|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Interatomic Potentials as Physically-informed Artificial Neural Networks
||James Hickman, Ganga P. Purja Pun, Vesselin I Yamakov, Yuri Mishin
|On-Site Speaker (Planned)
We present a new approach to the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. We demonstrate a number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials. Some of the specific materials systems include silicon, aluminum, as well as alloys and compounds. Computations aspects of PINN potentials are discussed and future developments in this field are outlined.
||Planned: Supplemental Proceedings volume