Transfer learning using energy-only datasets with MACE (see ChemRxiv)

Legacy implementation of a simple transfer learning approach using MACE. An more extensive and up-to-date version will be released soon.

Usage

Transfer learning can be used to augment the accuracy of a given baseline MACE model to a higher level of theory using a small additional readout layer. To achieve this, you only need a baseline model.pth and a dataset of atomic geometries in XYZ format that is labeled with the energy difference between the target level of theory and the baseline level of theory used to train model.pth. The train script in scripts/run_train.py is similar to the original MACE training script except that it takes two additional arguments:

• base_model: path to your baseline model which will be modified with an additional energy readout. In this example:

  --base_model=model.pth

• delta_MLP: layer sizes for the additional readout. This follows a similar input format as radial_MLP and should be a string representation of a list of layer sizes:

  --delta_MLP="[8, 8]"

Installation

Via pip: pip install git+https://github.com/molmod/transfermace Make sure you are using mace v0.3.0 and ase<3.23.