Transfer learning for atomistic simulations using GNNs and kernel mean embeddings

Published: 21 Sept 2023, Last Modified: 12 Jan 2024NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: GNN, Mean Embedding, Kernels, Atomistic Simulations, OCP, Transfer Learning, Molecular Dynamics, Kernel Ridge Regression, Neural Networks
TL;DR: We show that combining pretrained GNNs on the OCP dataset with kernel mean embeddings transfer to other chemical datasets, laying the groundwork for foundation models in chemistry
Abstract: Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally demanding. To bypass this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by incorporating into the kernel the chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches.
Supplementary Material: pdf
Submission Number: 5035