openff-toolkit
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openforcefield
Partial charge methods fail in clean environment
Describe the bug
In a new, clean OpenFF environment on an x86-64 Mac (14.4.1), partial charge methods fail. This is either from antechamber or possibly sqm and probably related to issues that I've seen @mattwthompson talking about with the Amber folks.
For this test script:
from openff.toolkit.utils import get_data_file_path
from openff.toolkit.topology import Molecule, Topology
from openff.toolkit.typing.engines.smirnoff import ForceField
# This prepared PDB file from the toolkit's test suite is a box of solvents
pdb_path = get_data_file_path(
"systems/packmol_boxes/propane_methane_butanol_0.2_0.3_0.5.pdb"
)
molecules = [Molecule.from_smiles(smi) for smi in ["CCC", "C", "CCCCO"]]
# The OpenFF Toolkit can directly read PDB files!
topology = Topology.from_pdb(pdb_path, unique_molecules=molecules)
# Construct the Interchange with the OpenFF "Sage" force field
interchange = ForceField("openff-2.0.0.offxml").create_interchange(topology)
I'm seeing:
ToolkitWrapper around The RDKit version 2024.03.5 <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : partial_charge_method 'am1bcc' is not available from RDKitToolkitWrapper. Available charge methods are {'mmff94': {}, 'gasteiger': {}}
ToolkitWrapper around AmberTools version 23.6 <class 'subprocess.CalledProcessError'> : Command '['antechamber', '-i', 'molecule.sdf', '-fi', 'sdf', '-o', 'charged.mol2', '-fo', 'mol2', '-pf', 'yes', '-dr', 'n', '-c', 'bcc', '-nc', '0.0']' returned non-zero exit status 1.
ToolkitWrapper around Built-in Toolkit version None <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : Partial charge method "am1bcc"" is not supported by the Built-in toolkit. Available charge methods are {'zeros': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}, 'formal_charge': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}}
The test cases also fail.
To Reproduce
-
conda create --name openff-clean -
mamba install -c conda-forge openff-toolkit - Save snippet as
tmp.py -
python tmp.py
Output
/Users/slochowe/miniforge3/envs/openff-clean/bin/wrapped_progs/antechamber: Fatal Error!
Cannot properly run "/Users/slochowe/miniforge3/envs/openff-clean/bin/sqm -O -i sqm.in -o sqm.out".
Traceback (most recent call last):
File "/Users/slochowe/tmp/tmp.py", line 15, in <module>
interchange = ForceField("openff-2.0.0.offxml").create_interchange(topology)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/utilities/utilities.py", line 80, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/toolkit/typing/engines/smirnoff/forcefield.py", line 1252, in create_interchange
return Interchange.from_smirnoff(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/components/interchange.py", line 268, in from_smirnoff
return _create_interchange(
^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_create.py", line 112, in _create_interchange
_electrostatics(
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_create.py", line 267, in _electrostatics
"Electrostatics": SMIRNOFFElectrostaticsCollection.create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_nonbonded.py", line 449, in create
handler.store_matches(
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_nonbonded.py", line 860, in store_matches
matches, potentials = self._find_reference_matches(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_nonbonded.py", line 718, in _find_reference_matches
) = cls._find_charge_model_matches(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_nonbonded.py", line 654, in _find_charge_model_matches
partial_charges = cls._compute_partial_charges(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/interchange/smirnoff/_nonbonded.py", line 469, in _compute_partial_charges
molecule.assign_partial_charges(method)
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/toolkit/topology/molecule.py", line 2677, in assign_partial_charges
toolkit_registry.call(
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/toolkit/utils/toolkit_registry.py", line 280, in call
raise ValueError(msg)
ValueError: No registered toolkits can provide the capability "assign_partial_charges" for args "()" and kwargs "{'molecule': Molecule with name '' and SMILES '[H][O][C]([H])([H])[C]([H])([H])[C]([H])([H])[C]([H])([H])[H]', 'partial_charge_method': 'am1bcc', 'use_conformers': None, 'strict_n_conformers': False, 'normalize_partial_charges': True, '_cls': <class 'openff.toolkit.topology.molecule.Molecule'>}"
Available toolkits are: [ToolkitWrapper around The RDKit version 2024.03.5, ToolkitWrapper around AmberTools version 23.6, ToolkitWrapper around Built-in Toolkit version None]
ToolkitWrapper around The RDKit version 2024.03.5 <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : partial_charge_method 'am1bcc' is not available from RDKitToolkitWrapper. Available charge methods are {'mmff94': {}, 'gasteiger': {}}
ToolkitWrapper around AmberTools version 23.6 <class 'subprocess.CalledProcessError'> : Command '['antechamber', '-i', 'molecule.sdf', '-fi', 'sdf', '-o', 'charged.mol2', '-fo', 'mol2', '-pf', 'yes', '-dr', 'n', '-c', 'bcc', '-nc', '0.0']' returned non-zero exit status 1.
ToolkitWrapper around Built-in Toolkit version None <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : Partial charge method "am1bcc"" is not supported by the Built-in toolkit. Available charge methods are {'zeros': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}, 'formal_charge': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}}
`conda list`
❯ conda list
# packages in environment at /Users/slochowe/miniforge3/envs/openff-clean:
#
# Name Version Build Channel
ambertools 23.6 cuda_None_nompi_py312hc98840c_105 conda-forge
amberutils 21.0 pypi_0 pypi
annotated-types 0.7.0 pyhd8ed1ab_0 conda-forge
anyio 4.4.0 pyhd8ed1ab_0 conda-forge
appnope 0.1.4 pyhd8ed1ab_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py312h104f124_4 conda-forge
arpack 3.9.1 nompi_hf81eadf_101 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-lru 2.0.4 pyhd8ed1ab_0 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
babel 2.14.0 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.12.3 pyha770c72_0 conda-forge
bleach 6.1.0 pyhd8ed1ab_0 conda-forge
blosc 1.21.6 h7d75f6d_0 conda-forge
brotli 1.1.0 h0dc2134_1 conda-forge
brotli-bin 1.1.0 h0dc2134_1 conda-forge
brotli-python 1.1.0 py312heafc425_1 conda-forge
bson 0.5.9 py_0 conda-forge
bzip2 1.0.8 hfdf4475_7 conda-forge
c-ares 1.32.3 h51dda26_0 conda-forge
c-blosc2 2.15.0 hb9356d3_1 conda-forge
ca-certificates 2024.7.4 h8857fd0_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.4.0 pyhd8ed1ab_0 conda-forge
cairo 1.18.0 h37bd5c4_3 conda-forge
certifi 2024.7.4 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py312h38bf5a0_0 conda-forge
chardet 5.2.0 py312hb401068_1 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
comm 0.2.2 pyhd8ed1ab_0 conda-forge
contourpy 1.2.1 py312h9230928_0 conda-forge
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
debugpy 1.8.2 py312h28f332c_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
edgembar 0.2 pypi_0 pypi
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.2.2 pyhd8ed1ab_0 conda-forge
executing 2.0.1 pyhd8ed1ab_0 conda-forge
expat 2.6.2 h73e2aa4_0 conda-forge
fftw 3.3.10 nompi_h292e606_110 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 h77eed37_2 conda-forge
fontconfig 2.14.2 h5bb23bf_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.53.1 py312hbd25219_0 conda-forge
fqdn 1.5.1 pyhd8ed1ab_0 conda-forge
freetype 2.12.1 h60636b9_2 conda-forge
freetype-py 2.3.0 pyhd8ed1ab_0 conda-forge
greenlet 3.0.3 py312hede676d_0 conda-forge
h11 0.14.0 pyhd8ed1ab_0 conda-forge
h2 4.1.0 pyhd8ed1ab_0 conda-forge
hdf4 4.2.15 h8138101_7 conda-forge
hdf5 1.14.3 nompi_h687a608_105 conda-forge
hpack 4.0.0 pyh9f0ad1d_0 conda-forge
httpcore 1.0.5 pyhd8ed1ab_0 conda-forge
httpx 0.27.0 pyhd8ed1ab_0 conda-forge
hyperframe 6.0.1 pyhd8ed1ab_0 conda-forge
icu 75.1 h120a0e1_0 conda-forge
idna 3.7 pyhd8ed1ab_0 conda-forge
importlib-metadata 8.2.0 pyha770c72_0 conda-forge
importlib_metadata 8.2.0 hd8ed1ab_0 conda-forge
importlib_resources 6.4.0 pyhd8ed1ab_0 conda-forge
ipykernel 6.29.5 pyh57ce528_0 conda-forge
ipython 8.26.0 pyh707e725_0 conda-forge
ipywidgets 8.1.3 pyhd8ed1ab_0 conda-forge
isoduration 20.11.0 pyhd8ed1ab_0 conda-forge
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
jinja2 3.1.4 pyhd8ed1ab_0 conda-forge
joblib 1.4.2 pyhd8ed1ab_0 conda-forge
json5 0.9.25 pyhd8ed1ab_0 conda-forge
jsonpointer 3.0.0 py312hb401068_0 conda-forge
jsonschema 4.23.0 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.12.1 pyhd8ed1ab_0 conda-forge
jsonschema-with-format-nongpl 4.23.0 hd8ed1ab_0 conda-forge
jupyter-lsp 2.2.5 pyhd8ed1ab_0 conda-forge
jupyter_client 8.6.2 pyhd8ed1ab_0 conda-forge
jupyter_core 5.7.2 py312hb401068_0 conda-forge
jupyter_events 0.10.0 pyhd8ed1ab_0 conda-forge
jupyter_server 2.14.2 pyhd8ed1ab_0 conda-forge
jupyter_server_terminals 0.5.3 pyhd8ed1ab_0 conda-forge
jupyterlab 4.2.4 pyhd8ed1ab_0 conda-forge
jupyterlab_pygments 0.3.0 pyhd8ed1ab_1 conda-forge
jupyterlab_server 2.27.3 pyhd8ed1ab_0 conda-forge
jupyterlab_widgets 3.0.11 pyhd8ed1ab_0 conda-forge
khronos-opencl-icd-loader 2023.04.17 h37ebe6b_1 conda-forge
kiwisolver 1.4.5 py312h49ebfd2_1 conda-forge
krb5 1.21.3 h37d8d59_0 conda-forge
lcms2 2.16 ha2f27b4_0 conda-forge
lerc 4.0.0 hb486fe8_0 conda-forge
libaec 1.1.3 h73e2aa4_0 conda-forge
libblas 3.9.0 22_osx64_openblas conda-forge
libboost 1.84.0 hcca3243_4 conda-forge
libboost-python 1.84.0 py312h44e70fa_4 conda-forge
libbrotlicommon 1.1.0 h0dc2134_1 conda-forge
libbrotlidec 1.1.0 h0dc2134_1 conda-forge
libbrotlienc 1.1.0 h0dc2134_1 conda-forge
libcblas 3.9.0 22_osx64_openblas conda-forge
libcurl 8.9.0 hfcf2730_0 conda-forge
libcxx 18.1.8 hef8daea_0 conda-forge
libdeflate 1.20 h49d49c5_0 conda-forge
libedit 3.1.20191231 h0678c8f_2 conda-forge
libev 4.33 h10d778d_2 conda-forge
libexpat 2.6.2 h73e2aa4_0 conda-forge
libffi 3.4.2 h0d85af4_5 conda-forge
libgfortran 5.0.0 13_2_0_h97931a8_3 conda-forge
libgfortran5 13.2.0 h2873a65_3 conda-forge
libglib 2.80.3 h736d271_1 conda-forge
libiconv 1.17 hd75f5a5_2 conda-forge
libintl 0.22.5 h5ff76d1_2 conda-forge
libjpeg-turbo 3.0.0 h0dc2134_1 conda-forge
liblapack 3.9.0 22_osx64_openblas conda-forge
libnetcdf 4.9.2 nompi_h7334405_114 conda-forge
libnghttp2 1.58.0 h64cf6d3_1 conda-forge
libopenblas 0.3.27 openmp_h8869122_1 conda-forge
libpng 1.6.43 h92b6c6a_0 conda-forge
libpq 16.3 h4501773_0 conda-forge
librdkit 2024.03.5 hbc19afa_1 conda-forge
libsodium 1.0.18 hbcb3906_1 conda-forge
libsqlite 3.46.0 h1b8f9f3_0 conda-forge
libssh2 1.11.0 hd019ec5_0 conda-forge
libtiff 4.6.0 h129831d_3 conda-forge
libwebp-base 1.4.0 h10d778d_0 conda-forge
libxcb 1.16 h0dc2134_0 conda-forge
libxml2 2.12.7 heaf3512_4 conda-forge
libzip 1.10.1 hc158999_3 conda-forge
libzlib 1.3.1 h87427d6_1 conda-forge
llvm-openmp 18.1.8 h15ab845_0 conda-forge
lz4-c 1.9.4 hf0c8a7f_0 conda-forge
markupsafe 2.1.5 py312h41838bb_0 conda-forge
matplotlib-base 3.9.1 py312h0d5aeb7_0 conda-forge
matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge
mda-xdrlib 0.2.0 pyhd8ed1ab_0 conda-forge
mdtraj 1.10.0 py312h00f8f5a_0 conda-forge
mistune 3.0.2 pyhd8ed1ab_0 conda-forge
mmpbsa-py 16.0 pypi_0 pypi
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
nbclient 0.10.0 pyhd8ed1ab_0 conda-forge
nbconvert-core 7.16.4 pyhd8ed1ab_1 conda-forge
nbformat 5.10.4 pyhd8ed1ab_0 conda-forge
ncurses 6.5 h5846eda_0 conda-forge
nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge
netcdf-fortran 4.6.1 nompi_h3a6982b_104 conda-forge
networkx 3.3 pyhd8ed1ab_1 conda-forge
notebook 7.2.1 pyhd8ed1ab_0 conda-forge
notebook-shim 0.2.4 pyhd8ed1ab_0 conda-forge
numexpr 2.10.0 py312h1171441_0 conda-forge
numpy 1.26.4 py312he3a82b2_0 conda-forge
ocl_icd_wrapper_apple 1.0.0 hbcb3906_0 conda-forge
openff-amber-ff-ports 0.0.4 pyhca7485f_0 conda-forge
openff-forcefields 2024.07.0 pyhff2d567_0 conda-forge
openff-interchange 0.3.28 pyhd8ed1ab_0 conda-forge
openff-interchange-base 0.3.28 pyhd8ed1ab_0 conda-forge
openff-models 0.1.2 pyhca7485f_0 conda-forge
openff-toolkit 0.16.2 pyhd8ed1ab_0 conda-forge
openff-toolkit-base 0.16.2 pyhd8ed1ab_0 conda-forge
openff-units 0.2.2 pyhca7485f_0 conda-forge
openff-utilities 0.1.12 pyhd8ed1ab_0 conda-forge
openjpeg 2.5.2 h7310d3a_0 conda-forge
openmm 8.1.2 py312hacce00b_2_khronos conda-forge
openssl 3.3.1 h87427d6_2 conda-forge
overrides 7.7.0 pyhd8ed1ab_0 conda-forge
packaging 24.1 pyhd8ed1ab_0 conda-forge
packmol-memgen 2024.2.9 pypi_0 pypi
pandas 2.2.2 py312h1171441_1 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
panedr 0.8.0 pyhd8ed1ab_0 conda-forge
parmed 4.2.2 py312h444b7ae_1 conda-forge
parso 0.8.4 pyhd8ed1ab_0 conda-forge
pcre2 10.44 h7634a1b_0 conda-forge
pdb4amber 22.0 pypi_0 pypi
perl 5.32.1 7_h10d778d_perl5 conda-forge
pexpect 4.9.0 pyhd8ed1ab_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 10.4.0 py312hbd70edc_0 conda-forge
pint 0.23 pyhd8ed1ab_1 conda-forge
pip 24.0 pyhd8ed1ab_0 conda-forge
pixman 0.43.4 h73e2aa4_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_1 conda-forge
platformdirs 4.2.2 pyhd8ed1ab_0 conda-forge
prometheus_client 0.20.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.47 pyha770c72_0 conda-forge
psutil 6.0.0 py312hbd25219_0 conda-forge
pthread-stubs 0.4 hc929b4f_1001 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.3 pyhd8ed1ab_0 conda-forge
py-cpuinfo 9.0.0 pyhd8ed1ab_0 conda-forge
pycairo 1.26.1 py312h4a9e434_0 conda-forge
pycparser 2.22 pyhd8ed1ab_0 conda-forge
pydantic 2.8.2 pyhd8ed1ab_0 conda-forge
pydantic-core 2.20.1 py312ha47ea1c_0 conda-forge
pyedr 0.8.0 pyhd8ed1ab_0 conda-forge
pygments 2.18.0 pyhd8ed1ab_0 conda-forge
pymsmt 22.0 pypi_0 pypi
pyobjc-core 10.3.1 py312he77c50b_0 conda-forge
pyobjc-framework-cocoa 10.3.1 py312he77c50b_0 conda-forge
pyparsing 3.1.2 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
pytables 3.9.2 py312hd51072b_3 conda-forge
python 3.12.4 h37a9e06_0_cpython conda-forge
python-constraint 1.4.0 py_0 conda-forge
python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.20.0 pyhd8ed1ab_0 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python-tzdata 2024.1 pyhd8ed1ab_0 conda-forge
python_abi 3.12 4_cp312 conda-forge
pytraj 2.0.6 pypi_0 pypi
pytz 2024.1 pyhd8ed1ab_0 conda-forge
pyyaml 6.0.1 py312h104f124_1 conda-forge
pyzmq 26.0.3 py312ha04878a_0 conda-forge
qhull 2020.2 h3c5361c_5 conda-forge
rdkit 2024.03.5 py312hcfd6466_1 conda-forge
readline 8.2 h9e318b2_1 conda-forge
referencing 0.35.1 pyhd8ed1ab_0 conda-forge
reportlab 4.2.2 py312hbd25219_0 conda-forge
requests 2.32.3 pyhd8ed1ab_0 conda-forge
rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rlpycairo 0.2.0 pyhd8ed1ab_0 conda-forge
rpds-py 0.19.1 py312ha47ea1c_0 conda-forge
sander 22.0 pypi_0 pypi
scipy 1.14.0 py312hb9702fa_1 conda-forge
send2trash 1.8.3 pyh31c8845_0 conda-forge
setuptools 71.0.4 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
smirnoff99frosst 1.1.0 pyh44b312d_0 conda-forge
snappy 1.2.1 he1e6707_0 conda-forge
sniffio 1.3.1 pyhd8ed1ab_0 conda-forge
soupsieve 2.5 pyhd8ed1ab_1 conda-forge
sqlalchemy 2.0.31 py312hbd25219_0 conda-forge
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
terminado 0.18.1 pyh31c8845_0 conda-forge
tinycss2 1.3.0 pyhd8ed1ab_0 conda-forge
tk 8.6.13 h1abcd95_1 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
tornado 6.4.1 py312hbd25219_0 conda-forge
tqdm 4.66.4 pyhd8ed1ab_0 conda-forge
traitlets 5.14.3 pyhd8ed1ab_0 conda-forge
types-python-dateutil 2.9.0.20240316 pyhd8ed1ab_0 conda-forge
typing-extensions 4.12.2 hd8ed1ab_0 conda-forge
typing_extensions 4.12.2 pyha770c72_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge
tzdata 2024a h0c530f3_0 conda-forge
uri-template 1.3.0 pyhd8ed1ab_0 conda-forge
urllib3 2.2.2 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.13 pyhd8ed1ab_0 conda-forge
webcolors 24.6.0 pyhd8ed1ab_0 conda-forge
webencodings 0.5.1 pyhd8ed1ab_2 conda-forge
websocket-client 1.8.0 pyhd8ed1ab_0 conda-forge
wheel 0.43.0 pyhd8ed1ab_1 conda-forge
widgetsnbextension 4.0.11 pyhd8ed1ab_0 conda-forge
xmltodict 0.13.0 pyhd8ed1ab_0 conda-forge
xorg-kbproto 1.0.7 h35c211d_1002 conda-forge
xorg-libice 1.1.1 h0dc2134_0 conda-forge
xorg-libsm 1.2.4 h0dc2134_0 conda-forge
xorg-libx11 1.8.9 h7022169_1 conda-forge
xorg-libxau 1.0.11 h0dc2134_0 conda-forge
xorg-libxdmcp 1.1.3 h35c211d_0 conda-forge
xorg-libxext 1.3.4 hb7f2c08_2 conda-forge
xorg-libxt 1.3.0 h0dc2134_1 conda-forge
xorg-xextproto 7.3.0 hb7f2c08_1003 conda-forge
xorg-xproto 7.0.31 h35c211d_1007 conda-forge
xz 5.2.6 h775f41a_0 conda-forge
yaml 0.2.5 h0d85af4_2 conda-forge
zeromq 4.3.5 hde137ed_4 conda-forge
zipp 3.19.2 pyhd8ed1ab_0 conda-forge
zlib 1.3.1 h87427d6_1 conda-forge
zlib-ng 2.2.1 hf036a51_0 conda-forge
zstd 1.5.6 h915ae27_0 conda-forge
CC @mikemhenry
Thanks for the detailed writeup. I'm unable to reproduce on an ARM mac, but I'm charging my old intel mac to try it there.
Some of our CI should be testing on intel macs, and I'm not seeing any issues there, though
- They're using macos 12 instead of 14.
- I can't recall if these tests parameterize that particular solvent box
- there may be some difference due to the GHA host being virtualized
Unable to reproduce with intel mac on OSX 13.2.1, updating to 14 and will report back.
Thanks! And on my end, this is reproducible (not related to this specific molecule/solvent box). Hit the bug trying to use OpenFE stuff (on real ligands) with @mikemhenry and we initially thought it might be related to RDKit, but it does not seem to be. I'm happy to do additional debugging if you need.
Darn, my intel macbook isn't compatible with OSX 14. If you're up to iterate a bit, could you let me know if this reproduces the issue?
from openff.toolkit import Molecule
mol = Molecule.from_smiles('[H][O][C]([H])([H])[C]([H])([H])[C]([H])([H])[C]([H])([H])[H]')
mol.assign_partial_charges("am1bcc")
And if that DOES reproduce the issue, could you run
mol.generate_conformers()
mol.to_file('temp.sdf', file_format='sdf')
and in the terminal
antechamber -i molecule.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0
and let me know what happens?
Oh wait!
I ran it once and it completed without error.
I ran it a second time and it did reproduce the error. Fun.
~/tmp is 📦 v0.1.0 via 🐍 v3.12.4 via 🅒 openff-clean took 10s
❯ python tmp5.py
(openff-clean)
~/tmp is 📦 v0.1.0 via 🐍 v3.12.4 via 🅒 openff-clean took 27s
❯ python tmp5.py
/Users/slochowe/miniforge3/envs/openff-clean/bin/wrapped_progs/antechamber: Fatal Error!
Cannot properly run "/Users/slochowe/miniforge3/envs/openff-clean/bin/sqm -O -i sqm.in -o sqm.out".
Traceback (most recent call last):
File "/Users/slochowe/tmp/tmp5.py", line 3, in <module>
mol.assign_partial_charges("am1bcc")
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/toolkit/topology/molecule.py", line 2677, in assign_partial_charges
toolkit_registry.call(
File "/Users/slochowe/miniforge3/envs/openff-clean/lib/python3.12/site-packages/openff/toolkit/utils/toolkit_registry.py", line 280, in call
raise ValueError(msg)
ValueError: No registered toolkits can provide the capability "assign_partial_charges" for args "()" and kwargs "{'molecule': Molecule with name '' and SMILES '[H][O][C]([H])([H])[C]([H])([H])[C]([H])([H])[C]([H])([H])[H]', 'partial_charge_method': 'am1bcc', 'use_conformers': None, 'strict_n_conformers': False, 'normalize_partial_charges': True, '_cls': <class 'openff.toolkit.topology.molecule.Molecule'>}"
Available toolkits are: [ToolkitWrapper around The RDKit version 2024.03.5, ToolkitWrapper around AmberTools version 23.6, ToolkitWrapper around Built-in Toolkit version None]
ToolkitWrapper around The RDKit version 2024.03.5 <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : partial_charge_method 'am1bcc' is not available from RDKitToolkitWrapper. Available charge methods are {'mmff94': {}, 'gasteiger': {}}
ToolkitWrapper around AmberTools version 23.6 <class 'subprocess.CalledProcessError'> : Command '['antechamber', '-i', 'molecule.sdf', '-fi', 'sdf', '-o', 'charged.mol2', '-fo', 'mol2', '-pf', 'yes', '-dr', 'n', '-c', 'bcc', '-nc', '0.0']' returned non-zero exit status 1.
ToolkitWrapper around Built-in Toolkit version None <class 'openff.toolkit.utils.exceptions.ChargeMethodUnavailableError'> : Partial charge method "am1bcc"" is not supported by the Built-in toolkit. Available charge methods are {'zeros': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}, 'formal_charge': {'rec_confs': 0, 'min_confs': 0, 'max_confs': 0}}
@slochower can you try this? for i in (seq 10); python tmp5.py; end and lets see how often this happens... (assuming you are using fish shell)
Looks like 5/10. Perfect.
I made a slight modification for i in (seq 10); python tmp5.py && echo "Pass"; end and counted "Pass" five times.
Here's another version:
↪ for i in (seq 10); python tmp5.py &> /dev/null && echo "Pass $i"; end
Pass 5
Pass 7
Pass 8
Okay, so it's Ambertools that is leading to the mess.
↪ for i in (seq 10); antechamber -i temp.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0 &> /dev/null && echo "Pass $i"; end
Pass 2
Pass 4
Pass 5
Pass 8
Pass 10
WHen it fails, it's linked to sqm.
And this is what happens when sqm fails.
❯ cat sqm.in
Run semi-empirical minimization
&qmmm
qm_theory='AM1', grms_tol=0.0005,
scfconv=1.d-10, ndiis_attempts=700, qmcharge=0,
/
❯ cat sqm.out
--------------------------------------------------------
AMBER SQM VERSION 19
By
Ross C. Walker, Michael F. Crowley, Scott Brozell,
Tim Giese, Andreas W. Goetz,
Tai-Sung Lee and David A. Case
--------------------------------------------------------
QM ATOM VALIDATION: nquant has a value of 0
SANDER BOMB in subroutine validate_qm_atoms
nquant illegal
Need 0 < nquant <= natom
The input file should have the coordinates in it after the / but in cases where it fails, it just ends like above, without coordinates. A correct file would be something like this:
Run semi-empirical minimization
&qmmm
qm_theory='AM1', grms_tol=0.0005,
scfconv=1.d-10, ndiis_attempts=700, qmcharge=0,
/
1 H1 2.5450 1.2970 0.8130
8 O1 2.5880 0.3750 0.4790
6 C1 1.3640 0.0320 -0.1430
1 H2 1.6070 -0.8440 -0.7780
1 H3 1.0220 0.8280 -0.8300
6 C2 0.4040 -0.2880 0.9570
1 H4 0.8200 -1.1770 1.4820
1 H5 0.3520 0.5510 1.6630
6 C3 -0.9630 -0.6780 0.4760
1 H6 -1.6150 -0.8110 1.3650
1 H7 -1.0030 -1.6010 -0.0910
6 C4 -1.6300 0.4120 -0.3350
1 H8 -0.9920 0.7070 -1.1940
1 H9 -1.9350 1.2690 0.3110
1 H10 -2.5640 -0.0710 -0.7400
Can you try installing ambertools 22? Hopefully it doesn't cause too much package churn, on my system it looks like this:
- ambertools=22
Package Version Build Channel Size
─────────────────────────────────────────────────────────────────────────────────
Install:
─────────────────────────────────────────────────────────────────────────────────
+ boost-cpp 1.78.0 h2c5509c_4 conda-forge Cached
+ cython 3.0.10 py310hc6cd4ac_0 conda-forge Cached
+ packmol 20.15.0 hc8b2c43_0 conda-forge 130kB
+ boost 1.78.0 py310hcb52e73_5 conda-forge Cached
Remove:
─────────────────────────────────────────────────────────────────────────────────
- libboost 1.84.0 hba137d9_3 conda-forge Cached
- libboost-python 1.84.0 py310he6ccd79_3 conda-forge Cached
Downgrade:
─────────────────────────────────────────────────────────────────────────────────
- arpack 3.9.1 nompi_h77f6705_101 conda-forge Cached
+ arpack 3.7.0 hdefa2d7_2 conda-forge Cached
- ambertools 23.6 nompi_py310hcbc9ba0_103 conda-forge Cached
+ ambertools 22.5 py310hd182041_0 conda-forge Cached
- rdkit 2024.03.3 py310h6f17f40_0 conda-forge Cached
+ rdkit 2023.03.3 py310h399bcf7_0 conda-forge Cached
which for our testing purposes isn't too bad, another option would be to keep ambertools 23, but just install an older build, like before mpi support was added for example.
Oof. Couldn't install with the existing 3.12 environment. Stepped back to 3.11, also dependency conflicts. Stepped back to 3.10, also dependency conflicts...
warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE
Could not solve for environment specs
The following packages are incompatible
├─ ambertools 22** is installable with the potential options
│ ├─ ambertools 22.0 would require
│ │ └─ boost-cpp >=1.74.0,<1.74.1.0a0 with the potential options
│ │ ├─ boost-cpp 1.74.0 would require
│ │ │ └─ icu >=68.1,<69.0a0 , which can be installed;
│ │ ├─ boost-cpp [1.74.0|1.78.0] would require
│ │ │ └─ icu >=70.1,<71.0a0 , which can be installed;
│ │ ├─ boost-cpp 1.74.0 would require
│ │ │ └─ icu >=69.1,<70.0a0 , which can be installed;
│ │ └─ boost-cpp 1.74.0 would require
│ │ └─ icu >=67.1,<68.0a0 , which can be installed;
│ ├─ ambertools 22.0 would require
│ │ └─ python >=3.7,<3.8.0a0 , which can be installed;
│ ├─ ambertools [22.0|22.1|...|22.5] would require
│ │ └─ python >=3.8,<3.9.0a0 , which can be installed;
│ ├─ ambertools [22.0|22.1|...|22.5] would require
│ │ └─ python >=3.9,<3.10.0a0 , which can be installed;
│ └─ ambertools [22.0|22.1|...|22.5] would require
│ └─ boost-cpp >=1.78.0,<1.78.1.0a0 with the potential options
│ ├─ boost-cpp [1.74.0|1.78.0], which can be installed (as previously explained);
│ ├─ boost-cpp 1.78.0 would require
│ │ ├─ icu >=73.2,<74.0a0 , which conflicts with any installable versions previously reported;
│ │ └─ libboost <0 , which can be installed;
│ └─ boost-cpp 1.78.0 would require
│ └─ icu >=72.1,<73.0a0 , which can be installed;
├─ libboost is installable with the potential options
│ ├─ libboost [1.84.0|1.85.0] would require
│ │ └─ icu >=75.1,<76.0a0 , which conflicts with any installable versions previously reported;
│ ├─ libboost 1.84.0 would require
│ │ ├─ boost-cpp 1.84.0* , which conflicts with any installable versions previously reported;
│ │ └─ icu >=73.2,<74.0a0 , which conflicts with any installable versions previously reported;
│ ├─ libboost 1.82.0 would require
│ │ └─ icu >=72.1,<73.0a0 , which can be installed;
│ ├─ libboost 1.82.0 would require
│ │ └─ boost-cpp 1.82.0* , which conflicts with any installable versions previously reported;
│ ├─ libboost 1.83.0 would require
│ │ └─ boost-cpp 1.83.0* , which conflicts with any installable versions previously reported;
│ ├─ libboost 1.85.0 would require
│ │ └─ boost-cpp 1.85.0* , which conflicts with any installable versions previously reported;
│ ├─ libboost [1.65.1|1.67.0|1.71.0|1.73.0] would require
│ │ └─ icu >=58.2,<59.0a0 , which can be installed;
│ └─ libboost 1.82.0 conflicts with any installable versions previously reported;
└─ librdkit is not installable because it requires
└─ libboost >=1.84.0,<1.85.0a0 , which cannot be installed (as previously explained).
Sadly not. Looks like libboost is the culprit, at least when trying in the original environment.
↪ mamba install ambertools=23.3 (openff-clean)
Looking for: ['ambertools=23.3']
conda-forge/osx-64 Using cache
conda-forge/noarch Using cache
pkgs/main/noarch No change
pkgs/r/osx-64 No change
pkgs/r/noarch No change
pkgs/main/osx-64 No change
Pinned packages:
- python 3.12.*
warning libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE
Could not solve for environment specs
The following packages are incompatible
├─ ambertools 23.3** is installable with the potential options
│ ├─ ambertools 23.3 would require
│ │ └─ libboost >=1.82.0,<1.83.0a0 with the potential options
│ │ ├─ libboost 1.82.0 would require
│ │ │ └─ icu >=72.1,<73.0a0 , which can be installed;
│ │ ├─ libboost 1.82.0 would require
│ │ │ └─ icu >=73.2,<74.0a0 , which can be installed;
│ │ └─ libboost 1.82.0 would require
│ │ └─ icu >=73.1,<74.0a0 , which can be installed;
│ ├─ ambertools 23.3 would require
│ │ └─ python >=3.10,<3.11.0a0 , which can be installed;
│ ├─ ambertools 23.3 would require
│ │ └─ python >=3.11,<3.12.0a0 , which can be installed;
│ ├─ ambertools 23.3 would require
│ │ └─ python >=3.8,<3.9.0a0 , which can be installed;
│ └─ ambertools 23.3 would require
│ └─ python >=3.9,<3.10.0a0 , which can be installed;
└─ librdkit is not installable because it requires
└─ libboost >=1.84.0,<1.85.0a0 but there are no viable options
├─ libboost 1.84.0 would require
│ └─ icu >=75.1,<76.0a0 , which conflicts with any installable versions previously reported;
└─ libboost 1.84.0 conflicts with any installable versions previously reported.
Is that making a fresh env? Using some hacks (I am on linux) it looks like it should solve on osx-arm64 CONDA_SUBDIR="osx-arm64" micromamba create -n foooooobar --dry-run -c conda-forge ambertools=23.3 "openff-toolkit=0.16.*" BUT I know sometimes when you have an env, the solver can get stuck trying to figure things out but a fresh one can help it out.
Good point. Although 23.3 is still flaky. I can step back to 22 in a new environment.
↪ for i in (seq 10); antechamber -i temp.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0 &> /dev/null && echo "Pass $i"; end
Pass 1
Pass 3
Pass 4
Pass 5
Pass 6
Pass 7
Here's ambertools=22 ...
↪ for i in (seq 10); antechamber -i temp.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0 &> /dev/null && echo "Pass $i"; end
Pass 2
Pass 3
Pass 4
Pass 5
Pass 7
Pass 8
😰
okay one more idea,
antechamber -i molecule.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0 -ek
see if adding the -ek arg helps at all
You are correct! -ek pseudo_diag=0 We can use -ek to pass in arguments to sqm, I've seen some weird things where diagonalization options/methods causes sqm to not be deterministic -- this is a bit of a rabbit hole since even if we figure out some collection of arguments that makes sqm work consistently, it is unclear if we can get those options upstreamed into the toolkit.
Another option to pass in is diag_routine=3, so try -ek "pseudo_diag=0, diag_routine=3" and see if that gets us 10 passes.
↪ antechamber -i temp.sdf -fi sdf -o charged.mol2 -fo mol2 -pf yes -dr n -c bcc -nc 0.0 -ek pseudo_diag=0
Welcome to antechamber 22.0: molecular input file processor.
Info: The atom type is set to gaff; the options available to the -at flag are
gaff, gaff2, amber, bcc, and sybyl.
Info: Total number of electrons: 0; net charge: 0
Running: /Users/slochowe/miniforge3/envs/foo2/bin/sqm -O -i sqm.in -o sqm.out
/Users/slochowe/miniforge3/envs/foo2/bin/wrapped_progs/antechamber: Fatal Error!
Cannot properly run "/Users/slochowe/miniforge3/envs/foo2/bin/sqm -O -i sqm.in -o sqm.out".
I don't think this is a problem inside sqm but rather -- the input file is not being written correctly:
↪ bat sqm.in (foo2)
───────┬────────────────────────────────────────────────────────────────────────
│ File: sqm.in
───────┼────────────────────────────────────────────────────────────────────────
1 │ Run semi-empirical minimization
2 │ &qmmm
3 │ pseudo_diag=0 qmcharge=0,
4 │ /
5 │
───────┴────────────────────────────────────────────────────────────────────────
IIUC, the toolkit doesn't actually write that file, so something before that step is failing?
I can't reproduce this behavior this morning using cuda_None_nompi_py312hc98840c_105
You are correct!
-ek pseudo_diag=0We can use-ekto pass in arguments to sqm, I've seen some weird things where diagonalization options/methods causessqmto not be deterministic -- this is a bit of a rabbit hole since even if we figure out some collection of arguments that makes sqm work consistently, it is unclear if we can get those options upstreamed into the toolkit.Another option to pass in is
diag_routine=3, so try-ek "pseudo_diag=0, diag_routine=3"and see if that gets us 10 passes.
Hi, I've been experiencing a similar issue with partial charge assignment failing. In my case, it looks like it wasn't a conformer generation issue as in #1741 but the SCF not converging within the default number of steps. Doing it manuallly with -ek pseudo_diag=0 solved it for me.
What would be the way to fallback to this solution in python? Currently I have:
for mol in mols:
mol:Molecule
try:
mol.assign_partial_charges("am1bcc")
except ValueError:
mol.assign_partial_charges(
"am1bcc", use_conformers=mol.conformers
) # doesn't work! How can I rum the -ek pseudo_diag=0 here?
except Exception as e:
logging.error(f"Error in assigning partial charges: {e}")
raise e
Maybe @mattwthompson has an idea?
There are a few options here but I think @j-wags will have to decide which is the best recommendation to give
Hi @RokasEl - Apologies for the delay. I'm quite hesitant to make any changes to .assign_partial_charges methods (even adding an additional keyword for a new behavior) since the API and behavior is so deeply rooted in many of our pathways. So unfortunately I don't think there's a clean fallback. I'd recommend basically copying the existing assign_partial_charges into a utility method in your script and adding the custom antechamber/sqm flags there.