Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale.
@article{arxiv.2503.10538,
title = {Foundation Models for Atomistic Simulation of Chemistry and Materials},
author = {Eric C. -Y. Yuan and Yunsheng Liu and Junmin Chen and Peichen Zhong and Sanjeev Raja and Tobias Kreiman and Santiago Vargas and Wenbin Xu and Martin Head-Gordon and Chao Yang and Samuel M. Blau and Bingqing Cheng and Aditi Krishnapriyan and Teresa Head-Gordon},
journal= {arXiv preprint arXiv:2503.10538},
year = {2025}
}