Crash testing; Density-functional-theory; Field architectures; Force field models; Forcefields; Machine-learning; Molecule surface; Performance; Surface interfaces; Testing machine; Chemistry (all)
Abstract :
[en] We present the second part of the rigorous evaluation of modern machine learning force fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of the performance of MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19* in modeling molecules, molecule-surface interfaces, and periodic materials. We compare observables obtained from molecular dynamics (MD) simulations using different MLFFs under identical conditions. Where applicable, density-functional theory (DFT) or experiment serves as a reference to reliably assess the performance of the ML models. In the absence of DFT benchmarks, we conduct a comparative analysis based on results from various MLFF architectures. Our findings indicate that, at the current stage of MLFF development, the choice of ML model is in the hands of the practitioner. When a problem falls within the scope of a given MLFF architecture, the resulting simulations exhibit weak dependency on the specific architecture used. Instead, emphasis should be placed on developing complete, reliable, and representative training datasets. Nonetheless, long-range noncovalent interactions remain challenging for all MLFF models, necessitating special caution in simulations of physical systems where such interactions are prominent, such as molecule-surface interfaces. The findings presented here reflect the state of MLFF models as of October 2023.
PULEVA, Mirela ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
CHARKIN-GORBULIN, Anton ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) ; Laboratory for Chemistry of Novel Materials, University of Mons B-7000 Mons Belgium
Fonseca, Grégory; Department of Physics and Materials Science, University of Luxembourg L-1511 Luxembourg Luxembourg alexandre.tkatchenko@uni.lu igor.poltavskyi@uni.lu
Batatia, Ilyes; Department of Engineering, University of Cambridge Trumpington Street Cambridge CB2 1PZ UK
Browning, Nicholas J; Swiss National Supercomputing Centre (CSCS) 6900 Lugano Switzerland
Chmiela, Stefan; Machine Learning Group, Technical University Berlin Berlin Germany ; BIFOLD, Berlin Institute for the Foundations of Learning and Data Berlin Germany
Cui, Mengnan; Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin Germany
Frank, J Thorben ; Machine Learning Group, Technical University Berlin Berlin Germany ; BIFOLD, Berlin Institute for the Foundations of Learning and Data Berlin Germany
Heinen, Stefan; Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
Huang, Bing; Wuhan University, Department of Chemistry and Molecular Sciences 430072 Wuhan China
Käser, Silvan ; Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
KABYLDA, Adil ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Khan, Danish; Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada ; Chemical Physics Theory Group, Department of Chemistry, University of Toronto St. George Campus Toronto ON Canada
Price, Alastair J A; Department of Chemistry, University of Toronto St. George campus Toronto ON Canada ; Acceleration Consortium, University of Toronto 80 St George St Toronto ON M5S 3H6 Canada
Riedmiller, Kai ; Heidelberg Institute for Theoretical Studies Heidelberg Germany
Töpfer, Kai ; Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
Ko, Tsz Wai; Department of NanoEngineering, University of California San Diego 9500 Gilman Dr, Mail Code 0448 La Jolla CA 92093-0448 USA
Meuwly, Markus ; Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
Rupp, Matthias ; Luxembourg Institute of Science and Technology (LIST) L-4362 Esch-sur-Alzette Luxembourg
Csányi, Gábor ; Department of Engineering, University of Cambridge Trumpington Street Cambridge CB2 1PZ UK
Anatole von Lilienfeld, O ; Machine Learning Group, Technical University Berlin Berlin Germany ; BIFOLD, Berlin Institute for the Foundations of Learning and Data Berlin Germany ; Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada ; Department of Chemistry, University of Toronto St. George campus Toronto ON Canada ; Acceleration Consortium, University of Toronto 80 St George St Toronto ON M5S 3H6 Canada ; Department of Materials Science and Engineering, University of Toronto St. George campus Toronto ON Canada ; Department of Physics, University of Toronto, St. George campus Toronto ON Canada
Margraf, Johannes T; University of Bayreuth, Bavarian Center for Battery Technology (BayBatt) Bayreuth Germany
Müller, Klaus-Robert ; Machine Learning Group, Technical University Berlin Berlin Germany ; BIFOLD, Berlin Institute for the Foundations of Learning and Data Berlin Germany ; University of Bayreuth, Bavarian Center for Battery Technology (BayBatt) Bayreuth Germany ; Department of Artificial Intelligence, Korea University Seoul South Korea ; Max Planck Institut für Informatik Saarbrücken Germany ; Google DeepMind Berlin Germany
TKATCHENKO, Alexandre ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Fonds National de la Recherche Luxembourg H2020 European Research Council Université du Luxembourg Natural Sciences and Engineering Research Council of Canada University of Toronto Bundesministerium für Bildung und Forschung Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung Alexander von Humboldt-Stiftung Ministry of Science and ICT, South Korea Korea University Klaus Tschira Stiftung
Funding text :
The simulations were performed on the Luxembourg national supercomputer MeluXina. The authors gratefully acknowledge the LuxProvide teams for their expert support. I. Poltavsky would like to acknowledge the financial support afforded by the Luxembourg National Research (FNR) (Grant C19/MS/13718694/QML-FLEX). A. Tkatchenko acknowledges support from the European Research Council (ERC-AdG grant FITMOL) and Luxembourg National Research Fund (FNR-CORE Grant MBD-in-BMD). M. Puleva acknowledges the financial support from Institute of Advanced Studies, University of Luxembourg under the Young Academics project AQMA. The work of A. Charkin-Gorbulin was performed with the support of the Belgian National Fund for Scientific Research (F.R.S.-FNRS). Computational resources were provided by the Consortium des \u00C9quipements de Calcul Intensif (C\u00C9CI) funded FNRS under Grant 2.5020.11. We thank J. Weinreich for the helpful discussions. We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), RGPIN-2023-04853. O. A. von Lilienfeld has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 772834). This research was undertaken thanks in part to funding provided to the University of Toronto's Acceleration Consortium from the Canada First Research Excellence Fund, grant number: CFREF-2022-00042. O.A. von Lilienfeld has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. I. Batatia acknowledges access to CSD3 GPU resources through a University of Cambridge EPSRC Core Equipment Award (EP/X034712/1). IB was supported by the Harding Distinguished Postgraduate Scholarship. S. Chmiela and J.T. Frank acknowledge support by the German Ministry of Education and Research (BMBF) for BIFOLD (01IS18037A). M. Cui acknowledges the Max Planck Computing and Data Facility (MPCDF) for computation time. J.T. Margraf acknowledges support by the Bavarian Center for Battery Technology (BayBatt) at the University of Bayreuth. The research at Uni Basel was financially supported (to MM) by the Swiss National Science Foundation through grants 200020_219779 and 200021_215088, which is gratefully acknowledged. A. Kabylda acknowledges financial support from the Luxembourg National Research Fund (FNR) (AFR PhD Grant 15720828). C. M\u00FCller acknowledges funding by a Feodor-Lynen fellowship of the Alexander von Humboldt foundation. K.-R. M\u00FCller was in part supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D, and 01IS18037A and by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation). K. Riedmiller acknowledges financial support from the Klaus Tschira Foundation. We thank O. Unke and C. Quarti for their helpful comments.
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