbital block. + - Store the singular values, left singular vectors, and right singular vectors in `s`, `Clist`, and `Vob` respectively. + +5. **Sorting Singular Values:** + - Sort the singular values in descending order along with their corresponding indices. + - Update the singular values `s` and the corresponding spaces. + +6. **Constructing Total Singular Vectors Matrix:** + - Concatenate the matrices in `Clist` horizontally to form the total singular vectors matrix `Ctot`. + +7. **Printing Information:** + - Display information about the singular values, their corresponding indices, and the orbital blocks they belong to. + +8. **Active Space Dimensions:** + - Display the dimensions of the active space for each orbital block. + +9. **Return:** + - Return lists `Cf` and `Ce` representing the fragment and environment orbitals respectively. + +The function aims to partition the orbitals into fragments and environment based on their overlap with the projector `Pv` using SVD. The logic involves manipulatin +g the orbital matrices and singular value decomposition to identify the active space for each orbital block. The active space is divided into fragment and environment orbitals, +and the dimensions of the active space are printed for each orbital block. + +""" +def svd_subspace_partitioning(orbitals_blocks, Pv, S): + """ + Find orbitals that most strongly overlap with the projector, P, by doing rotations within each orbital block. + [C1, C2, C3] -> [(C1f, C2f, C3f), (C1e, C2e, C3e)] + where C1f (C2f) and C1e (C2e) are the fragment orbitals in block 1 (2) and remainder orbitals in block 1 (2). + + Common scenarios would be + `orbital_blocks` = [Occ, Virt] + or + `orbital_blocks` = [Occ, Sing, Virt] + + P[AO, frag] + O[AO, occupied] + U[AO, virtual] + """ + + nfrag = Pv.shape[1] + nbas = S.shape[0] + assert(Pv.shape[0] == nbas) + nmo = 0 + for i in orbitals_blocks: + assert(i.shape[0] == nbas) + nmo += i.shape[1] + + + X = scipy.linalg.sqrtm(S) + + print(" Partition %4i orbitals into a total of %4i orbitals" %(nmo, Pv.shape[1])) + P = Pv @ np.linalg.inv(Pv.T @ S @ Pv) @ Pv.T + + + s = [] + Clist = [] + spaces = [] + Cf = [] + Ce = [] + for obi, ob in enumerate(orbitals_blocks): + _,sob,Vob = np.linalg.svd(X @ P @ S @ ob, full_matrices=True) + s.extend(sob) + Clist.append(ob @ Vob.T) + spaces.extend([obi for i in range(ob.shape[1])]) + Cf.append(np.zeros((nbas, 0))) + Ce.append(np.zeros((nbas, 0))) + + spaces = np.array(spaces) + s = np.array(s) + + # Sort all the singular values + perm = np.argsort(s)[::-1] + s = s[perm] + spaces = spaces[perm] + + Ctot = np.hstack(Clist) + Ctot = Ctot[:,perm] + + print(" %16s %12s %-12s" %("Index", "Sing. Val.", "Space")) + for i in range(nfrag): + print(" %16i %12.8f %12s*" %(i, s[i], spaces[i])) + block = spaces[i] + Cf[block] = np.hstack((Cf[block], Ctot[:,i:i+1])) + + for i in range(nfrag, nmo): + if s[i] > 1e-6: + print(" %16i %12.8f %12s" %(i, s[i], spaces[i])) + block = spaces[i] + Ce[block] = np.hstack((Ce[block], Ctot[:,i:i+1])) + + print(" SVD active space has the following dimensions:") + print(" %14s %14s %14s" %("Orbital Block", "Environment", "Active")) + for obi,ob in enumerate(orbitals_blocks): + print(" %14i %14i %14i" %(obi, Ce[obi].shape[1], Cf[obi].shape[1])) + assert(abs(np.linalg.det(ob.T @ S @ ob)) > 1e-12) + + return Cf, Ce + +def svd_subspace_partitioning_orth(orbitals_blocks, frag, S): + """ + Find orbitals that most strongly overlap with the atomic orbitals listed in `frag` by doing rotations within each orbital block. + [C1, C2, C3] -> [(C1f, C2f, C3f), (C1e, C2e, C3e)] + where C1f (C2f) and C1e (C2e) are the fragment orbitals in block 1 (2) and remainder orbitals in block 1 (2). + + Common scenarios would be + `orbital_blocks` = [Occ, Virt] + or + `orbital_blocks` = [Occ, Sing, Virt] + + frag[ao1, ao2, ...] + O[AO, occupied] + U[AO, virtual] + + frag listed here are assumed to be orthogonalized AOs + """ + + print(" In svd_subspace_partitioning_orth") + nbas = S.shape[0] + assert(len(orbitals_blocks)) > 0 + + I = np.eye(nbas) + + # Define projectors + X = scipy.linalg.sqrtm(S) + Xinv = np.linalg.inv(X) + + # acts, envs = svd_subspace_partitioning([X@o for o in orbitals_blocks], X[:,frag], I) + # return [Xinv@o for o in acts], [Xinv@o for o in envs] + + return svd_subspace_partitioning(orbitals_blocks, X[:,frag], S) + + + +def svd_subspace_partitioning_nonorth(orbitals_blocks, frag, S): + """ + Find orbitals that most strongly overlap with the atomic orbitals listed in `frags` by doing rotations within each orbital block. + [C1, C2, C3] -> [(C1f, C2f, C3f), (C1e, C2e, C3e)] + where C1f (C2f) and C1e (C2e) are the fragment orbitals in block 1 (2) and remainder orbitals in block 1 (2). + + Common scenarios would be + `orbital_blocks` = [Occ, Virt] + or + `orbital_blocks` = [Occ, Sing, Virt] + + frags[ao1, ao2, ...] + O[AO, occupied] + U[AO, virtual] + + NOTE: frags listed here are assumed to be the non-orthogonal AOs + """ + + + print(" In svd_subspace_partitioning_nonorth") + nbas = S.shape[0] + assert(len(orbitals_blocks)) > 0 + + I = np.eye(nbas) + + return svd_subspace_partitioning(orbitals_blocks, I[:,frag], S) + + + + + +def sym_ortho(frags, S, thresh=1e-8): + """ + Orthogonalize list of MO coefficients. + + `frags` is a list of mo-coeff matrices, e.g., [C[ao,mo], C[ao, mo], ...] + """ + Nbas = S.shape[1] + + inds = [] + Cnonorth = np.hstack(frags) + shift = 0 + for f in frags: + inds.append(list(range(shift, shift+f.shape[1]))) + shift += f.shape[1] + + + Smo = Cnonorth.T @ S @ Cnonorth + X = np.linalg.inv(scipy.linalg.sqrtm(Smo)) + # print(Cnonorth.shape, X.shape) + Corth = Cnonorth @ X + + frags2 = [] + for f in inds: + frags2.append(Corth[:,f]) + return frags2 + + + + + + +if __name__ == "__main__": + # Do something if this file is invoked on its own + pass \ No newline at end of file diff --git a/examples/tetracene_dimer/overlap_mat18.npy b/examples/tetracene_dimer/overlap_mat18.npy new file mode 100644 index 0000000..527b35a Binary files /dev/null and b/examples/tetracene_dimer/overlap_mat18.npy differ diff --git a/examples/tetracene_dimer/rhf.out b/examples/tetracene_dimer/rhf.out new file mode 100644 index 0000000..0cf4ec5 --- /dev/null +++ b/examples/tetracene_dimer/rhf.out @@ -0,0 +1,136 @@ + + +******** ******** +method = RHF +initial guess = minao +damping factor = 0 +level_shift factor = 0 +DIIS = +diis_start_cycle = 1 +diis_space = 8 +SCF conv_tol = 1e-09 +SCF conv_tol_grad = None +SCF max_cycles = 200 +direct_scf = True +direct_scf_tol = 1e-13 +chkfile to save SCF result = /var/folders/td/qpnnxwv93pq0t7bbdkh5rvzr0000gn/T/tmppc_y98iq +max_memory 4000 MB (current use 0 MB) +Set gradient conv threshold to 3.16228e-05 +init E= -1389.45366124002 + HOMO = -0.145262677431497 LUMO = -0.0550854674010574 +cycle= 1 E= -1376.85730939042 delta_E= 12.6 |g|= 0.981 |ddm|= 8.13 + HOMO = -0.245350690798504 LUMO = 0.00693712142183936 +cycle= 2 E= -1377.22377335779 delta_E= -0.366 |g|= 0.27 |ddm|= 1.91 + HOMO = -0.216201672559677 LUMO = 0.0427600742629329 +cycle= 3 E= -1377.25155592446 delta_E= -0.0278 |g|= 0.101 |ddm|= 0.762 + HOMO = -0.227463487653541 LUMO = 0.0332813202414119 +cycle= 4 E= -1377.25476963288 delta_E= -0.00321 |g|= 0.0216 |ddm|= 0.146 + HOMO = -0.228714881929556 LUMO = 0.0332889695400231 +cycle= 5 E= -1377.25505925905 delta_E= -0.00029 |g|= 0.00943 |ddm|= 0.0424 + HOMO = -0.228952391709473 LUMO = 0.0339833467228525 +cycle= 6 E= -1377.25514950645 delta_E= -9.02e-05 |g|= 0.0047 |ddm|= 0.0208 + HOMO = -0.229006025759246 LUMO = 0.0346670761776774 +cycle= 7 E= -1377.25518253398 delta_E= -3.3e-05 |g|= 0.00169 |ddm|= 0.0167 + HOMO = -0.229305162774948 LUMO = 0.0346476908878027 +cycle= 8 E= -1377.25518599635 delta_E= -3.46e-06 |g|= 0.000577 |ddm|= 0.00574 + HOMO = -0.229335719242601 LUMO = 0.0346671326546259 +cycle= 9 E= -1377.255186242 delta_E= -2.46e-07 |g|= 0.00028 |ddm|= 0.00147 + HOMO = -0.229342656950483 LUMO = 0.0346656249725508 +cycle= 10 E= -1377.25518628924 delta_E= -4.72e-08 |g|= 0.000126 |ddm|= 0.000503 + HOMO = -0.229346530544654 LUMO = 0.0346637887381009 +cycle= 11 E= -1377.25518631965 delta_E= -3.04e-08 |g|= 9.26e-05 |ddm|= 0.000472 + HOMO = -0.229350687866647 LUMO = 0.0346593544752005 +cycle= 12 E= -1377.2551863322 delta_E= -1.25e-08 |g|= 4.57e-05 |ddm|= 0.000345 + HOMO = -0.229350502219827 LUMO = 0.0346585086348484 +cycle= 13 E= -1377.25518633694 delta_E= -4.74e-09 |g|= 2.23e-05 |ddm|= 0.000251 + HOMO = -0.229349969305211 LUMO = 0.0346587420505109 +cycle= 14 E= -1377.25518633774 delta_E= -8.04e-10 |g|= 5.43e-06 |ddm|= 0.000133 + HOMO = -0.229350403154358 LUMO = 0.0346582406477969 +Extra cycle E= -1377.25518633778 delta_E= -3.18e-11 |g|= 3.26e-06 |ddm|= 1.15e-05 +converged SCF energy = -1377.25518633778 + + +******** for ******** +nstates = 2 singlet +wfnsym = None +conv_tol = 1e-09 +eigh lindep = 1e-12 +eigh level_shift = 0 +eigh max_space = 50 +eigh max_cycle = 100 +chkfile = /var/folders/td/qpnnxwv93pq0t7bbdkh5rvzr0000gn/T/tmppc_y98iq +max_memory 4000 MB (current use 0 MB) + + +Excited State energies (eV) +[3.32193121 3.44113945] + +** Singlet excitation energies and oscillator strengths ** +Excited State 1: 3.32193 eV 373.23 nm f=0.4874 + 117 -> 124 0.11243 + 118 -> 123 -0.11271 + 119 -> 122 0.45379 + 120 -> 121 0.49259 +Excited State 2: 3.44114 eV 360.30 nm f=0.0000 + 117 -> 123 -0.12731 + 118 -> 124 0.12660 + 119 -> 121 0.48217 + 120 -> 122 0.45912 + +** Transition electric dipole moments (AU) ** +state X Y Z Dip. S. Osc. + 1 -2.2312 0.9962 -0.1351 5.9891 0.4874 + 2 -0.0013 0.0005 0.0006 0.0000 0.0000 + +** Transition velocity dipole moments (imaginary part, AU) ** +state X Y Z Dip. S. Osc. + 1 0.0954 -0.1102 0.0245 0.0218 0.1193 + 2 0.0000 0.0000 0.0001 0.0000 0.0000 + +** Transition magnetic dipole moments (imaginary part, AU) ** +state X Y Z + 1 0.0002 0.0003 -0.0034 + 2 -0.0210 -0.2197 0.4448 + + +******** for ******** +nstates = 2 triplet +wfnsym = None +conv_tol = 1e-09 +eigh lindep = 1e-12 +eigh level_shift = 0 +eigh max_space = 50 +eigh max_cycle = 100 +chkfile = /var/folders/td/qpnnxwv93pq0t7bbdkh5rvzr0000gn/T/tmppc_y98iq +max_memory 4000 MB (current use 0 MB) + + +Excited State energies (eV) +[1.13553472 1.1364029 ] + +** Triplet excitation energies and oscillator strengths ** +Excited State 1: 1.13553 eV 1091.86 nm f=0.4452 + 115 -> 126 0.13617 + 116 -> 125 0.13718 + 119 -> 122 0.43609 + 120 -> 121 0.45041 +Excited State 2: 1.13640 eV 1091.02 nm f=0.0007 + 115 -> 125 -0.13542 + 116 -> 126 -0.13739 + 119 -> 121 0.44698 + 120 -> 122 0.43960 + +** Transition electric dipole moments (AU) ** +state X Y Z Dip. S. Osc. + 1 -3.5276 1.8366 -0.4324 16.0038 0.4452 + 2 -0.1367 0.0709 -0.0164 0.0240 0.0007 + +** Transition velocity dipole moments (imaginary part, AU) ** +state X Y Z Dip. S. Osc. + 1 -0.4287 0.2269 -0.0524 0.2380 3.8016 + 2 -0.0166 0.0088 -0.0020 0.0004 0.0057 + +** Transition magnetic dipole moments (imaginary part, AU) ** +state X Y Z + 1 -0.0005 0.0106 0.0452 + 2 -0.0008 -0.2921 -1.2642 diff --git a/examples/tetracene_dimer/rhf.py b/examples/tetracene_dimer/rhf.py new file mode 100644 index 0000000..88af21f --- /dev/null +++ b/examples/tetracene_dimer/rhf.py @@ -0,0 +1,149 @@ +from functools import reduce +import numpy as np +import scipy + +import pyscf +from pyscf import fci +from pyscf import gto, scf, ao2mo, lo, tdscf, cc + + +def tda_denisty_matrix(td, state_id): + ''' + Taking the TDA amplitudes as the CIS coefficients, calculate the density + matrix (in AO basis) of the excited states + ''' + cis_t1 = td.xy[state_id][0] + dm_oo =-np.einsum('ia,ka->ik', cis_t1.conj(), cis_t1) + dm_vv = np.einsum('ia,ic->ac', cis_t1, cis_t1.conj()) + + # The ground state density matrix in mo_basis + mf = td._scf + dm = np.diag(mf.mo_occ) + + # Add CIS contribution + nocc = cis_t1.shape[0] + # Note that dm_oo and dm_vv correspond to spin-up contribution. "*2" to + # include the spin-down contribution + dm[:nocc,:nocc] += dm_oo * 2 + dm[nocc:,nocc:] += dm_vv * 2 + + # Transform density matrix to AO basis + mo = mf.mo_coeff + dm = np.einsum('pi,ij,qj->pq', mo, dm, mo.conj()) + return dm + +mol = gto.Mole() +mol.atom = ''' +C 3.86480 -1.02720 1.27180 +C 3.60170 -0.34020 0.07040 +C 2.93000 -1.06360 2.30730 +C 4.54410 -0.28950 -0.97720 +C 2.32170 0.33090 -0.09430 +C 3.20030 -1.73210 3.54060 +C 1.65210 -0.39240 2.14450 +C 4.27330 0.38740 -2.16700 +C 2.05960 1.01870 -1.29550 +C 1.37970 0.28170 0.95340 +C 2.27260 -1.73270 4.55160 +C 0.71620 -0.41900 3.22490 +C 2.99590 1.05910 -2.32920 +C 5.20900 0.41800 -3.24760 +C 1.01870 -1.06880 4.39470 +C 2.72630 1.73410 -3.55840 +C 4.90670 1.07300 -4.41500 +C 3.65410 1.73950 -4.56900 +C -3.86650 1.01870 -1.29550 +C -2.93020 1.05910 -2.32920 +C -3.60440 0.33090 -0.09430 +C -3.19980 1.73410 -3.55840 +C -1.65280 0.38740 -2.16700 +C -2.32430 -0.34020 0.07040 +C -4.54630 0.28170 0.95340 +C -2.27200 1.73950 -4.56900 +C -0.71710 0.41800 -3.24760 +C -1.38200 -0.28950 -0.97720 +C -2.06130 -1.02720 1.27180 +C -4.27400 -0.39240 2.14450 +C -1.01930 1.07300 -4.41500 +C -2.99610 -1.06360 2.30730 +C -5.20980 -0.41900 3.22490 +C -2.72580 -1.73210 3.54060 +C -4.90730 -1.06880 4.39470 +C -3.65350 -1.73270 4.55160 +H 4.82300 -1.53290 1.39770 +H 5.49910 -0.80290 -0.85660 +H 4.15900 -2.23700 3.66390 +H 1.10180 1.52560 -1.42170 +H 0.42460 0.79440 0.83100 +H 2.50000 -2.24040 5.48840 +H -0.23700 0.09640 3.10140 +H 6.16210 -0.09790 -3.12730 +H 0.29870 -1.07700 5.21470 +H 1.76850 2.24120 -3.67870 +H 5.62580 1.08320 -5.23570 +H 3.42730 2.25190 -5.50340 +H -4.82430 1.52560 -1.42170 +H -4.15760 2.24120 -3.67870 +H -5.50150 0.79440 0.83100 +H -2.49880 2.25190 -5.50340 +H 0.23610 -0.09790 -3.12730 +H -0.42700 -0.80290 -0.85660 +H -1.10300 -1.53290 1.39770 +H -0.30030 1.08320 -5.23570 +H -6.16310 0.09640 3.10140 +H -1.76710 -2.23700 3.66390 +H -5.62740 -1.07700 5.21470 +H -3.42610 -2.24040 5.48840 +''' + + +mol.basis = '6-31g*' +mol.spin = 0 +mol.build() + +#mf = scf.ROHF(mol).x2c() +mf = scf.RHF(mol) +mf.verbose = 4 +mf.get_init_guess(mol, key='minao') +mf.conv_tol = 1e-9 +#mf.level_shift = .1 +#mf.diis_start_cycle = 4 +#mf.diis_space = 10 +mf.run(max_cycle=200) + + +n_triplets = 2 +n_singlets = 2 + +avg_rdm1 = mf.make_rdm1() + + +# compute singlets +mytd = tdscf.TDA(mf) +mytd.singlet = True +mytd = mytd.run(nstates=n_singlets) +mytd.analyze() +for i in range(mytd.nroots): + avg_rdm1 += tda_denisty_matrix(mytd, i) + +# compute triplets +mytd = tdscf.TDA(mf) +mytd.singlet = False +mytd = mytd.run(nstates=n_triplets) +mytd.analyze() +for i in range(mytd.nroots): + avg_rdm1 += tda_denisty_matrix(mytd, i) + +# normalize +avg_rdm1 = avg_rdm1 / (n_singlets + n_triplets + 1) + + +S = mf.get_ovlp() +F = mf.get_fock() +np.save("fock_mat18", F) +np.save("overlap_mat18", S) +np.save("density_mat18", mf.make_rdm1()) +np.save("rhf_mo_coeffs18", mf.mo_coeff) +np.save("cis_sa_density_mat18", avg_rdm1) + + diff --git a/examples/tetracene_dimer/rhf_mo_coeffs18.npy b/examples/tetracene_dimer/rhf_mo_coeffs18.npy new file mode 100644 index 0000000..da0fc48 Binary files /dev/null and b/examples/tetracene_dimer/rhf_mo_coeffs18.npy differ diff --git a/examples/tetracene_dimer/spt.jl b/examples/tetracene_dimer/spt.jl new file mode 100644 index 0000000..7e5f7ba --- /dev/null +++ b/examples/tetracene_dimer/spt.jl @@ -0,0 +1,71 @@ +using QCBase +using Printf +using FermiCG +using NPZ +using InCoreIntegrals +using RDM +using JLD2 + +@load "data_cmf_TD_12.jld2" +M = 100 +#@load "cmf_op_TD_with_ops.jld2" +display(clusters) +display(init_fspace) +ref_fspace = FockConfig(init_fspace) +ecore = ints.h0 + +cluster_bases = FermiCG.compute_cluster_eigenbasis_spin(ints, clusters, d1, [5,5], ref_fspace, max_roots=M, verbose=1); + +clustered_ham = FermiCG.extract_ClusteredTerms(ints, clusters) +cluster_ops = FermiCG.compute_cluster_ops(cluster_bases, ints); + +FermiCG.add_cmf_operators!(cluster_ops, cluster_bases, ints, d1.a, d1.b); +@save "cmf_op_TD_with_ops.jld2" clusters init_fspace ints cluster_bases cluster_ops clustered_ham +nroots = 8 +ci_vector = BSTstate(clusters,FermiCG.FockConfig(init_fspace), cluster_bases, R=nroots); +#ci_vector = FermiCG.TPSCIstate(clusters, FermiCG.FockConfig(init_fspace), R=nroots); +#ci_vector = FermiCG.add_spin_focksectors(ci_vector) + +# Add the lowest energy single exciton and biexciton to basis +#ci_vector=FermiCG.bst_single_excitonic_basis(FermiCG.FockConfig(init_fspace),ci_vector,R=nroots) +#ci_vector=FermiCG.bst_biexcitonic_basis(FermiCG.FockConfig(init_fspace),ci_vector,R=nroots) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((1:1,1:1))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((2:2,1:1))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((1:1,2:2))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((1:1,3:3))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((3:3,1:1))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +ci_vector[FermiCG.FockConfig(init_fspace)][FermiCG.TuckerConfig((2:2,2:2))] = + FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) + + +# Spin-flip states +fspace_0 = FermiCG.FockConfig(init_fspace) + +## ba +tmp_fspace = FermiCG.replace(fspace_0, (1,2), ([6,4],[4,6])) +FermiCG.add_fockconfig!(ci_vector, tmp_fspace) +ci_vector[tmp_fspace][FermiCG.TuckerConfig((1:1,1:1))]=FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) + +## ab +tmp_fspace = FermiCG.replace(fspace_0, (1,2), ([4,6],[6,4])) +FermiCG.add_fockconfig!(ci_vector, tmp_fspace) +ci_vector[tmp_fspace][FermiCG.TuckerConfig((1:1,1:1))]=FermiCG.Tucker(tuple([zeros(Float64, 1, 1) for _ in 1:nroots]...)) +FermiCG.eye!(ci_vector) +display(ci_vector) + + +e_ci, v = FermiCG.ci_solve(ci_vector, cluster_ops, clustered_ham); +@save "data_ci.jld2" v e_ci + + +σ = FermiCG.build_compressed_1st_order_state(v, cluster_ops, clustered_ham, thresh=1e-8) +σ = FermiCG.compress(σ, thresh=1e-2) +FermiCG.nonorth_add!(v, σ) + +e_ci, v = FermiCG.ci_solve(σ, cluster_ops, clustered_ham); + diff --git a/examples/tetracene_dimer/xyz.npy b/examples/tetracene_dimer/xyz.npy new file mode 100644 index 0000000..67fe780 Binary files /dev/null and b/examples/tetracene_dimer/xyz.npy differ diff --git a/src/FermiCG.jl b/src/FermiCG.jl index 7f3046c..b6623dc 100644 --- a/src/FermiCG.jl +++ b/src/FermiCG.jl @@ -106,4 +106,5 @@ export add_subspace! export add_fockconfig! export expand_each_fock_space! export block_sparse_tucker +export correlation_functions end diff --git a/src/tpsci_helpers.jl b/src/tpsci_helpers.jl index 7f61df0..ccd1570 100644 --- a/src/tpsci_helpers.jl +++ b/src/tpsci_helpers.jl @@ -1,7 +1,7 @@ """ - function single_excitonic_basis(clusters, fspace::FockConfig{N}; R=1, Nk=2, T=Float64) where {N} +function single_excitonic_basis(clusters, fspace::FockConfig{N}; R=1, Nk=2, T=Float64) where {N} """ function single_excitonic_basis(clusters, fspace::FockConfig{N}; R=1, Nk=2, T=Float64) where {N} ci_vector = TPSCIstate(clusters, fspace, R=R) @@ -18,3 +18,563 @@ function single_excitonic_basis(clusters, fspace::FockConfig{N}; R=1, Nk=2, T=Fl end + +""" + correlation_functions(v::TPSCIstate{T,N,R}) where {T,N,R} + +Compute , >, , and > +""" +function correlation_functions(v::TPSCIstate{T,N,R}) where {T,N,R} + + n1 = [zeros(N) for i in 1:R] + n2 = [zeros(N,N) for i in 1:R] + sz1 = [zeros(N) for i in 1:R] + sz2 = [zeros(N,N) for i in 1:R] + + for root in 1:R + for (fock,configs) in v.data + prob = 0 + for (config, coeff) in configs + prob += coeff[root]*coeff[root] + end + + for ci in v.clusters + n1[root][ci.idx] += prob * (fock[ci.idx][1] + fock[ci.idx][2]) + sz1[root][ci.idx] += prob * (fock[ci.idx][1] - fock[ci.idx][2]) / 2 + for cj in v.clusters + ci.idx <= cj.idx || continue + n2[root][ci.idx, cj.idx] += prob * (fock[ci.idx][1] + fock[ci.idx][2]) * (fock[cj.idx][1] + fock[cj.idx][2]) + sz2[root][ci.idx, cj.idx] += prob * (fock[ci.idx][1] - fock[ci.idx][2]) * (fock[cj.idx][1] - fock[cj.idx][2]) / 4 + n2[root][cj.idx, ci.idx] = n2[root][ci.idx, cj.idx] + sz2[root][cj.idx, ci.idx] = sz2[root][ci.idx, cj.idx] + end + end + end + end + + for r in 1:R + n2[r] = n2[r] - n1[r]*n1[r]' + sz2[r] = sz2[r] - sz1[r]*sz1[r]' + end + + return n1, n2, sz1, sz2 +end + + + + + + +""" + correlation_functions(v::TPSCIstate{T,N,R}, refspace::TPSCIstate{T,N,R}; verbose=1) where {T,N,R} + +Compute 1st and 2nd cumulants for finding excitations outside of a reference space of local cluster states present in `refspace` + +Returns: + out["Q0"][order][root]: Dictionary for all results + +order is either 1 or 2 +""" +function correlation_functions(v::TPSCIstate{T,N,R}, refspace::TPSCIstate{T,N,R2}; verbose=1) where {T,N,R,R2} + + N_1, N_2, Sz_1, Sz_2 = correlation_functions(v) + + cf = Dict{String,Tuple{Vector{Vector{T}}, Vector{Matrix{T}}}}() + + c1 = [zeros(N) for i in 1:R] + c2 = [zeros(N, N) for i in 1:R] + + p_space = [Dict{Tuple{Int,Int,Int}, Bool}() for i in 1:N] + + for (fock, configs) in refspace.data + for (config, _) in configs + for ci in 1:N + p_space[ci][(fock[ci][1], fock[ci][2], config[ci])] = true + end + end + end + + display(p_space) + for root in 1:R + for (fock, configs) in v.data + for (config, coeff) in configs + for ci in 1:N + presenti = false + if haskey(p_space[ci], (fock[ci][1], fock[ci][2], config[ci])) + presenti = true + end + if presenti == false + c1[root][ci] += coeff[root] * coeff[root] + end + + + + for cj in 1:N + presentj = false + if haskey(p_space[cj], (fock[cj][1], fock[cj][2], config[cj])) + presentj = true + end + + if (presenti == false) && (presentj == false) + c2[root][ci, cj] += coeff[root] * coeff[root] + end + end + end + end + end + end + + for r in 1:R + c2[r] = c2[r] - c1[r] * c1[r]' + end + + cf["Q"] = (c1, c2) + cf["N"] = (N_1, N_2) + cf["Sz"] = (Sz_1, Sz_2) + + if verbose > 0 + @printf(" * κ1(Q) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Q"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ1(N) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["N"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ1(Sz) *************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Sz"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ2(N) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(NI, NJ)") + for j in 1:N + [@printf(" %12.8f", cf["N"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(NI, NJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["N"][2][r][i,j]/sqrt(cf["N"][2][r][i,i]*cf["N"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + + @printf(" * κ2(Sz) *************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(SzI, SzJ)") + for j in 1:N + [@printf(" %12.8f", cf["Sz"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(SzI, SzJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["Sz"][2][r][i,j]/sqrt(cf["Sz"][2][r][i,i]*cf["Sz"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + + @printf(" * κ2(Q) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(QI, QJ)") + for j in 1:N + [@printf(" %12.8f", cf["Q"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(QI, QJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["Q"][2][r][i,j]/sqrt(cf["Q"][2][r][i,i]*cf["Q"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + end + return cf +end + +function correlation_functions_old(v::TPSCIstate{T,N,R}, refspace::TPSCIstate{T,N,R2}; verbose=1) where {T,N,R,R2} + + N_1, N_2, Sz_1, Sz_2 = correlation_functions(v) + + cf = Dict{String,Tuple{Vector{Vector{T}}, Vector{Matrix{T}}}}() + + c1 = [zeros(N) for i in 1:R] + c2 = [zeros(N, N) for i in 1:R] + + p_space = [Dict{Tuple{Int,Int}, Dict{Int,Int}}() for i in 1:N] + + for (fock, configs) in refspace.data + for (config, _) in configs + for ci in 1:N + if haskey(p_space[ci], fock[ci]) == false + p_space[ci][fock[ci]] = Dict{Int,Int}() + end + p_space[ci][fock[ci]][config[ci]] = 1 + end + end + end + + for root in 1:R + for (fock, configs) in v.data + for (config, coeff) in configs + for ci in 1:N + presenti = false + for (focki, configsi) in refspace.data + for (configi, coeffi) in configsi + if (focki[ci] == fock[ci]) && (config[ci] == configi[ci]) + presenti = true + end + end + end + if presenti == false + c1[root][ci] += coeff[root] * coeff[root] + end + + + + for cj in 1:N + ci <= cj || continue + presentj = false + for (fockj, configsj) in refspace.data + for (configj, coeffj) in configsj + if (fockj[cj] == fock[cj]) && (config[cj] == configj[cj]) + presentj = true + break + end + end + end + + if (presenti == false) && (presentj == false) + c2[root][ci, cj] += coeff[root] * coeff[root] + c2[root][cj, ci] += coeff[root] * coeff[root] + end + end + end + end + end + end + + for r in 1:R + c2[r] = c2[r] - c1[r] * c1[r]' + end + + cf["Q"] = (c1, c2) + cf["N"] = (N_1, N_2) + cf["Sz"] = (Sz_1, Sz_2) + + if verbose > 0 + @printf(" * κ1(Q) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Q"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ1(N) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["N"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ1(Sz) *************************\n") + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Sz"][1][r][i]) for i in 1:N] + println() + end + + + @printf(" * κ2(N) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(NI, NJ)") + for j in 1:N + [@printf(" %12.8f", cf["N"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(NI, NJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["N"][2][r][i,j]/sqrt(cf["N"][2][r][i,i]*cf["N"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + + @printf(" * κ2(Sz) *************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(SzI, SzJ)") + for j in 1:N + [@printf(" %12.8f", cf["Sz"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(SzI, SzJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["Sz"][2][r][i,j]/sqrt(cf["Sz"][2][r][i,i]*cf["Sz"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + + @printf(" * κ2(Q) **************************\n") + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(QI, QJ)") + for j in 1:N + [@printf(" %12.8f", cf["Q"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + # for r in 1:R + # @printf(" Root = %2i: %7s:\n", r, "Corr(QI, QJ)") + # for j in 1:N + # [@printf(" %12.8f", cf["Q"][2][r][i,j]/sqrt(cf["Q"][2][r][i,i]*cf["Q"][2][r][j,j])) for i in 1:N] + # println() + # end + # println() + # end + + end + return cf +end + + + + +""" + correlation_functions(v::TPSCIstate{T,N,R}, cluster_ops; verbose=1) where {T,N,R} + +Compute cumulants for S2, H, and Hcmf +""" +function correlation_functions(v::TPSCIstate{T,N,R}, cluster_ops::Vector{ClusterOps{T}}; verbose=1) where {T,N,R} + + N_1, N_2, Sz_1, Sz_2 = correlation_functions(v) + + cf = Dict{String,Tuple{Vector{Vector{T}}, Vector{Matrix{T}}}}() + N_1 = N_1 + N_2 = N_2 + Sz_1 = Sz_1 + Sz_2 = Sz_2 + + H_1 = [zeros(T,N) for i in 1:R] + H_2 = [zeros(T,N,N) for i in 1:R] + + Hcmf_1 = [zeros(T,N) for i in 1:R] + Hcmf_2 = [zeros(T,N,N) for i in 1:R] + + S2_1 = [zeros(T,N) for i in 1:R] + S2_2 = [zeros(T,N,N) for i in 1:R] + + for (fock,configs) in v.data + prob = 0 + fock_trans = fock - fock + for (config, coeff) in configs + + for ci in v.clusters + + I = config[ci.idx] + Hi = cluster_ops[ci.idx]["H"][fock[ci.idx], fock[ci.idx]][I,I] + Hcmfi = cluster_ops[ci.idx]["Hcmf"][fock[ci.idx], fock[ci.idx]][I,I] + S2i = cluster_ops[ci.idx]["S2"][fock[ci.idx], fock[ci.idx]][I,I] + for r in 1:R + prob = coeff[r]*coeff[r] + + H_1[r][ci.idx] += prob * Hi + Hcmf_1[r][ci.idx] += prob * Hcmfi + S2_1[r][ci.idx] += prob * S2i + end + for cj in v.clusters + ci.idx <= cj.idx || continue + + J = config[cj.idx] + Hj = cluster_ops[cj.idx]["H"][fock[cj.idx], fock[cj.idx]][J,J] + Hcmfj = cluster_ops[cj.idx]["Hcmf"][fock[cj.idx], fock[cj.idx]][J,J] + S2j = cluster_ops[cj.idx]["S2"][fock[cj.idx], fock[cj.idx]][J,J] + + for r in 1:R + prob = coeff[r]*coeff[r] + + H_2[r][ci.idx, cj.idx] += prob * Hi * Hj + Hcmf_2[r][ci.idx, cj.idx] += prob * Hcmfi * Hcmfj + S2_2[r][ci.idx, cj.idx] += prob * S2i * S2j + + H_2[r][cj.idx, ci.idx] = H_2[r][ci.idx, cj.idx] + Hcmf_2[r][cj.idx, ci.idx] = Hcmf_2[r][ci.idx, cj.idx] + S2_2[r][cj.idx, ci.idx] = S2_2[r][ci.idx, cj.idx] + end + end + end + end + end + + for r in 1:R + H_2[r] = H_2[r] - H_1[r]*H_1[r]' + Hcmf_2[r] = Hcmf_2[r] - Hcmf_1[r]*Hcmf_1[r]' + S2_2[r] = S2_2[r] - S2_1[r]*S2_1[r]' + end + cf["N"] = (N_1, N_2) + cf["Sz"] = (Sz_1, Sz_2) + cf["H"] = (H_1, H_2) + cf["Hcmf"] = (Hcmf_1, Hcmf_2) + cf["S2"] = (S2_1, S2_2) + + if verbose > 0 + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["N"][1][r][i]) for i in 1:N] + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Sz"][1][r][i]) for i in 1:N] + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["S2"][1][r][i]) for i in 1:N] + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["H"][1][r][i]) for i in 1:N] + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s = ", r, "") + [@printf(" %12.8f", cf["Hcmf"][1][r][i]) for i in 1:N] + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(Ni,Nj)") + for j in 1:N + [@printf(" %12.8f", cf["N"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(Szi,Szj)") + for j in 1:N + [@printf(" %12.8f", cf["Sz"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(S2i,S2j)") + for j in 1:N + [@printf(" %12.8f", cf["S2"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + + + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(Hi,Hj)") + for j in 1:N + [@printf(" %12.8f", cf["H"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + + + for r in 1:R + @printf(" Root = %2i: %7s:\n", r, "Cov(Hcmf_i,Hcmf_j)") + for j in 1:N + [@printf(" %12.8f", cf["Hcmf"][2][r][i,j]) for i in 1:N] + println() + end + println() + end + + + end + return cf +end + + + +""" + full_dim(clusters::Vector{MOCluster}, bases::Vector{ClusterBasis{A,T}}, na, nb) where {A,T} + +Compute the total dimension of the Hilbert space defined by our current basis. +- `basis::Vector{ClusterBasis}` +- `na`: Number of alpha electrons total +- `nb`: Number of alpha electrons total +""" +function full_dim(clusters::Vector{MOCluster}, bases::Vector{ClusterBasis{A,T}}, na, nb) where {A,T} + # {{{ + println("\n Expand to full space") + ns = [] + + dim_tot = 0 + for c in clusters + nsi = [] + for (fspace,basis) in bases[c.idx] + push!(nsi,fspace) + end + push!(ns,nsi) + end + for newfock in Iterators.product(ns...) + nacurr = 0 + nbcurr = 0 + for c in newfock + nacurr += c[1] + nbcurr += c[2] + end + if (nacurr == na) && (nbcurr == nb) + config = FockConfig(collect(newfock)) + dim_tot += dim(config, clusters) + #add_fockconfig!(s,config) + end + end + #expand_each_fock_space!(bases) + + return dim_tot +end +# }}} + + diff --git a/src/tucker_form_sigma_block_expand.jl b/src/tucker_form_sigma_block_expand.jl index e7e3b55..d40e126 100644 --- a/src/tucker_form_sigma_block_expand.jl +++ b/src/tucker_form_sigma_block_expand.jl @@ -61,8 +61,8 @@ function form_sigma_block_expand(term::ClusteredTerm1B, # this means that all the overlaps and the operator is a scalar bra_cores[r] .= coeffs_ket.core[r] .* s else - bra_cores[r] .= @ncon(tensors, indices) - bra_cores[r] .= bra_cores[r] .* s + bra_cores[r] .= @ncon(tensors, indices) .* s + #bra_cores[r] .= bra_cores[r] .* s end deleteat!(tensors,length(tensors)) end diff --git a/src/tucker_outer.jl b/src/tucker_outer.jl index 5230ed7..78ee87a 100644 --- a/src/tucker_outer.jl +++ b/src/tucker_outer.jl @@ -887,7 +887,7 @@ function _build_compressed_1st_order_state_job(fock_σ, jobs, σ::BSTstate{T,N,R fock_ψ, tconfig_ψ, tuck_ψ, max_number=max_number, prescreen=thresh) - + length(tuck_σ) > 0 || continue # norm(tuck_σ) > thresh || continue diff --git a/src/type_FockConfig.jl b/src/type_FockConfig.jl index 86db23a..b2d7bf4 100644 --- a/src/type_FockConfig.jl +++ b/src/type_FockConfig.jl @@ -30,7 +30,7 @@ Return total dimension of space indexed by `fc` on `no` orbitals function dim(fc::FockConfig, clusters) dim = 1 for ci in clusters - dim *= binomial(length(ci), fc[ci.idx][1]) * binomial(length(ci), fc[ci.idx][2]) + dim *= binomial(BigInt(length(ci)), fc[ci.idx][1]) * binomial(BigInt(length(ci)), fc[ci.idx][2]) end return dim end diff --git a/src/type_TPSCIstate.jl b/src/type_TPSCIstate.jl index 6c2316c..b76b342 100644 --- a/src/type_TPSCIstate.jl +++ b/src/type_TPSCIstate.jl @@ -295,15 +295,25 @@ function print_configs(s::TPSCIstate; thresh=1e-3, root=1) for (fock,configs) in s.data length(s.clusters) == length(fock) || throw(Exception) length(s.data[fock]) > 0 || continue + print_fock = false + for (config, value) in s.data[fock] + if value[root]*value[root] > thresh + print_fock = true + break + end + end + print_fock || continue + @printf(" Dim %4i fock_space: ",length(s.data[fock])) [@printf(" %-2i(%i:%i) ",fii,fi[1],fi[2]) for (fii,fi) in enumerate(fock)] println() for (config, value) in s.data[fock] - @printf(" %5i",idx) + value[root]*value[root] > thresh || continue + #@printf(" %5i",idx) for c in config @printf("%3i",c) end - @printf(":%12.8f\n",value[1]) + @printf(":%12.8f\n",value[root]) idx += 1 end end @@ -687,21 +697,25 @@ function ct_analysis(s::TPSCIstate; ne_cluster=10, thresh=1e-5, nroots=1) ct = 0 for (fock,configs) in s.data prob = 0 + is_ct = false + for cluster in 1:length(s.clusters) if sum(fock[cluster]) != ne_cluster - prob = 0 - for (config, coeff) in configs - prob += coeff[root]*coeff[root] - end - if prob > thresh - @printf(" %-20.5f%-20i", prob,length(s.data[fock])) - for sector in fock - @printf("(%2i,%-2i)", sector[1],sector[2]) - end - println() + is_ct = true + end + end + + if is_ct + for (config, coeff) in configs + prob += coeff[root]*coeff[root] + end + if prob > thresh + @printf(" %-20.5f%-20i", prob,length(s.data[fock])) + for sector in fock + @printf("(%2i,%-2i)", sector[1],sector[2]) end + println() end - break end ct += prob end @@ -732,14 +746,18 @@ function ct_table(s::TPSCIstate; ne_cluster=10, nroots=1) ct = 0 for (fock,configs) in s.data prob = 0 + is_ct = false + for cluster in 1:length(s.clusters) if sum(fock[cluster]) != ne_cluster - prob = 0 - for (config, coeff) in configs - prob += coeff[root]*coeff[root] - end + is_ct = true + end + end + if is_ct + prob = 0 + for (config, coeff) in configs + prob += coeff[root]*coeff[root] end - break end ct += prob end @@ -752,7 +770,6 @@ end - nroots(v::TPSCIstate{T,N,R}) where {T,N,R} = R type(v::TPSCIstate{T,N,R}) where {T,N,R} = T nclusters(v::TPSCIstate{T,N,R}) where {T,N,R} = N