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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 Million-scale Surface Property Screening Enabled by Universal Machine Learning Interatomic Potentials
Author(s) Peter Schindler, Ardavan Mehdizadeh
On-Site Speaker (Planned) Peter Schindler
Abstract Scope Universal machine learning interatomic potentials (uMLIPs) have recently enabled materials screening at a scale previously inaccessible to first-principles methods. Surface properties govern many critical materials aspects such as catalytic facet activity and corrosion stability, yet systematic periodic table coverage remains infeasible using density functional theory (DFT) alone. uMLIPs can bridge this gap, but large-scale datasets of fully relaxed surface arrangements remain scarce. Here, building on our earlier benchmark study of 19 uMLIPs, we present a dataset of over 1 million relaxed surface slabs spanning 21,000 bulk materials from the Materials Project. We survey surface relaxation behavior across the periodic table and screen work function across equilibrium exposed facets using FIRE-GNN (a force-informed graph neural network using MLIP-derived forces). Surfaces at the work function extremes serve as physically motivated DFT validation targets, forming a pipeline from uMLIP relaxation to GNN-based work function inference to ab initio confirmation.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation, Energy Conversion and Storage

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