Weighted Active Space Protocol for Multireference Machine-Learned Potentials
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918147727032320 |
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| author | Seal, Aniruddha Perego, Simone Hennefarth, Matthew R. Raucci, Umberto Bonati, Luigi Ferguson, Andrew L. Parrinello, Michele Gagliardi, Laura |
| author_facet | Seal, Aniruddha Perego, Simone Hennefarth, Matthew R. Raucci, Umberto Bonati, Luigi Ferguson, Andrew L. Parrinello, Michele Gagliardi, Laura |
| contents | Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations to the underlying active space, which complicates achieving consistent energies and gradients across diverse nuclear configurations. To overcome this limitation, we introduce the Weighted Active-Space Protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrate the method on the TiC+-catalyzed C-H activation of methane, a reaction that poses challenges for Kohn-Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a new paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weighted Active Space Protocol for Multireference Machine-Learned Potentials Seal, Aniruddha Perego, Simone Hennefarth, Matthew R. Raucci, Umberto Bonati, Luigi Ferguson, Andrew L. Parrinello, Michele Gagliardi, Laura Chemical Physics Statistical Mechanics Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations to the underlying active space, which complicates achieving consistent energies and gradients across diverse nuclear configurations. To overcome this limitation, we introduce the Weighted Active-Space Protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrate the method on the TiC+-catalyzed C-H activation of methane, a reaction that poses challenges for Kohn-Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a new paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods. |
| title | Weighted Active Space Protocol for Multireference Machine-Learned Potentials |
| topic | Chemical Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2505.10505 |