Inverse design of bespoke interatomic potentials via active learning by information-matching
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness. Active learning (AL) provides a principled framework for constructing efficient and accurate IPs, yet most strategies reduce parameter uncertainty without explicitly accounting for the specific material properties being predicted. The information-matching (IM) approach addresses this limitation by requiring that the selected training data provide at least as much parameter space information as needed to achieve prescribed uncertainty target...
| Authors | Yonatan Kurniawan, Logan D. Williams, Amit Samanta et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 6 June 2026 |
| Last updated at source | 29 July 2026 |
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