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  3. Assessing Zero-shot Generalisation Behaviour In Graph-neural-network Interatomic Potentials.
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  3. Assessing Zero-shot Generalisation Behaviour In Graph-neural-network Interatomic Potentials.

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

Chiheb Ben Mahmoud1, Zakariya El-Machachi1, Krystian A Gierczak1

  • 1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford Oxford OX1 3QR UK chiheb.benmahmoud@chem.ox.ac.uk.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study evaluates the transferability of a machine-learned interatomic potential (MLIP) model, GO-MACE-23, from graphene oxide to molecular chemistry. Results show limitations in zero-shot generalization for molecules and reactions, informing future MLIP development.

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Area of Science:

  • Materials Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Machine-learned interatomic potentials (MLIPs) are increasingly prevalent in chemistry research.
  • Developing generally applicable, foundational MLIPs is a key research focus.
  • Assessing the transferability of MLIPs across different chemical domains is crucial.

Purpose of the Study:

  • To evaluate the zero-shot transferability of the GO-MACE-23 MLIP model.
  • To quantify the model's performance on small molecules and chemical reactions outside its training scope.
  • To provide insights into the generalization capabilities of graph-based MLIP models.

Main Methods:

  • Utilized the GO-MACE-23 model, originally designed for graphene oxide.
  • Tested the model's performance on isolated small molecules.
  • Assessed the model's applicability to chemical reaction simulations.
  • Quantified zero-shot performance metrics.

    • Main Results:

    • The GO-MACE-23 model demonstrated limited zero-shot generalization for small molecules.
    • Performance on chemical reactions also indicated scope limitations.
    • Quantitative data on generalization ability was obtained.

    Conclusions:

    • Graph-based MLIPs like GO-MACE-23 have constraints in transferring knowledge to new chemical domains.
    • The study highlights the need for careful consideration of model scope and limitations.
    • Findings can guide the development of more robust and broadly applicable future MLIPs.