ArticleThe journal of physical chemistry letters2025
Machine Learning-Enhanced Structure-Based Gaussian Expansion for Efficient Wavepacket Calculations.
Article in The journal of physical chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Correction to "Machine Learning-Enhanced Structure-Based Gaussian Expansion for Efficient Wavepacket Calculations".The journal of physical chemistry letters · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The theoretical treatment of molecular wavepackets remains computationally demanding and becomes increasingly impractical for complex systems with a large number of atoms. To tackle this problem, we previously developed the structure-based Gaussian (SBG) expansion method, where space-fixed Gaussian basis functions for the expansion of wavepackets are placed intensively around reaction pathways connecting equilibrium structures and transition states. In this study, we incorporated two machine learning techniques into the SBG expansion, thereby developing a highly efficient and versatile approach for wavepacket calculations: the principal component analysis for systematic construction of the SBG basis set and the Gaussian process regression for interpolation of potential energy surfaces. To demonstrate the performance of this approach, we constructed full-dimensional nuclear wave functions for the umbrella inversion tunneling in H
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