Towards Explainable, Scalable, and Accurate Machine-Learned Interatomic Potentials
Project description
Carcará
🚧 (Under development) 🚧
Towards Explainable, Scalable, and Accurate Machine-Learned Interatomic Potentials
Installation
From Pip
The easiest way to install Carcará is with pip:
pip install carcara
Getting started
Training
model: "MPNN"
name: "my_model"
training_dataset: "training.xyz"
validation_dataset: "validation.xyz"
test_dataset: "test.xyz"
cutoff_radius: 6.0
num_channels: 64
l_max: 1
mp_layers: 2
manybody_correlation: 3
energy_key: "REF_energy"
forces_key: "REF_forces"
stress_key: "REF_stress"
energy_weight: 10
forces_weight: 1000
stress_weight: 1000
seed: 42
device: cpu
Evaluation
# TODO
License
This is an open source code under MIT License.
Acknowledgements
We thank financial support from FAPESP (Grant No. 2022/14549-3), INCT Materials Informatics (Grant No. 406447/2022-5), and CNPq (Grant No. 311324/2020-7).
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