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Weighted active space protocol for multireference machine-learned potentials

Journal Article · · Proceedings of the National Academy of Sciences of the United States of America
 [1];  [2];  [1];  [2];  [2];  [1];  [2];  [1]
  1. University of Chicago, IL (United States)
  2. Italian Institute of Technology, Genova (Italy)
+ Show Author Affiliations
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.
Research Organization:
University of Chicago, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); NSF Graduate Research Fellowship
Grant/Contract Number:
SC0023383
OSTI ID:
3005508
Journal Information:
Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 38 Vol. 122; ISSN 1091-6490; ISSN 0027-8424
Publisher:
National Academy of SciencesCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

enhanced sampling
machine-learned potentials
molecular dynamics
strong correlation