Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models.

ABSTRACT: We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally efficient physics-aware potentials to achieve quantum-level accuracy for complex interfacial systems. Our approach trains physics-aware potentials, with incorporated flexibility and smoothness, on actively sampled density functional theory (DFT) data to describe interactions between undercoordinated atomic silver (Ag) clusters and gaseous pollutants (CO₂, CO, SO₂), relevant for environmental applications like sensing. The AL process follows three stages: (1) FFs are trained using adaptable physics aware potentials of semiem

SUBMITTER: Patsalidis N 

PROVIDER: S-EPMC12421685 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature