An On-the-fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems

18 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The paper describes a modification to the generalized energy-based fragmentation (GEBF) method that uses a machine fitted potential energy surface for the subsytems instead of ab initio calculation, in order to speed up the calculations. An on-the-fly active learning is used to construct vaious kind of subsystems force field automatically. Our method can bpyss over 99% of the QM calculations during the ab inito molecular dynamics.


machine learning
force field


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