Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials

Chiheb Ben Mahmoud

doi:10.1039/D5DD00103J

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials

2025-09-30

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DOI：10.1039/D5DD00103J

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With the rapidly growing availability of machine-learned interatomic potential (MLIP) models for chemistry; much current research focuses on the development of generally applicable and 'foundational' MLIPs. An important question in this context is whether; and how well; such models can transfer from one application domain to another. Here; we assess this transferability for an MLIP model at the interface of materials and molecular chemistry. Specifically; we study GO-MACE-23; a model designed for the extended covalent network of graphene oxide; and quantify its zero-shot performance for small; isolated molecules and chemical reactions outside its direct scope—in direct comparison with a state-of-the-art model which has been trained in-domain. Our work provides quantitative insight into the transfer and generalisation ability of graph-neural-network potentials and; more generally; makes a step towards the more widespread applicability of MLIPs in chemistry.

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials

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