Modular and Scalable Solutions for Machine Learned Models for Material Science and Beyond

Model development that utilizes machine learning must define feature sets, model forms, and ultimately if said models are efficient to use for the desired accuracy.

Specialized software to develop machine learned interatomic potentials utilized in MD is highlighted herein. The FitSNAP code has evolved quickly in the last few years to accept numerous descriptor sets, model forms (and associated regression techniques), all while ensuring portability into LAMMPS for efficient use. This talk will overview the user friendly FitSNAP code and its integration into the Exascale Computing Project EXAALT software stack with a focus on the challenges and advances made to tackle exascale sized training sets needed to construct robust and truly transferable interatomic potentials. Additionally, a span of applications will be highlighted that motivate a paradigm shift in how training data is curated for machine learned models. A pair of new methods for training set generation that is applicable beyond interatomic potentials will be demonstrated to this end.