# bespokefit — OpenFF BespokeFit with two reference levels:
#   * Psi4 (BespokeFit's default B3LYP-D3BJ/DZVP), and
#   * MACE-OMOL-0 / MACE-OFF23 through the QCEngine adapters in mace_adapter.py
#     (this directory).
#
# Create:
#   conda env create -p <prefix>/bespokefit -f environment.yml
#   <prefix>/bespokefit/bin/pip install --no-deps mace-torch==0.3.16
#
# Pins that matter:
#   qcengine/qcelemental <0.50   BespokeFit 0.4.3 uses the v1 QCSchema models.
#   e3nn=0.4.4                   required exactly by mace-torch 0.3.16.
#   openff-forcefields>=2024.09  provides openff_unconstrained-2.2.1 (Sage 2.2.1,
#                                MD-tools' default ligand force field).
# mace-torch is installed with --no-deps so pip cannot replace conda's numpy.
name: bespokefit
channels:
  - conda-forge
  - nodefaults
dependencies:
  - python=3.11
  - openff-bespokefit=0.4.3
  - openff-toolkit
  - openff-forcefields>=2024.09
  - ambertools
  - psi4
  - torsiondrive
  - geometric
  - qcengine<0.50
  - qcelemental<0.50
  - forcebalance
  - redis-server
  - cuda-version=12.9
  - pytorch=*=cuda*
  - ase
  - e3nn=0.4.4
  - matscipy
  - h5py
  - torch-ema
  - opt_einsum_fx
  - configargparse
  - gitpython
  - prettytable
  - python-lmdb
  - orjson
  - torchmetrics
  - lightning-utilities
  - python-hostlist
  - matplotlib
  - pandas
  - pip