Weighted active space protocol for multireference machine-learned potentials

• 成果类型: Article
• 署名作者: Seal, Aniruddha; Perego, Simone; Hennefarth, Matthew R.; Raucci, Umberto; Bonati, Luigi; Ferguson, Andrew L.; Parrinello, Michele; Gagliardi, Laura
• 署名单位: University of Chicago; University of Chicago; Istituto Italiano di Tecnologia - IIT; University of Chicago
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9593
• DOI: 10.1073/pnas.2513693122
• 发表日期: 2025-09-23
• 关键词: density functional theory; saddle-points; valence; DYNAMICS; SURFACES; library

摘要

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally 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 demonstrated the approach 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 paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.