|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||An Active Learning Approach for the Generation of Force Fields from DFT Calculations
||Nathan Wilson, Yang Yang, Raymundo Arroyave, Xiaofeng Qian
|On-Site Speaker (Planned)
Molecular dynamics(MD) is a powerful tool to explore the transport properties and dynamics of materials, but it is severely limited by the availability of an accurate force field for a given material. Recent advances in machine learning have sped up the development of force fields using first-principles density functional theory(DFT) calculations. However, many of the current approaches require extensive amounts of data to produce accurate force fields, which involves a large number of DFT calculations. Here we present an active learning approach to adaptively select structures that are most informative to the machine learning model. This allows for the generation of accurate force fields using only a small number of DFT calculations, thereby significantly reducing the computational cost. This adaptive learning force field approach can be used to speed up MD studies of complex structural and functional materials.
||Planned: Supplemental Proceedings volume