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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16425 |
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| _version_ | 1866916912147988480 |
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| author | Cărare, Vlad Thiemann, Fabian L. Morrow, Joe Wales, David J. Pyzer-Knapp, Edward O. Dicks, Luke |
| author_facet | Cărare, Vlad Thiemann, Fabian L. Morrow, Joe Wales, David J. Pyzer-Knapp, Edward O. Dicks, Luke |
| contents | Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction rate calculations -- remains largely unexplored. We introduce Landscape17, a dataset of complete kinetic transition networks (KTNs) for the molecules of the MD17 dataset, computed using hybrid-level density functional theory. Each KTN contains minima, transition states, and approximate steepest-descent paths, along with energies, forces, and Hessian eigenspectra at stationary points. We develop a comprehensive test suite to evaluate the MLIP ability to reproduce these reference landscapes and apply it to a number of state-of-the-art architectures. Our results reveal limitations in current MLIPs: all the models considered miss over half of the DFT transition states and generate stable unphysical structures throughout the potential energy surface. Data augmentation with pathway configurations improves reproduction of DFT potential energy surfaces, resulting in significant improvement in the global kinetics. However, these models still produce many spurious stable structures, indicating that current MLIP architectures face underlying challenges in capturing the topology of molecular potential energy surfaces. The Landscape17 benchmark provides a straightforward but demanding test of MLIPs for kinetic applications, requiring only up to a few hours of compute time. We propose this test for validation of next-generation MLIPs targeting reaction discovery and rate prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global properties of the energy landscape: a testing and training arena for machine learned potentials Cărare, Vlad Thiemann, Fabian L. Morrow, Joe Wales, David J. Pyzer-Knapp, Edward O. Dicks, Luke Chemical Physics Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction rate calculations -- remains largely unexplored. We introduce Landscape17, a dataset of complete kinetic transition networks (KTNs) for the molecules of the MD17 dataset, computed using hybrid-level density functional theory. Each KTN contains minima, transition states, and approximate steepest-descent paths, along with energies, forces, and Hessian eigenspectra at stationary points. We develop a comprehensive test suite to evaluate the MLIP ability to reproduce these reference landscapes and apply it to a number of state-of-the-art architectures. Our results reveal limitations in current MLIPs: all the models considered miss over half of the DFT transition states and generate stable unphysical structures throughout the potential energy surface. Data augmentation with pathway configurations improves reproduction of DFT potential energy surfaces, resulting in significant improvement in the global kinetics. However, these models still produce many spurious stable structures, indicating that current MLIP architectures face underlying challenges in capturing the topology of molecular potential energy surfaces. The Landscape17 benchmark provides a straightforward but demanding test of MLIPs for kinetic applications, requiring only up to a few hours of compute time. We propose this test for validation of next-generation MLIPs targeting reaction discovery and rate prediction. |
| title | Global properties of the energy landscape: a testing and training arena for machine learned potentials |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2508.16425 |