Discarded 8 diffused primitive functions, 0 contracted functions #INFO: **** input file is /projects/wagner/wawheel2/he_finite_size/bug_example/hfvmc_bug.py **** #!/usr/bin/env python import numpy as np from pyscf.pbc import gto, scf import pyqmc from pyqmc.slaterpbc import PySCFSlaterPBC, get_supercell #from pyqmc.mc import vmc from pyqmc.dasktools import distvmc as vmc from dask.distributed import Client, LocalCluster def run_scf(nk): cell = gto.Cell() cell.atom = """ He 0.000000000000 0.000000000000 0.000000000000 """ cell.basis = "bfd-vdz" cell.ecp = "bfd" cell.exp_to_discard = 1.0 cell.a = """ 5.61, 0.00, 0.00 0.00, 5.61, 0.00 0.00, 0.00, 5.61""" cell.unit = "B" cell.verbose = 7 cell.build() kpts = cell.make_kpts([nk, nk, nk]) kmf = scf.KRHF(cell, exxdiv="ewald").density_fit() kmf.kpts = kpts ehf = kmf.kernel() return cell, kmf if __name__=="__main__": nk = 2 cell, kmf = run_scf(nk) S = np.eye(3) * nk supercell = get_supercell(cell, S) wf = PySCFSlaterPBC(supercell, kmf) enacc = pyqmc.EnergyAccumulator(supercell) ncore = 20 nconfig = ncore * 300 #nconfig = 2000 configs = pyqmc.initial_guess(supercell, nconfig, r=6.0) cluster = LocalCluster(n_workers=ncore, threads_per_worker=1) client = Client(cluster) # Run VMC hdf_file = "bfd_hfvmc_nk{0}.hdf".format(nk) df, configs = vmc( wf, configs, nsteps=4000, accumulators={"energy": enacc}, hdf_file=hdf_file, verbose=True, client=client, nsteps_per=50, ) #INFO: ******************** input file end ******************** System: uname_result(system='Linux', node='golub004', release='3.10.0-957.21.3.el7.x86_64', version='#1 SMP Tue Jun 18 16:35:19 UTC 2019', machine='x86_64', processor='x86_64') Threads 1 Python 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0] numpy 1.15.4 scipy 1.3.0 Date: Tue Feb 11 12:59:38 2020 PySCF version 1.6.3 PySCF path /projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/pyscf [CONFIG] DEBUG = False [CONFIG] MAX_MEMORY = 4000 [CONFIG] TMPDIR = /tmp [CONFIG] UNIT = angstrom [CONFIG] VERBOSE = 3 [CONFIG] conf_file = None [INPUT] verbose = 7 [INPUT] max_memory = 4000 [INPUT] num. atoms = 1 [INPUT] num. electrons = 2 [INPUT] charge = 0 [INPUT] spin (= nelec alpha-beta = 2S) = 0 [INPUT] symmetry False subgroup None [INPUT] Mole.unit = B [INPUT] 1 He 0.000000000000 0.000000000000 0.000000000000 AA 0.000000000000 0.000000000000 0.000000000000 Bohr [INPUT] ---------------- BASIS SET ---------------- [INPUT] l, kappa, [nprim/nctr], expnt, c_1 c_2 ... [INPUT] He [INPUT] 0 0 [5 /1 ] 26.895662 0.002531 12.951926 0.038892 6.237154 0.064912 3.003576 0.12545 1.446408 0.215025 [INPUT] 1 0 [5 /1 ] 16.73718 0.098422 9.063386 -0.224631 4.907934 0.46971 2.657706 -0.879477 1.43918 1.54585 Ewald components = 3.52824610955262e-242, -9.46183687521147, 8.45032262227713 nuclear repulsion = -1.01151425293433 number of shells = 2 number of NR pGTOs = 20 number of NR cGTOs = 4 basis = bfd-vdz ecp = bfd bas 0, expnt(s) = [26.895662 12.951926 6.237154 3.003576 1.446408] bas 1, expnt(s) = [16.73718 9.063386 4.907934 2.657706 1.43918 ] CPU time: 1.33 lattice vectors a1 [5.610000000, 0.000000000, 0.000000000] a2 [0.000000000, 5.610000000, 0.000000000] a3 [0.000000000, 0.000000000, 5.610000000] dimension = 3 low_dim_ft_type = None Cell volume = 176.558 exp_to_discard = 1.0 rcut = 6.362699415542759 (nimgs = [2 2 2]) lattice sum = 81 cells precision = 1e-08 pseudo = None ke_cutoff = 1031.9012683055153 = [82 82 82] mesh (551368 PWs) ew_eta = 4.19177 ew_cut = 1.6998687872300327 (nimgs = [1 1 1]) ******** ******** method = KRHF-KSCF-RHF-SCF-RHF initial guess = minao damping factor = 0 level shift factor = 0 DIIS = DIIS start cycle = 1 DIIS space = 8 SCF tol = 1e-07 SCF gradient tol = None max. SCF cycles = 50 direct_scf = False chkfile to save SCF result = /tmp/tmpimsjqjuq max_memory 4000 MB (current use 112 MB) ******** PBC SCF flags ******** N kpts = 8 kpts = [[0. 0. 0. ] [0. 0. 0.55999869] [0. 0.55999869 0. ] [0. 0.55999869 0.55999869] [0.55999869 0. 0. ] [0.55999869 0. 0.55999869] [0.55999869 0.55999869 0. ] [0.55999869 0.55999869 0.55999869]] Exchange divergence treatment (exxdiv) = ewald Monkhorst pack size [2 2 2] ew_eta 2.1486892381881426 ew_cut 3.2214756178970303 Ewald components = 2.3977487592528e-255, -1.21250896277658, 1.08606968115978 madelung (= occupied orbital energy shift) = 0.25287856323360147 Total energy shift due to Ewald probe charge = -1/2 * Nelec*madelung = -0.252878563234 DF object = Set gradient conv threshold to 0.000316228 Big error detected in the electron number of initial guess density matrix (Ne/cell = 1.45665)! This can cause huge error in Fock matrix and lead to instability in SCF for low-dimensional systems. DM is normalized wrt the number of electrons 2.0 CPU time for vnuc pass1: analytic int 0.03 sec, wall time 0.03 sec CPU time for contracting Vnuc [0:12167] 0.52 sec, wall time 0.52 sec CPU time for contracting Vnuc 0.52 sec, wall time 0.52 sec nao 4 -> nao 4 cond(S) = [1.00000003 1.00000006 1.00000006 1.00000006 1.00000006 1.00000006 1.00000006 1.00000003] ******** ******** mesh = [23 23 23] (12167 PWs) auxbasis = None eta = 1.3093123169315382 exp_to_discard = 1.0 _cderi_to_save = /tmp/tmpqh6yazyq len(kpts) = 8 kpts = [[0. 0. 0. ] [0. 0. 0.55999869] [0. 0.55999869 0. ] [0. 0.55999869 0.55999869] [0.55999869 0. 0. ] [0.55999869 0. 0.55999869] [0.55999869 0.55999869 0. ] [0.55999869 0.55999869 0.55999869]] Even tempered Gaussians are generated as DF auxbasis for He ETB auxbasis for He [[0, [46.285056, 1]], [0, [23.142528, 1]], [0, [11.571264, 1]], [0, [5.785632, 1]], [0, [2.892816, 1]], [1, [23.08463157931354, 1]], [1, [11.54231578965677, 1]], [1, [5.771157894828385, 1]], [1, [2.8855789474141926, 1]], [2, [23.02688, 1]], [2, [11.51344, 1]], [2, [5.75672, 1]], [2, [2.87836, 1]]] num shells = 13, num cGTOs = 37 Drop 0 primitive fitting functions make aux basis, num shells = 13, num cGTOs = 37 auxcell.rcut 5.172993541720129 make compensating basis, num shells = 3, num cGTOs = 9 chgcell.rcut 7.140211701147448 CPU time for 3c2e 0.27 sec, wall time 0.27 sec Num uniq kpts 14 uniq_kpts [[ 0. 0. 0. ] [ 0. 0. -0.55999869] [ 0. -0.55999869 0. ] [ 0. -0.55999869 0.55999869] [ 0. -0.55999869 -0.55999869] [-0.55999869 0. 0. ] [-0.55999869 0. 0.55999869] [-0.55999869 0.55999869 0. ] [-0.55999869 0.55999869 0.55999869] [-0.55999869 0. -0.55999869] [-0.55999869 0.55999869 -0.55999869] [-0.55999869 -0.55999869 0. ] [-0.55999869 -0.55999869 0.55999869] [-0.55999869 -0.55999869 -0.55999869]] max_memory 3868.731392 (MB) blocksize 2628214 Cholesky decomposition for j2c at kpt 0 Symmetry pattern (k - [0. 0. 0.])*a= 2n pi make_kpt for uniq_kptji_ids [0] kpt = [0. 0. 0.] adapted_ji_idx = [ 0 2 5 9 14 20 27 35] memory = 131.796992 int3c2e [1/1], AO [0:2], ncol = 10 Symmetry pattern (k + [0. 0. 0.])*a= 2n pi make_kpt for [0] Cholesky decomposition for j2c at kpt 1 Symmetry pattern (k - [ 0. 0. -0.55999869])*a= 2n pi make_kpt for uniq_kptji_ids [1] kpt = [ 0. 0. -0.55999869] adapted_ji_idx = [ 1 8 19 34] memory = 156.971008 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [ 0. 0. -0.55999869])*a= 2n pi make_kpt for [1] Cholesky decomposition for j2c at kpt 2 Symmetry pattern (k - [ 0. -0.55999869 0. ])*a= 2n pi make_kpt for uniq_kptji_ids [2] kpt = [ 0. -0.55999869 0. ] adapted_ji_idx = [ 3 7 25 33] memory = 159.371264 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [ 0. -0.55999869 0. ])*a= 2n pi make_kpt for [2] Cholesky decomposition for j2c at kpt 3 Symmetry pattern (k - [ 0. -0.55999869 0.55999869])*a= 2n pi make_kpt for uniq_kptji_ids [3 4] kpt = [ 0. -0.55999869 0.55999869] adapted_ji_idx = [ 4 26] memory = 157.315072 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [ 0. -0.55999869 -0.55999869] adapted_ji_idx = [ 6 32] memory = 157.323264 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [ 0. -0.55999869 0.55999869])*a= 2n pi make_kpt for [3 4] Cholesky decomposition for j2c at kpt 5 Symmetry pattern (k - [-0.55999869 0. 0. ])*a= 2n pi make_kpt for uniq_kptji_ids [5] kpt = [-0.55999869 0. 0. ] adapted_ji_idx = [10 16 23 31] memory = 157.323264 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [-0.55999869 0. 0. ])*a= 2n pi make_kpt for [5] Cholesky decomposition for j2c at kpt 6 Symmetry pattern (k - [-0.55999869 0. 0.55999869])*a= 2n pi make_kpt for uniq_kptji_ids [6 9] kpt = [-0.55999869 0. 0.55999869] adapted_ji_idx = [11 24] memory = 157.323264 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [-0.55999869 0. -0.55999869] adapted_ji_idx = [15 30] memory = 157.323264 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [-0.55999869 0. 0.55999869])*a= 2n pi make_kpt for [6 9] Cholesky decomposition for j2c at kpt 7 Symmetry pattern (k - [-0.55999869 0.55999869 0. ])*a= 2n pi make_kpt for uniq_kptji_ids [ 7 11] kpt = [-0.55999869 0.55999869 0. ] adapted_ji_idx = [12 18] memory = 157.323264 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [-0.55999869 -0.55999869 0. ] adapted_ji_idx = [21 29] memory = 157.32736 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [-0.55999869 0.55999869 0. ])*a= 2n pi make_kpt for [ 7 11] Cholesky decomposition for j2c at kpt 8 Symmetry pattern (k - [-0.55999869 0.55999869 0.55999869])*a= 2n pi make_kpt for uniq_kptji_ids [ 8 10 12 13] kpt = [-0.55999869 0.55999869 0.55999869] adapted_ji_idx = [13] memory = 157.32736 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [-0.55999869 0.55999869 -0.55999869] adapted_ji_idx = [17] memory = 161.603584 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [-0.55999869 -0.55999869 0.55999869] adapted_ji_idx = [22] memory = 161.603584 int3c2e [1/1], AO [0:2], ncol = 16 kpt = [-0.55999869 -0.55999869 -0.55999869] adapted_ji_idx = [28] memory = 161.619968 int3c2e [1/1], AO [0:2], ncol = 16 Symmetry pattern (k + [-0.55999869 0.55999869 0.55999869])*a= 2n pi make_kpt for [ 8 10 12 13] CPU time for j3c 27.63 sec, wall time 27.64 sec CPU time for Init get_k_kpts 27.63 sec, wall time 27.64 sec CPU time for get_k_kpts: make_kpt ki>=kj (0,*) 0.00 sec, wall time 0.02 sec CPU time for get_k_kpts: make_kpt ki>=kj (1,*) 0.00 sec, wall time 0.00 sec CPU time for get_k_kpts: make_kpt ki>=kj (2,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (3,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (4,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (5,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (6,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (7,*) 0.02 sec, wall time 0.02 sec Monkhorst pack size [2 2 2] ew_eta 2.1486892381881426 ew_cut 3.2214756178970303 Ewald components = 2.3977487592528e-255, -1.21250896277658, 1.08606968115978 CPU time for get_j pass 1 0.01 sec, wall time 0.01 sec CPU time for get_j pass 2 0.01 sec, wall time 0.01 sec CPU time for vj and vk 27.79 sec, wall time 27.81 sec E1 = (-1.1251213648610787-1.927838434321902e-41j) E_coul = (0.8545145350900218-5.433315842534427e-44j) Ewald components = 3.52824610955262e-242, -9.46183687521147, 8.45032262227713 init E= -1.28212108270539 CPU time for initialize scf 28.43 sec, wall time 28.55 sec HOMO = 0.291953859391 LUMO = 2.75780079409 k-point mo_energy 0 ( 0.000 0.000 0.000) [0.29195385] [2.75780261 2.75780261 2.75780261] 1 ( 0.000 0.000 0.500) [0.29195385] [2.75780079 2.75780265 2.75780265] 2 ( 0.000 0.500 0.000) [0.29195385] [2.75780079 2.75780265 2.75780265] 3 ( 0.000 0.500 0.500) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 4 ( 0.500 0.000 0.000) [0.29195385] [2.75780079 2.75780265 2.75780265] 5 ( 0.500 0.000 0.500) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 6 ( 0.500 0.500 0.000) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 7 ( 0.500 0.500 0.500) [0.29195386] [2.75780088 2.75780088 2.75780088] CPU time for get_k_kpts: make_kpt ki>=kj (0,*) 0.00 sec, wall time 0.00 sec CPU time for get_k_kpts: make_kpt ki>=kj (1,*) 0.00 sec, wall time 0.00 sec CPU time for get_k_kpts: make_kpt ki>=kj (2,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (3,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (4,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (5,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (6,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (7,*) 0.02 sec, wall time 0.02 sec Monkhorst pack size [2 2 2] ew_eta 2.1486892381881426 ew_cut 3.2214756178970303 Ewald components = 2.3977487592528e-255, -1.21250896277658, 1.08606968115978 CPU time for get_j pass 1 0.01 sec, wall time 0.01 sec CPU time for get_j pass 2 0.01 sec, wall time 0.01 sec CPU time for vj and vk 0.16 sec, wall time 0.16 sec E1 = (-1.1251213648610787-1.9278384392752661e-41j) E_coul = (0.8545145350900218-1.7155461258656707e-33j) Ewald components = 3.52824610955262e-242, -9.46183687521147, 8.45032262227713 cycle= 1 E= -1.28212108270539 delta_E= 0 |g|= 4.79e-16 |ddm|= 6.92e-10 CPU time for cycle= 1 0.23 sec, wall time 0.26 sec HOMO = 0.291953859339 LUMO = 2.75780079409 k-point mo_energy 0 ( 0.000 0.000 0.000) [0.29195385] [2.75780261 2.75780261 2.75780261] 1 ( 0.000 0.000 0.500) [0.29195385] [2.75780079 2.75780265 2.75780265] 2 ( 0.000 0.500 0.000) [0.29195385] [2.75780079 2.75780265 2.75780265] 3 ( 0.000 0.500 0.500) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 4 ( 0.500 0.000 0.000) [0.29195385] [2.75780079 2.75780265 2.75780265] 5 ( 0.500 0.000 0.500) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 6 ( 0.500 0.500 0.000) [0.29195385] [2.75780084 2.75780084 2.7578027 ] 7 ( 0.500 0.500 0.500) [0.29195386] [2.75780088 2.75780088 2.75780088] CPU time for get_k_kpts: make_kpt ki>=kj (0,*) 0.00 sec, wall time 0.00 sec CPU time for get_k_kpts: make_kpt ki>=kj (1,*) 0.00 sec, wall time 0.00 sec CPU time for get_k_kpts: make_kpt ki>=kj (2,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (3,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (4,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (5,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (6,*) 0.01 sec, wall time 0.01 sec CPU time for get_k_kpts: make_kpt ki>=kj (7,*) 0.02 sec, wall time 0.02 sec Monkhorst pack size [2 2 2] ew_eta 2.1486892381881426 ew_cut 3.2214756178970303 Ewald components = 2.3977487592528e-255, -1.21250896277658, 1.08606968115978 CPU time for get_j pass 1 0.01 sec, wall time 0.01 sec CPU time for get_j pass 2 0.01 sec, wall time 0.01 sec CPU time for vj and vk 0.15 sec, wall time 0.15 sec E1 = (-1.1251213648610787-1.927838420281932e-41j) E_coul = (0.8545145350900218-2.3847831400099395e-33j) Ewald components = 3.52824610955262e-242, -9.46183687521147, 8.45032262227713 Extra cycle E= -1.28212108270539 delta_E= 0 |g|= 1.67e-16 |ddm|= 2.75e-16 CPU time for scf_cycle 28.87 sec, wall time 29.03 sec CPU time for SCF 28.93 sec, wall time 29.08 sec converged SCF energy = -1.28212108270539 nk 8 [0 1 2 3 4 5 6 7] scf object is type EnergyAccumulator using Ewald {} Setting Ewald alpha to 0.44563279857397503 /projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/h5py/_hl/dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead. "Use dataset[()] instead.", H5pyDeprecationWarning) epoch 0 finished /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:256: RuntimeWarning: divide by zero encountered in true_divide return ratios[1:] / ratios[:1] /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:256: RuntimeWarning: invalid value encountered in true_divide return ratios[1:] / ratios[:1] /projects/wagner/wawheel2/code/pyqmc/pyqmc/mc.py:85: RuntimeWarning: invalid value encountered in greater mask = tot > cutoff /projects/wagner/wawheel2/code/pyqmc/pyqmc/mc.py:86: RuntimeWarning: invalid value encountered in true_divide g[mask, :] = cutoff * g[mask, :] / tot[mask, np.newaxis] /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:269: RuntimeWarning: divide by zero encountered in true_divide return ratios / testvalue /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:269: RuntimeWarning: invalid value encountered in true_divide return ratios / testvalue /projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/numpy/core/_methods.py:75: RuntimeWarning: invalid value encountered in reduce ret = umr_sum(arr, axis, dtype, out, keepdims) distributed.worker - WARNING - Compute Failed Function: execute_task args: ((, , (, [, ]), {'nsteps': 50, 'accumulators': {'energy': }, 'stepoffset': 50})) kwargs: {} Exception: LinAlgError('Singular matrix') /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:269: RuntimeWarning: divide by zero encountered in true_divide return ratios / testvalue /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:269: RuntimeWarning: invalid value encountered in true_divide return ratios / testvalue /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:256: RuntimeWarning: divide by zero encountered in true_divide return ratios[1:] / ratios[:1] /projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py:256: RuntimeWarning: invalid value encountered in true_divide return ratios[1:] / ratios[:1] /projects/wagner/wawheel2/code/pyqmc/pyqmc/mc.py:85: RuntimeWarning: invalid value encountered in greater mask = tot > cutoff /projects/wagner/wawheel2/code/pyqmc/pyqmc/mc.py:86: RuntimeWarning: invalid value encountered in true_divide g[mask, :] = cutoff * g[mask, :] / tot[mask, np.newaxis] /projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/numpy/core/_methods.py:75: RuntimeWarning: invalid value encountered in reduce ret = umr_sum(arr, axis, dtype, out, keepdims) distributed.worker - WARNING - Compute Failed Function: execute_task args: ((, , (, [, ]), {'nsteps': 50, 'accumulators': {'energy': }, 'stepoffset': 50})) kwargs: {} Exception: LinAlgError('Singular matrix') Traceback (most recent call last): File "hfvmc_bug.py", line 56, in client=client, nsteps_per=50, File "/projects/wagner/wawheel2/code/pyqmc/pyqmc/dasktools.py", line 68, in distvmc res = r.result() File "/projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/distributed/client.py", line 221, in result six.reraise(*result) File "/projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/six.py", line 692, in reraise raise value.with_traceback(tb) File "/projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/dask/compatibility.py", line 107, in apply return func(*args, **kwargs) File "/projects/wagner/wawheel2/code/pyqmc/pyqmc/mc.py", line 151, in vmc wf.recompute(configs) File "/projects/wagner/wawheel2/code/pyqmc/pyqmc/slaterpbc.py", line 177, in recompute self._inverse.append(np.linalg.inv(mo)) File "/projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/numpy/linalg/linalg.py", line 532, in inv ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj) File "/projects/wagner/anaconda3/envs/pyscf/lib/python3.7/site-packages/numpy/linalg/linalg.py", line 89, in _raise_linalgerror_singular raise LinAlgError("Singular matrix") numpy.linalg.linalg.LinAlgError: Singular matrix