Abstract
Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum-mechanical (QM) database while constructing MLFFs with increasing model complexity. Our MLFFs employ the moment tensor potential formalism. During this process, we quantitatively assessed the structural properties, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, free surface energies, and generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs. Unsurprisingly, the model complexity has a positive correlation with prediction accuracy. We also find that the MLFFs were able to predict the properties of out-of-sample configurations without directly including these specific configurations in the training dataset. Additionally, we generated 100 MLFFs of high complexity (1513 parameters each) that reached different local optima during training. Their predictions cluster around the benchmark DFT values, but subtle physical features such as the location of local minima on the GSF energy surface are washed out by statistical noise.
Original language | English |
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Pages (from-to) | 6848-6856 |
Number of pages | 9 |
Journal | Journal of Chemical Theory and Computation |
Volume | 19 |
Issue number | 19 |
DOIs | |
State | Published - Oct 10 2023 |
Funding
The authors thank the Digital Research Alliance of Canada (formerly known as Compute Canada) for the generous allocation of computing resources. The research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the NSERC/UNENE Industrial Research Chair in Nuclear Materials at Queen’s. G.L.W.H. and J.A.M. thank the Advanced Materials Simulation Engineering Tool (AMSET) Project, sponsored by the U.S. Naval Nuclear Laboratory (NNL) and directed by Materials Design, Inc. for financial support. G.D.S. was supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Powertrain Materials Core Program.