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How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?

Title data

Stocker, Sina ; Gasteiger, Johannes ; Becker, Florian ; Günnemann, Stephan ; Margraf, Johannes T.:
How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?
In: Machine Learning: Science and Technology. Vol. 3 (2022) Issue 4 . - 045010.
ISSN 2632-2153
DOI: https://doi.org/10.1088/2632-2153/ac9955

Abstract in another language

Graph neural networks (GNNs) have emerged as a powerful machine learning approach for the prediction of molecular properties. In particular, recently proposed advanced GNN models promise quantum chemical accuracy at a fraction of the computational cost. While the capabilities of such advanced GNNs have been extensively demonstrated on benchmark datasets, there have been few applications in real atomistic simulations. Here, we therefore put the robustness of GNN interatomic potentials to the test, using the recently proposed GemNet architecture as a testbed. Models are trained on the QM7-x database of organic molecules and used to perform extensive molecular dynamics simulations. We find that low test set errors are not sufficient for obtaining stable dynamics and that severe pathologies sometimes only become apparent after hundreds of ps of dynamics. Nonetheless, highly stable and transferable GemNet potentials can be obtained with sufficiently large training sets.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 13:45
Last Modified: 13 Nov 2023 13:45
URI: https://eref.uni-bayreuth.de/id/eprint/87657