Rapid FF Generation via Hessian-Informed Initial Parameters and Automated Refinement

The FUERZA method and its modifications have greatly improved the generation of force field parameters from electronic structure calculations. The QFUERZA method presented here addresses some of the shortcomings of the original method and places it in a comprehensive workflow for the further optimization of the derived force constants within the Q2MM workflow. QFUERZA demonstrated improved accuracy when applied to the generation and full optimization of a ground-state force field for <i>cis</i>-platinum and a transition-state force field for the stereoselecting step of the rhodium-catalyzed hydrogenation of enamides, which combines literature MM3* values with new atom types for the reactive center. QFUERZA outperforms three similarly simple approaches to the derivation of force constants from the literature. Application of the gradient optimizer in the Q2MM workflow on the four different preliminary force fields also demonstrates faster convergence to parameters with excellent agreement with reference data when QFUERZA is used as the starting point. Comparison of QFUERZA-derived parameters for transition-state force fields can identify parameters closely associated with the chemical transition, automating parameter selection for the final stage of transition-state parameter refinement.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-12-30
DOI
https://doi.org/10.1021/acs.jctc.5c01751
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Rapid FF Generation via Hessian-Informed Initial Parameters and Automated Refinement

Norrby Per-Ola, Paul Helquist, Olaf Wiest, Mikaela Farrugia
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

Rapid FF Generation via Hessian-Informed Initial Parameters and Automated Refinement

Norrby Per-Ola, Paul Helquist, Olaf Wiest, Mikaela Farrugia
article en

Abstract

The FUERZA method and its modifications have greatly improved the generation of force field parameters from electronic structure calculations. The QFUERZA method presented here addresses some of the shortcomings of the original method and places it in a comprehensive workflow for the further optimization of the derived force constants within the Q2MM workflow. QFUERZA demonstrated improved accuracy when applied to the generation and full optimization of a ground-state force field for <i>cis</i>-platinum and a transition-state force field for the stereoselecting step of the rhodium-catalyzed hydrogenation of enamides, which combines literature MM3* values with new atom types for the reactive center. QFUERZA outperforms three similarly simple approaches to the derivation of force constants from the literature. Application of the gradient optimizer in the Q2MM workflow on the four different preliminary force fields also demonstrates faster convergence to parameters with excellent agreement with reference data when QFUERZA is used as the starting point. Comparison of QFUERZA-derived parameters for transition-state force fields can identify parameters closely associated with the chemical transition, automating parameter selection for the final stage of transition-state parameter refinement.

Journal of Chemical Theory and ComputationVol. 22(1)
University of Notre Dame (US), AstraZeneca (Australia) (AU)
Openalex Percentile: Top 47%
Machine Learning in Materials Science
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Rapid FF Generation via Hessian-Informed Initial Parameters and Automated Refinement — Norrby Per-Ola, Paul Helquist, et al. · Journal of Chemical Theory and Computation (2025) | TGRS Research Map | TGRS