Enerzyme

Towards next-generation machine learning force fields for enzymatic catalysis.

Currently supported model architectures:

Model	Type	Energy and force prediction	Charge and dipole prediction	Fully modulized	Shallow ensemble	Reference paper	Reference code
PhysNet	internal	✅	✅	✅	✅	J. Chem. Theory Comput. 2019, 15, 3678–3693	Github
SpookyNet	internal	✅	✅	✅	✅	Nat. Commun. 2021, 12(1), 7273	Github
LEFTNet	internal	✅	✅	❌	❌	NeurIPS 2023, arXiv:2304.04757	Github
MACE	external	✅	❌	❌	❌	NeurIPS 2022, arXiv:2206.07697	Github
NequIP	external	✅	❌	❌	❌	Nat. Commun. 2022, 13(1), 2453	Github
XPaiNN	external	✅	❌	❌	❌	J. Chem. Theory Comput. 2024, 20, 21, 9500–9511	Github

Usage

Installation

Recommended environment for internal force fields

python==3.10.12
pip==23.2.1
setuptools==68.1.2
h5py==3.9.0
numpy==1.24.3
addict==2.4.0
tqdm==4.66.1
joblib==1.3.2
pandas==2.1.0
pytorch==2.0.1
scikit-learn==1.3.0
ase==3.23.0
transformers==4.33.1
torch-ema==0.3
pyyaml==6.0.1
torch-scatter==2.1.2
e3nn==0.4.4

To test PhysNet, you also need

tensorflow==2.13.0

To invoke MACE, you need

mace-torch==0.3.6

To invoke NequIP, you need

nequip==0.6.1

To invoke XPaiNN, you need

XequiNet==0.3.6
scipy==1.11.2
pyscf==2.7.0
torch_geometric==2.5.3
pytorch-warmup==0.1.1
pydantic==1.10.12

Then install the package

pip install -e .

Training

Energy (force) / Atomic Charge / Dipole moment fitting.

enerzyme train -c <configuration yaml file> -o <output directory>

Please see enerzyme/config/train.yaml for details and recommended configurations.

Enerzyme saves the preprocessed dataset, split indices, final <configuration yaml file>, and the best/last model to the <output directory>.

Concurrent Training (Active Learning)

Please see enerzyme/config/concurrent_train.yaml for details and recommended configurations.

Evaluation

Energy (force) / Atomic Charge / Dipole moment prediction.

enerzyme predict -c <configuration yaml file> -o <output directory> -m <model directory>

Please see enerzyme/config/predict.yaml for details.

Enerzyme reads the <model directory> for the model configuration, load the models, predict the results from all active models, save the predicted values as a pickle in the corresponding model subfolders, and report the results as a csv file in the <output directory>.

Simulation

Supported simulation types:

• Flexible scanning on the distance between two atoms.
• Constrained Langevin MD

enerzyme simulate -c <configuration yaml file> -o <output directory> -m <model directory>

Enerzyme reads the <model directory> for the model configuration, load the models, do simulation, and report the results in the <output directory>.