Exploration of the Application of Machine Learning to the Improvement of Interatomic Potentials
Year: 2022
Type: article
Abstract: Current methods for atomistic simulations of material systems suffer from limitations which restrict the ability of the simulation to correctly characterize certain material behavior and physical phenomena. Small scale ab initio (AIMD) modeling is highly accurate but is computationally expensive. Classical molecular dynamics (CMD) simulations use interatomic potentials (IPs) to describe larger systems at a reduced computational cost, but with a reduced accuracy. Creating more robust IPs enable more precise CMD simulations. In this thesis, the application of a specific machine learning process,artifical neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.
Author: O'Connor, Sean
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funder: U.S. Department of Energy
Award: G4017088194
OpenAlex ID: W4282962371