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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title The Surface Problem in Machine-Learned Interatomic Potentials
Author(s) Emir Bilgili, Nicholas Taormina
On-Site Speaker (Planned) Emir Bilgili
Abstract Scope Machine-learned interatomic potentials (MLIPs) have revolutionized atomistic simulations, especially for bulk and molecular systems. Despite their importance, surfaces have received comparatively little attention in the MLIP field. In this work, we report an unrecognized failure mode in MLIPs: across a wide range of surfaces and MLIP frameworks, force prediction errors increase sharply and consistently as atoms approach the surface, reaching several-fold higher values than in the bulk, with the surface-normal component exhibiting significantly larger errors than in-plane components. Degradation is most pronounced for MLIPs with invariant descriptors, whereas deep message-passing and equivariant architectures, as well as foundational models, show improved, though insufficient, performance. Our results suggest fundamental limitations of current atomic descriptors in representing surface environments and show that conventional validation approaches struggle to describe the performance of MLIPs for surfaces. We discuss the theoretical and algorithmic origins of the observed limitations and motivate the development of next-generation, surface-aware descriptors.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Thin Films and Interfaces, Modeling and Simulation

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