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DMRG solver gives random order of multiple roots calculation #69

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hungpham2017 opened this issue Nov 30, 2018 · 12 comments
Closed

DMRG solver gives random order of multiple roots calculation #69

hungpham2017 opened this issue Nov 30, 2018 · 12 comments

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@hungpham2017
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Hello Seb,

I am trying to compare the PySCF/FCI and CheMPS2/DMRG solver for excited state calculations.
When I executed the script over and over, the FCI is stable while the DMRG solver gave random orders of excited state energies, and even sometimes give an excited state energy for the ground state (one root calculation).
What would be the possible reason? Maybe there were anything wrong in the way I used it.
Thank you,
Hung

Here is the script:

import numpy as np
from functools import reduce
from pyscf import gto, scf, fci, ao2mo

mol = gto.M(atom='Be 0 0 0', basis='sto3g')
m = scf.RHF(mol).run()
norb = m.mo_coeff.shape[1]
nelec = mol.nelec
h1e = reduce(np.dot, (m.mo_coeff.T, m.get_hcore(), m.mo_coeff))
g2e = ao2mo.incore.general(m._eri, (m.mo_coeff,)*4, compact=False).reshape(norb,norb,norb,norb)   
fs = fci.addons.fix_spin_(fci.FCI(mol, m.mo_coeff), .5)
fs.nroots = 5
e, fcivec = fs.kernel(verbose=0)

import PyCheMPS2
import ctypes, os, sys
Initializer = PyCheMPS2.PyInitialize()
Initializer.Init()
Group = 0
orbirreps = np.zeros([norb], dtype=ctypes.c_int)
HamCheMPS2 = PyCheMPS2.PyHamiltonian(norb, Group, orbirreps)

#Feed the 1e and 2e integral (T and V)
for orb1 in range(norb):
    for orb2 in range(norb):
        HamCheMPS2.setTmat(orb1, orb2, h1e[orb1, orb2])
        for orb3 in range(norb):
            for orb4 in range(norb):
                HamCheMPS2.setVmat(orb1, orb2, orb3, orb4, g2e[orb1, orb3, orb2, orb4]) #From chemist to physics notation		

TwoS = mol.spin       
Nel_up = (mol.nelectron+ TwoS) // 2
Nel_down = mol.nelectron - Nel_up 
Irrep = 0
maxMemWorkMB = m.max_memory

Prob  = PyCheMPS2.PyProblem(HamCheMPS2, TwoS, mol.nelectron, Irrep)
OptScheme = PyCheMPS2.PyConvergenceScheme(4)
OptScheme.setInstruction(0,  200, 1e-8,  5, 0.03)		
OptScheme.setInstruction(1,  500, 1e-8,  5, 0.03)
OptScheme.setInstruction(2, 1000, 1e-8,  5, 0.03)
OptScheme.setInstruction(3, 1000, 1e-8,  100, 0.00)

theDMRG = PyCheMPS2.PyDMRG(Prob, OptScheme)
EDMRG0 = theDMRG.Solve()
EDMRG = []
theDMRG.activateExcitations(5)
for i in range(4):
    theDMRG.newExcitation(20.0)
    EDMRG.append(theDMRG.Solve())  

print("PySCF  : Root1: %15.8f, Root2: %15.8f, Root3: %15.8f, Root4: %15.8f, Root5: %15.8f" % (e[0],e[1],e[2],e[3],e[4]))
print("CheMPS2: Root1: %15.8f, Root2: %15.8f, Root3: %15.8f, Root4: %15.8f, Root5: %15.8f" % (EDMRG0, EDMRG[0], EDMRG[1], EDMRG[2], EDMRG[3]))
@hungpham2017 hungpham2017 changed the title DMRG solver gives random and wrong order of multiple roots DMRG solver gives random order of multiple roots Nov 30, 2018
@hungpham2017 hungpham2017 changed the title DMRG solver gives random order of multiple roots DMRG solver gives random order of multiple roots calculation Nov 30, 2018
@SebWouters
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Hi @hungpham2017

Be in STO-3G has 4 orbitals, which is a rather small test case, better to just use FCI then and no DMRG.

Once DMRG gets stuck in a local minimum, it's hard to get out. Normally, the test case is big. In that case:

  1. The MPS state underlying DMRG is large and contains sufficient noise, due to which it's (comparatively) unlikely to get stuck in the wrong state.
  2. The utilized virtual dimension in DMRG is smaller than the one needed for representing the FCI state via an MPS. In that case, there is a discarded weight in DMRG optimizations near the middle of the MPS. Noise is introduced in the algorithm in an amount 0.5 * noise_factor * weight_discarded (see e.g. https://arxiv.org/pdf/1405.1225.pdf, section 3.4.3). If there never is a discarded weight, because the virtual dimension is sufficiently large, no noise will be introduced.

In your case Be in STO-3G is so small (4 orbitals), that it is very likely to get stuck in the wrong state and no noise is added as the virtual dimension for representing the FCI solution would be (not taking SU(2) into consideration) 16.

Given the above explanation, please test with a little bit larger system, for example O (oxygen) and 6-31G:

import numpy as np
from functools import reduce
from pyscf import gto, scf, fci, ao2mo

mol = gto.M(atom={'O 0 0 0'}, basis='6-31G')
m = scf.RHF(mol).run()
norb = m.mo_coeff.shape[1]
nelec = mol.nelec
h1e = reduce(np.dot, (m.mo_coeff.T, m.get_hcore(), m.mo_coeff))
g2e = ao2mo.incore.general(m._eri, (m.mo_coeff,)*4, compact=False).reshape(norb,norb,norb,norb)   
fs = fci.addons.fix_spin_(fci.FCI(mol, m.mo_coeff), .5)
fs.nroots = 16
e, fcivec = fs.kernel(verbose=0)

import PyCheMPS2
import ctypes, os, sys
Initializer = PyCheMPS2.PyInitialize()
Initializer.Init()
Group = 0
orbirreps = np.zeros([norb], dtype=ctypes.c_int)
HamCheMPS2 = PyCheMPS2.PyHamiltonian(norb, Group, orbirreps)

#Feed the 1e and 2e integral (T and V)
for orb1 in range(norb):
    for orb2 in range(norb):
        HamCheMPS2.setTmat(orb1, orb2, h1e[orb1, orb2])
        for orb3 in range(norb):
            for orb4 in range(norb):
                HamCheMPS2.setVmat(orb1, orb2, orb3, orb4, g2e[orb1, orb3, orb2, orb4]) #From chemist to physics notation		

TwoS = mol.spin       
Nel_up = (mol.nelectron+ TwoS) // 2
Nel_down = mol.nelectron - Nel_up 
Irrep = 0
maxMemWorkMB = m.max_memory

Prob  = PyCheMPS2.PyProblem(HamCheMPS2, TwoS, mol.nelectron, Irrep)
OptScheme = PyCheMPS2.PyConvergenceScheme(4)
OptScheme.setInstruction(0,  200, 1e-4,  5, 0.03)		
OptScheme.setInstruction(1,  500, 1e-5,  5, 0.03)
OptScheme.setInstruction(2, 1000, 1e-6,  5, 0.001)
OptScheme.setInstruction(3, 1000, 1e-8,  100, 0.00)

theDMRG = PyCheMPS2.PyDMRG(Prob, OptScheme)
EDMRG0 = theDMRG.Solve()
EDMRG = []
theDMRG.activateExcitations(15)
for i in range(15):
    theDMRG.newExcitation(20.0)
    EDMRG.append(theDMRG.Solve())  

print("PySCF  : Root0: %15.8f, Root1: %15.8f, Root2: %15.8f, Root3: %15.8f, Root4: %15.8f, Root5: %15.8f, Root6: %15.8f, Root7: %15.8f, Root8: %15.8f, Root9: %15.8f, Root10: %15.8f, Root11: %15.8f, Root12: %15.8f, Root13: %15.8f, Root14: %15.8f, Root15: %15.8f" % (e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15]))
print("CheMPS2: Root0: %15.8f, Root1: %15.8f, Root2: %15.8f, Root3: %15.8f, Root4: %15.8f, Root5: %15.8f, Root6: %15.8f, Root7: %15.8f, Root8: %15.8f, Root9: %15.8f, Root10: %15.8f, Root11: %15.8f, Root12: %15.8f, Root13: %15.8f, Root14: %15.8f, Root15: %15.8f" % (EDMRG0, EDMRG[0], EDMRG[1], EDMRG[2], EDMRG[3], EDMRG[4], EDMRG[5], EDMRG[6], EDMRG[7], EDMRG[8], EDMRG[9], EDMRG[10], EDMRG[11], EDMRG[12], EDMRG[13], EDMRG[14]))

The results are given here:

Run 1
PySCF  : Root0:    -74.75708817, Root1:    -74.75708817, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708817, Root5:    -74.69661726, Root6:    -73.91627244, Root7:    -73.91627239, Root8:    -73.91627239, Root9:    -73.77153553, Root10:    -73.77134495, Root11:    -73.76997568, Root12:    -73.64050889, Root13:    -73.63932580, Root14:    -73.63835551, Root15:    -73.63829142
CheMPS2: Root0:    -74.75708817, Root1:    -74.75708817, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708817, Root5:    -74.69661726, Root6:    -73.91627281, Root7:    -73.91627281, Root8:    -73.91627281, Root9:    -73.77134991, Root10:    -73.77134991, Root11:    -73.77134991, Root12:    -73.64080297, Root13:    -73.64080297, Root14:    -73.64080297, Root15:    -73.64080297

Run 2
PySCF  : Root0:    -78.84326124, Root1:    -74.75708817, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708817, Root5:    -74.75708817, Root6:    -74.69661726, Root7:    -73.91627281, Root8:    -73.91627281, Root9:    -73.91627278, Root10:    -73.77611243, Root11:    -73.77133784, Root12:    -73.77125551, Root13:    -73.64057296, Root14:    -73.64043002, Root15:    -73.64035129
CheMPS2: Root0:    -74.75708817, Root1:    -74.75708817, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708817, Root5:    -74.69661726, Root6:    -73.91627281, Root7:    -73.91627281, Root8:    -73.91627281, Root9:    -73.77134991, Root10:    -73.77134991, Root11:    -73.77134991, Root12:    -73.64080297, Root13:    -73.64080297, Root14:    -73.64080297, Root15:    -73.64080297

Run 3
PySCF  : Root0:    -74.75709041, Root1:    -74.75708820, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708816, Root5:    -74.69661726, Root6:    -73.91627299, Root7:    -73.91627283, Root8:    -73.91627255, Root9:    -73.77134943, Root10:    -73.77110371, Root11:    -73.77094994, Root12:    -73.64067206, Root13:    -73.64029826, Root14:    -73.63934603, Root15:    -73.63610232
CheMPS2: Root0:    -74.75708817, Root1:    -74.75708817, Root2:    -74.75708817, Root3:    -74.75708817, Root4:    -74.75708817, Root5:    -74.69661726, Root6:    -73.91627281, Root7:    -73.91627281, Root8:    -73.91627281, Root9:    -73.77134991, Root10:    -73.77134991, Root11:    -73.77134991, Root12:    -73.64080297, Root13:    -73.64080297, Root14:    -73.64080297, Root15:    -73.64080297

As you can observe, it is now PySCF which gives varying and wrong solutions in different runs, while CheMPS2 is consistent over the consecutive runs.

@sunqm: Any idea?

Best regards,
Sebastian

P.S.: The magic number "20.0" in the script above should be about 10 times the difference between the largest energy and the smallest energy you target. Better to make the number too large than too small. In chemistry, it makes sense to set it at fabs(energy_ground_state).

@sunqm
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sunqm commented Dec 1, 2018

The fix_spin_ function adds a penalty energy to the states of wrong spin. This
function can lead to noise in the answer since it changes the quadratic region
of the Hamiltonian. I tested the system on my desktop. With fix_spin_, small
fluctuations are always found for the degenerated states. Changing the level
shift value or removing this function is helpful to converge to the right results.

Another possible issue is the race condition bug of FCI code in the old versions
of pyscf (<1.5.4), if your local version is not updated.

@hungpham2017
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Thank you @SebWouters for the comprehensive explanation and your thesis.
I am trying to run your example and just simply increasing the number of processors: export OMP_NUM_THREADS = 4. The calculation is taking long and seems frozen the step below for more than 10 hours. Is this correct way to take advantage of omp parallelization in PyCheMPS2?

   CheMPS2: a spin-adapted implementation of DMRG for ab initio quantum chemistry
   Copyright (C) 2013-2018 Sebastian Wouters

   This program is free software; you can redistribute it and/or modify
   it under the terms of the GNU General Public License as published by
   the Free Software Foundation; either version 2 of the License, or
   (at your option) any later version.

   This program is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU General Public License for more details.

   You should have received a copy of the GNU General Public License along
   with this program; if not, write to the Free Software Foundation, Inc.,
   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

   Stats: nIt(DAVIDSON) = 14
Energy at sites (7, 8) is -48.4737074036521
   Stats: nIt(DAVIDSON) = 23
Energy at sites (6, 7) is -57.2275246628521
   Stats: nIt(DAVIDSON) = 63
Energy at sites (5, 6) is -66.3927895371372

@sunqm I am running the oxygen example with my local version (1.5.4) to see if the fluctuation still occur with FCI. One follow up question is the race condition bug of FCI significantly affects ground state and for multiples root calculations using the CASSCF solver?

@sunqm
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sunqm commented Dec 2, 2018

@hungpham2017 The bug affects all states

@hungpham2017
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It is maybe off topic a bit since it is more about FCI solver in PySCF, but maybe @SebWouters is also interested.
@sunqm : here are a few tests.
You're right that without using fix_spin, there is no fuctuation and some states have wrong spin as expected.
fix_spin_ using the level shift of 1.0 gave more consitent results but give the ground state -78.4276810400. also in @SebWouters calculation.

fix_spin_ using the level shift of 0.5 gave fuctuated results.
So the question here is how to use the fix_spin function efficiently? what would be the optimal value of the level shift?
Thank you very much!

  1. Without using fix_spin_ function.
#       Run 1           Run 2           Run 4           Run 4       2S + 1 (Run 5)
1	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
2	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
3	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
4	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
5	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
6	-74.6966172600	-74.6966172600	-74.6966172600	-74.6966172600	1.0000000000
7	-73.9162728100	-73.9162728100	-73.9162728100	-73.9162728100	1.0000000000
8	-73.9162728100	-73.9162728100	-73.9162728100	-73.9162728100	1.0000000000
9	-73.9162728100	-73.9162728100	-73.9162728100	-73.9162728100	1.0000000100
10	-73.8568848900	-73.8568848900	-73.8568848900	-73.8568848900	5.0000000000
11	-73.8568848900	-73.8568848900	-73.8568848900	-73.8568848900	5.0000000000
12	-73.8568848900	-73.8568848900	-73.8568848900	-73.8568848900	5.0000000000
13	-73.7713499100	-73.7713499100	-73.7713499100	-73.8001318200	1.0000000000
14	-73.7713499100	-73.7713499100	-73.7713499100	-73.7713499100	1.0000000000
15	-73.7713499100	-73.7713499100	-73.7713499100	-73.7713499100	1.0000000000
16	-73.6408029700	-73.6408029700	-73.6408029700	-73.7713499100	1.0000000000
  1. fix_spin_ with Eshift = 0.5
#       Run 1           Run 2           Run 4           Run 4       2S + 1 (Run 5)
1	-74.7570881700	-74.7570921300	-74.7570881900	-74.7570881900	1.0000000000
2	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
3	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.0000000000
4	-74.7570881700	-74.7570880600	-74.7570881700	-74.7570881700	1.0000000000
5	-74.7570876800	-74.7570870400	-74.7570881600	-74.7570881600	1.0000000000
6	-74.6966172600	-74.6966172600	-74.6966172600	-74.6966172600	1.0000000000
7	-73.9162715800	-73.9162728100	-73.9162727500	-73.9162727500	1.0000000000
8	-73.9162715800	-73.9162728100	-73.9162727500	-73.9162727500	1.0000000000
9	-73.9162713200	-73.9162728100	-73.9162727100	-73.9162727100	1.0000000000
10	-73.7709858400	-73.7744947700	-73.7713534500	-73.7713534500	1.0000000600
11	-73.7703598500	-73.7721523000	-73.7711208700	-73.7711208700	1.0000001000
12	-73.7701488500	-73.7711647800	-73.7680227400	-73.7680227400	1.0000000800
13	-73.6393650500	-73.6593311300	-73.6411810200	-73.6411810200	1.0000000500
14	-73.6387661600	-73.6403737200	-73.6401056900	-73.6401056900	1.0000001000
15	-73.6379877700	-73.6394735300	-73.6380977400	-73.6380977400	1.0000058400
16	-73.6375239500	-73.5721729600	-73.6350080200	-73.6350080200	1.0000006900
  1. fix_spin_ with Eshift = 1.0
#       Run 1           Run 2           Run 4           Run 4       2S + 1 (Run 5)
1	-78.4276810400	-78.4276810400	-78.4276810400	-78.4276810400	1.00000000
2	-74.7570883000	-74.7570883000	-74.7570883000	-74.7570883000	1.00000000
3	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.00000000
4	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.00000000
5	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.00000000
6	-74.7570881700	-74.7570881700	-74.7570881700	-74.7570881700	1.00000000
7	-74.6966172600	-74.6966172600	-74.6966172600	-74.6966172600	1.00000063
8	-73.9162704000	-73.9162704000	-73.9162704000	-73.9162704000	1.00000063
9	-73.9162704000	-73.9162704000	-73.9162704000	-73.9162704000	1.00000096
10	-73.9162659800	-73.9162659800	-73.9162659800	-73.9162659800	1.00000573
11	-73.7712955500	-73.7712955500	-73.7712955500	-73.7712955500	1.00000826
12	-73.7712075600	-73.7712075600	-73.7712075600	-73.7712075600	1.00108825
13	-73.7377514600	-73.7377514600	-73.7377514600	-73.7377514600	1.00016501
14	-73.6386089300	-73.6386089300	-73.6386089300	-73.6386089300	1.00004948
15	-73.6376151000	-73.6376151000	-73.6376151000	-73.6376151000	1.00046548
16	-73.6341031500	-73.6341031500	-73.6341031500	-73.6341031500	1.00040077

@SebWouters
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@hungpham2017

I am trying to run your example and just simply increasing the number of processors: export OMP_NUM_THREADS = 4. The calculation is taking long and seems frozen the step below for more than 10 hours. Is this correct way to take advantage of omp parallelization in PyCheMPS2?

When setting

export OMP_NUM_THREADS=4

on my machine, my original example runs fine. So I cannot tell with certainty.

I guess it has something to do with the Davidson residual tolerance being too strict. You can use

OptScheme.set_instruction(0,  200, 1e-6,     5, 0.03,  1e-3)		
OptScheme.set_instruction(1,  500, 1e-6,     5, 0.03,  1e-4)
OptScheme.set_instruction(2, 1000, 1e-6,     5, 0.01,  1e-5)
OptScheme.set_instruction(3, 1000, 1e-10,  100, 0.00,  1e-6)

instead. Note that the Davidson residual tolerance increases from 1e-3 to 1e-6. In the first sweep, the MPS is far off, and it makes no sense to put convergence criteria too strict. You can play with these entries, in order:

  • reduced virtual dimension (200, 500, 1000);
  • energy convergence (1e-6, 1e-10);
  • maximum number of sweeps (5, 100);
  • noise prefactor (0.03, 0.01, 0.00); and
  • Davidson residual tolerance (1e-3, 1e-4, 1e-5, 1e-6).

@hungpham2017
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hungpham2017 commented Dec 2, 2018

@SebWouters
I tried to loosen the tolerance as you suggested.
The CheMPS2 calculation seems to take very long (even it hangs) and has not finished yet. FCI calculation was very quick (1-2 minutes). Is there possibly any installation problem with my local version?
How long did the calculation in your computer take? Do you have any reference that compares the computational time for FCI and DMRG for those both methods are affordable?
Thank you

@SebWouters
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@hungpham2017

I don't think there's a problem with your installation per se. The only thing I can think of is a different floating point specification (due to OS or compiler), or numerical instability. How do the CheMPS2 binary tests perform? Test2 should also be prone to "hanging" I guess.

The calculation takes only a couple of minutes for all excited states, i.e. less than a minute per state, on my computer.

Regarding comparison of computational efforts: I think starting around 14 orbitals, DMRG becomes more efficient than FCI. For a smaller number of orbitals, it is always best to use FCI.

I'll try to get you timing output a.s.a.p.

Best regards,
Sebastian

@SebWouters
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@hungpham2017

@wpoely86 just let me know that there might be a "hanging" problem in CheMPS2 with OpenMP, as he encountered a similar thing. Can you provide information relating to your system (os, compiler, omp library, ...)?

@hungpham2017
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I installed CheMPS2 on my anaconda environment. I do apologize for long response. I just want to show you all the packages I have installed. In the mean time, I am trying to reinstalled the CheMPS2 manually.
You don't need to give me the detailed time ming, if you said it was only a few minutes then probably there i some problem with my local version.

Here are the configuration:

lsb_release -a
LSB Version:    :base-4.0-amd64:base-4.0-ia32:base-4.0-noarch:core-4.0-amd64:core-4.0-ia32:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-ia32:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-ia32:printing-4.0-noarch
Distributor ID: CentOS
Description:    CentOS release 6.10 (Final)
Release:        6.10
Codename:       Final

my anaconda env:

@labc03 [~] % conda list
# packages in environment at /home/gagliard/phamx494/anaconda:
#
# Name                    Version                   Build  Channel
_ipyw_jlab_nb_ext_conf    0.1.0                    py36_0  
_tflow_select             2.3.0                       mkl  
absl-py                   0.5.0                    py36_0  
alabaster                 0.7.12                   py36_0  
anaconda                  custom           py36hbbc8b67_0  
anaconda-client           1.7.2                    py36_0  
anaconda-project          0.8.2                    py36_0  
asn1crypto                0.24.0                   py36_0  
astor                     0.7.1                    py36_0  
astroid                   2.0.4                    py36_0  
atomicwrites              1.2.1                    py36_0  
attrs                     18.2.0           py36h28b3542_0  
babel                     2.6.0                    py36_0  
backcall                  0.1.0                    py36_0  
backports                 1.0                      py36_1  
backports.functools_lru_cache 1.4                      py36_1    conda-forge
backports.os              0.1.1                    py36_0  
backports.shutil_get_terminal_size 1.0.0                    py36_2  
backports.weakref         1.0.post1                py36_0  
beautifulsoup4            4.6.3                    py36_0  
bitarray                  0.8.3            py36h14c3975_0  
blas                      1.0                    openblas  
bleach                    1.5.0                    py36_0    conda-forge
blosc                     1.14.4               hdbcaa40_0  
boto                      2.49.0                   py36_0  
bzip2                     1.0.6                h14c3975_5  
ca-certificates           2018.03.07                    0  
cairo                     1.14.12              h8948797_3  
certifi                   2018.10.15               py36_0  
cffi                      1.11.5           py36he75722e_1  
chardet                   3.0.4                    py36_1  
chemps2                   1.8.7                h8c3debe_2    psi4/label/dev
click                     7.0                      py36_0  
cloog                     0.18.0                        0  
cloudpickle               0.6.1                    py36_0  
clyent                    1.2.2                    py36_1  
cmake                     3.12.3               h011004d_0    conda-forge
colorama                  0.4.0                    py36_0  
conda                     4.5.11                   py36_0  
conda-build               3.16.1                   py36_0  
conda-env                 2.6.0                         1  
conda-verify              3.1.1                    py36_0  
contextlib2               0.5.5                    py36_0  
cryptography              2.3                      py36_0    intel
curl                      7.61.0               h84994c4_0  
cycler                    0.10.0                   py36_0  
cython                    0.29             py36hfc679d8_0    conda-forge
cytoolz                   0.9.0.1          py36h14c3975_1  
dask-core                 0.19.4                   py36_0  
dbus                      1.13.2               h714fa37_1  
decorator                 4.3.0                    py36_0  
distributed               1.23.3                   py36_0  
dkh                       1.2                           1    psi4/label/dev
docutils                  0.14                     py36_0  
eigen                     3.3.5                h2d50403_1    conda-forge
entrypoints               0.2.3                    py36_2  
erd                       3.0.6                         1    psi4/label/dev
et_xmlfile                1.0.1                    py36_0  
expat                     2.2.6                he6710b0_0  
fastcache                 1.0.2            py36h14c3975_2  
fftw                      3.3.8                h470a237_0    conda-forge
filelock                  3.0.9                    py36_0  
flask                     1.0.2                    py36_1  
flask-cors                3.0.6                    py36_0  
fontconfig                2.13.0               h9420a91_0  
freetype                  2.9.1                h6debe1e_4    conda-forge
fribidi                   1.0.5                h7b6447c_0  
future                    0.16.0                   py36_0  
gast                      0.2.0                    py36_0  
gcc-5                     5.2.0                         1    psi4
gcc-5-mp                  5.2.0                         0    psi4
gdma                      2.2.6                         3    psi4/label/dev
get_terminal_size         1.0.0                haa9412d_0  
gettext                   0.19.8.1             hd7bead4_3  
gevent                    1.3.7            py36h7b6447c_0  
glib                      2.56.2               h464dc38_0    conda-forge
glibc214                  2.14.1               ha26e528_0    pwwang
glob2                     0.6                      py36_1  
gmp                       6.1.2                h6c8ec71_1  
gmpy2                     2.0.8            py36hc8893dd_2  
graphite2                 1.3.12               h23475e2_2  
greenlet                  0.4.15           py36h7b6447c_0  
grpcio                    1.12.1           py36hdbcaa40_0  
gst-plugins-base          1.14.0               hbbd80ab_1  
gstreamer                 1.14.0               hb453b48_1  
h5py                      2.8.0            py36ha1f6525_0  
harfbuzz                  1.9.0                h04dbb29_1    conda-forge
hdf5                      1.10.2               hba1933b_1  
heapdict                  1.0.0                    py36_2  
html5lib                  0.9999999                py36_0    conda-forge
icu                       58.2                 h9c2bf20_1  
idna                      2.7                      py36_0  
imagesize                 1.1.0                    py36_0  
importlib_metadata        0.6                      py36_0  
intel-openmp              2019.0                      118  
intelpython               2019.0                        2    intel
iomp5                     15.0.3                        7    psi4
ipykernel                 5.1.0            py36h39e3cac_0  
ipython                   7.0.1            py36h39e3cac_0  
ipython_genutils          0.2.0                    py36_0  
ipywidgets                7.4.2                    py36_0  
isl                       0.12.2                        0  
isort                     4.3.4                    py36_0  
itsdangerous              0.24                     py36_1  
jbig                      2.1                  hdba287a_0  
jdcal                     1.4                      py36_0  
jedi                      0.13.1                   py36_0  
jeepney                   0.4                      py36_0  
jinja2                    2.10                     py36_0  
jpeg                      9b                   h024ee3a_2  
jsonschema                2.6.0                    py36_0  
jupyter_client            5.2.3                    py36_0  
jupyter_console           6.0.0                    py36_0  
jupyter_core              4.4.0                    py36_0  
jupyterlab                0.35.1                   py36_0  
jupyterlab_launcher       0.13.1                   py36_0  
jupyterlab_server         0.2.0                    py36_0  
keyring                   15.1.0                   py36_0  
kiwisolver                1.0.1            py36hf484d3e_0  
krb5                      1.14.6                        0    conda-forge
lapack                    3.6.1                         1    conda-forge
lawrap                    0.1                           0    psi4/label/dev
lazy-object-proxy         1.3.1            py36h14c3975_2  
libarchive                3.3.2                hb43526a_6  
libcurl                   7.61.0               h1ad7b7a_0  
libedit                   3.1.20170329         haf1bffa_1    conda-forge
libffi                    3.2.1                hd88cf55_4  
libgcc                    5.2.0                         0    msarahan
libgcc-5                  5.4.0                         2    ostrokach
libgcc-ng                 8.2.0                hdf63c60_1  
libgfortran               3.0.0                         1    conda-forge
libgfortran-ng            7.3.0                hdf63c60_0  
libiconv                  1.15                 h63c8f33_5  
libint                    1.2.1                         0    psi4
libopenblas               0.3.3                h5a2b251_3  
libpng                    1.6.34               ha92aebf_2    conda-forge
libprotobuf               3.6.0                hdbcaa40_0  
libsodium                 1.0.16               h1bed415_0  
libssh2                   1.8.0                h9cfc8f7_4  
libstdcxx-ng              8.2.0                hdf63c60_1  
libtiff                   4.0.9                he85c1e1_2  
libtool                   2.4.6                h7b6447c_5  
libuuid                   1.0.3                h1bed415_2  
libuv                     1.23.2               h470a237_0    conda-forge
libxc                     3.0.0                         3    psi4
libxcb                    1.13                 h1bed415_1  
libxml2                   2.9.8                h26e45fe_1  
libxslt                   1.1.32               h1312cb7_0  
llvm-meta                 7.0.0                         0    conda-forge
llvmlite                  0.25.0           py36hd408876_0  
locket                    0.2.0                    py36_1  
lxml                      4.2.5            py36hefd8a0e_0  
lz4-c                     1.8.1.2              h14c3975_0  
lzo                       2.10                 h49e0be7_2  
markdown                  3.0.1                    py36_0  
markupsafe                1.0              py36h14c3975_1  
matplotlib                3.0.1                h8a2030e_1    conda-forge
matplotlib-base           3.0.1            py36hc039c98_1    conda-forge
mccabe                    0.6.1                    py36_1  
mistune                   0.8.4            py36h7b6447c_0  
mkl                       2019.0                intel_117    intel
mkl-include               2019.0                intel_117    intel
mkl_fft                   1.0.6                    py36_0    conda-forge
mkl_random                1.0.1                    py36_0    conda-forge
more-itertools            4.3.0                    py36_0  
mpc                       1.0.1                         0  
mpfr                      3.1.2                         0  
mpi                       1.0                       mpich    conda-forge
mpi4py                    3.0.0              py36_mpich_3    conda-forge
mpich                     3.2.1                h26a2512_5    conda-forge
mpich2                    1.4.1p1                       0    anaconda
mpmath                    1.0.0                    py36_2  
msgpack-python            0.5.6            py36h6bb024c_1  
multipledispatch          0.6.0                    py36_0  
nbconvert                 5.3.1                    py36_0  
nbformat                  4.4.0                    py36_0  
ncurses                   6.1                  hfc679d8_1    conda-forge
networkx                  2.2                      py36_1  
ninja                     1.8.2            py36h6bb024c_1  
nltk                      3.3.0                    py36_0  
nose                      1.3.7                    py36_2  
notebook                  5.7.0                    py36_0  
numba                     0.40.0           py36hf8a1672_0    conda-forge
numexpr                   2.6.8            py36hf8a1672_0    conda-forge
numpy                     1.15.3           py36h99e49ec_0  
numpy-base                1.15.3           py36h2f8d375_0  
numpydoc                  0.8.0                    py36_0  
olefile                   0.46                     py36_0  
openblas                  0.3.3                ha44fe06_1    conda-forge
openmp                    7.0.0                h2d50403_0    conda-forge
openpyxl                  2.5.8                    py36_0  
openssl                   1.0.2p               h14c3975_0  
packaging                 18.0                     py36_0  
pandoc                    2.2.3.2                       0  
pandocfilters             1.4.2                    py36_1  
pango                     1.40.14              he752989_2    conda-forge
parso                     0.3.1                    py36_0  
partd                     0.3.9                    py36_0  
patchelf                  0.9                  he6710b0_3  
path.py                   11.5.0                   py36_0  
pathlib2                  2.3.2                    py36_0  
pcmsolver                 1.1.10                   py36_1    psi4/label/dev
pcre                      8.42                 h439df22_0  
pep8                      1.7.1                    py36_0  
pexpect                   4.6.0                    py36_0  
pickleshare               0.7.5                    py36_0  
pillow                    5.3.0            py36h34e0f95_0  
pip                       10.0.1                   py36_0  
pixman                    0.34.0               hceecf20_3  
pkginfo                   1.4.2                    py36_1  
pluggy                    0.7.1            py36h28b3542_0  
ply                       3.11                     py36_0  
prometheus_client         0.4.2                    py36_0  
prompt_toolkit            2.0.6                    py36_0  
protobuf                  3.6.0            py36hf484d3e_0  
psutil                    5.4.7            py36h14c3975_0  
ptyprocess                0.6.0                    py36_0  
py                        1.7.0                    py36_0  
pybind11                  2.2.4            py36hfd86e86_0  
pychemps2                 1.8.7            py36ha05f3a8_2    psi4/label/dev
pycodestyle               2.4.0                    py36_0  
pycosat                   0.6.3            py36h14c3975_0  
pycparser                 2.19                     py36_0  
pycrypto                  2.6.1            py36h14c3975_9  
pycurl                    7.43.0.2         py36hb7f436b_0  
pyflakes                  2.0.0                    py36_0  
pygments                  2.2.0                    py36_0  
pylint                    2.1.1                    py36_0  
pyodbc                    4.0.24           py36he6710b0_0  
pyopenssl                 18.0.0                   py36_0  
pyparsing                 2.2.2                    py36_0  
pyqt                      5.6.0                    py36_2  
pysocks                   1.6.8                    py36_0  
pytest                    3.8.2                    py36_0  
pytest-openfiles          0.3.0                    py36_0  
pytest-remotedata         0.3.0                    py36_0  
python                    3.6.6                hc3d631a_0  
python-dateutil           2.7.3                    py36_0  
python-libarchive-c       2.8                      py36_6  
pytz                      2018.5                   py36_0  
pyyaml                    3.13             py36h14c3975_0  
pyzmq                     17.1.2           py36h14c3975_0  
qt                        5.6.3                h39df351_1  
qt5                       5.3.1                         1    dsdale24
qtawesome                 0.5.1                    py36_1  
qtpy                      1.5.1                    py36_0  
readline                  7.0                  haf1bffa_1    conda-forge
requests                  2.19.1                   py36_0  
rhash                     1.3.6                hb7f436b_0  
rope                      0.11.0                   py36_0  
ruamel_yaml               0.15.46          py36h14c3975_0  
scikit-learn              0.20.0           py36h22eb022_1  
scipy                     1.1.0            py36he2b7bc3_1  
secretstorage             3.1.0                    py36_0  
send2trash                1.5.0                    py36_0  
setuptools                40.4.3                   py36_0  
simint                    0.7                           0    psi4
simplegeneric             0.8.1                    py36_2  
singledispatch            3.4.0.3                  py36_0  
sip                       4.19.8           py36hf484d3e_0  
six                       1.11.0                   py36_1  
snappy                    1.1.7                hbae5bb6_3  
snowballstemmer           1.2.1                    py36_0  
sortedcollections         1.0.1                    py36_0  
sortedcontainers          2.0.5                    py36_0  
sphinx                    1.8.1                    py36_0  
sphinxcontrib             1.0                      py36_1  
sphinxcontrib-websupport  1.1.0                    py36_1  
spyder-kernels            0.2.6                    py36_0  
sqlalchemy                1.2.12           py36h7b6447c_0  
sqlite                    3.25.2               hb1c47c0_0    conda-forge
sympy                     1.3                      py36_0  
tbb                       2019.1                  intel_0    intel
tblib                     1.3.2                    py36_0  
tensorboard               1.10.0                   py36_0    conda-forge
tensorflow                1.10.0                   py36_0    conda-forge
termcolor                 1.1.0                    py36_1  
terminado                 0.8.1                    py36_1  
testpath                  0.4.2                    py36_0  
tk                        8.6.8                hbc83047_0  
toolz                     0.9.0                    py36_0  
tornado                   5.1.1            py36h7b6447c_0  
tqdm                      4.26.0           py36h28b3542_0  
traitlets                 4.3.2                    py36_0  
typed-ast                 1.1.0            py36h14c3975_0  
typing                    3.6.4                    py36_0  
unicodecsv                0.14.1                   py36_0  
unixodbc                  2.3.7                h14c3975_0  
urllib3                   1.23                     py36_0  
wcwidth                   0.1.7                    py36_0  
webencodings              0.5.1                    py36_1  
werkzeug                  0.14.1                   py36_0  
wheel                     0.32.1                   py36_0  
widgetsnbextension        3.4.2                    py36_0  
wrapt                     1.10.11          py36h14c3975_2  
xlrd                      1.1.0                    py36_1  
xlsxwriter                1.1.1                    py36_0  
xlwt                      1.3.0                    py36_0  
xz                        5.2.4                h14c3975_4  
yaml                      0.1.7                had09818_2  
zeromq                    4.2.5                hf484d3e_1  
zict                      0.1.3                    py36_0  
zlib                      1.2.11               ha838bed_2  
zstd                      1.3.3                h84994c4_0 

@loriab
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loriab commented Dec 3, 2018

@hungpham2017, I spot some really old packages and mixed channels in that conda environment. conda has undergone a substantial upgrade of their underlying toolchain. In particular, the gcc & the iomp5 can be updated, the mkl can be got from a more consistent channel, and it's always a bad idea to have both openblas and mkl installed in the same env. Psi4 has seen some weird behaviour with that.

I recommend a new conda env conda create -n nuchemps2 python=3.6 pychemps2 -c psi4. That should get you gcc 7.2 or 7.3 and mkl from the defaults (not intel or conda-forge) channel. Pretty much only chemps2 and pychemps should be from a non-default channel.

@hungpham2017
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Thank you very much @loriab and @SebWouters , actually it worked after I installed it in a new environment.
Before I am using the gcc-5 because the interactive queue at our HPC doesn't support the new libc.so.6 that required for gcc-7.
I guess I need to figure out the best way to solver these independent and not messing up with some codes I already installed.

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