Force field parameterization of azobenzene derivatives based on machine learning potential energy surface prediction and high-throughput fitting

Description

All data and code supporting the findings of this study, including the QM datasets for ten AZDs, the GPR-interpolated PESs, the GBT/RF models for PES extrapolation, and the tabulated potentials for force field implementation

Institutions

• Beijing Jiaotong University

  Beijing, Beijing

Categories

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