ColabFit: Collaborative Development of Data-Driven Interatomic Potentials

Atomic interactions in classical molecular simulations are modeled using a function called an interatomic potential (IP). Traditionally, IPs have used functional forms that explicitly represent aspects of the bonding and/or geometry of the system and are fitted to small datasets of key material properties. Recently, interest has grown in data-driven IPs (DDIPs) which use machine learning methods to interpolate first principles calculations. Due to the lack of explicit physics, DDIPs must be trained on large datasets and frequently retrained when applications fall outside the original dataset. To facilitate this and allow research groups to easily exchange DDIPs and their training datasets, it is important to develop a standard for archiving and retrieving datasets. The ColabFit project (https://colabfit.openkim.org/) aims to address this need by enabling the development, exchange, and deployment of DDIPs and their datasets through the OpenKIM framework (https://openkim.org/). In this work we outline a standard for distributing DDIP training sets and showcase the functionality of the ColabFit tools and data repository for providing open access to a large collection of high quality training data.