Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly

Abstract

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce <i>IPSuite</i>, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the <i>IPSuite</i> code enables simple model sharing and deployment in simulations. Currently, <i>IPSuite</i> supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of <i>ab initio</i> calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.