This folder contains an example of how to finetune a machine learning potential from FAIR Chemistry to perform direct adsorption energy inference or "Initial Structure to Relaxed Energy" (IS2RE) on the provided High-Entropy Alloy dataset. Additionally, it showcases the implementation of the model in the surrogate surface simulation.
To run the contents of this folder, you should follow fairchem's installation instructions to set up a conda environment and subsequently install the fairchem-core package. Next install cheatools from the main folder.
This example uses Lightning Memory-Mapped Databases (LMDBs) as sources for the training, validation and testing of the inference model. To create these run dft2lmdbs.py which reduces the relaxed atomic structures to template structures as described in Clausen et al. J. Phys. Chem. C 2024 and save them to LMDBs.
To avoid training a model from scratch we need a checkpoint file to initilize the pre-trained model. The checkpoint used in this example is the EquiformerV2-31M model trained on the OC20-dataset which can be fetched in the checkpoints folder. After fetching the checkpoint file, you have the option of setting up a wandb profile to monitor the finetuning process (see configs/equiformer_v2_N@8_L@4_M@2_31M.yml. The finetuning is initialized by running the finetune.py wrapper script.
Be mindful that this should be done on a GPU supported machine. For convenience, an already finetuned checkpoint file can also be fetched in the checkpoints folder. Run test.py to obtain a parity plot of the test results.
Finally simulate_surface.py showcases the surrogate surface simulation identical to the one found in surface_simulation but employs the finetuned EquiformerV2 model to infer the adsorption energies of the surface.