Machine learning of accurate energy-conserving molecular force fields
Abstract
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of...
Paper Details
Title
Machine learning of accurate energy-conserving molecular force fields
Published Date
May 5, 2017
Journal
Volume
3
Issue
5
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