Abstract
Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks.
| Original language | English |
|---|---|
| Pages (from-to) | 4367-4380 |
| Number of pages | 14 |
| Journal | Journal of Chemical Information and Modeling |
| Volume | 65 |
| Issue number | 9 |
| DOIs | |
| State | Published - May 12 2025 |
Funding
The authors thank Benjamin Nebgen, Justin S. Smith, Aidan Thompson, and Mitchell Wood for helpful discussions. S.Z., M.C., R.A.M., and N.L. acknowledge support from the US Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security, LLC (“Triad”) contract grant 89233218CNA000001 (FWP: LANLE3F2). S.Z. gratefully acknowledges the resources of the Los Alamos National Laboratory (LANL) Computational Science summer student program. The work at LANL was supported by the LANL Laboratory Directed Research and Development (LDRD) Projects 20230290ER and 20230435ECR. Work at LANL was performed in part at the Center for Nonlinear Studies and the Center for Integrated Nanotechnologies, a US Department of Energy Office of Science user facility at LANL. This research used resources provided by the LANL Institutional Computing Program. This research used resources provided by the Darwin testbed at LANL, which is funded by the Computational Systems and Software Environments subprogram of LANL’s Advanced Simulation and Computing program (NNSA/DOE). S.Z. and O.I. acknowledge support from the Office of Naval Research through the Energetic Materials Program (MURI grant number N00014-21-1-2476). This work used supercomputing resources through allocation CHE200122 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by NSF grants #2138259, #2138286, #2138307, #2137603, and #2138296. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725.