Machine-learned potentials: current status and perspectives

 

Recently, machine-learning (ML) approaches to developing interatomic potentials are attracting considerable attention because it is poised to overcome the major shortcoming inherent to the classical potential and density functional theory (DFT), i.e., difficulty in potential development and huge computational cost, respectively. In this presentation, based on in-house program SIMPLE-NN [1] for training and using neural network potentials (NNPs), we present our recent results on various material simulations: highly-efficient crystal structure prediction [2], accelerated computation of thermal conductivities [3], emission spectrum of quantum dots [4], electrocatalysts in fuel-cells, and simulation of semiconductor processing such as etching and atomic-layer deposition. We also discuss about a new avenue of material modeling that opens up by combining the machine learned potentials and various statistical approaches.

 

References

1. K. Lee et al, Comp. Phys. Comm. 242, 95 (2019).

2. S. Kang, W. Jeong et al, npj Computational Materials 8, 108 (2022).

3. J. M. Choi, K. Lee et al, Computational Materials Science 211, 111472 (2022).

 

 

Zoom 정보

https://korea-ac-kr.zoom.us/j/97479212962?pwd=eXhNK29UWlRjMjUyY0s2Z2xTOGFPZz09

 

회의 ID: 974 7921 2962

 

암호: 8a5X0j7A0e

 

 

20221013_대학원세미나_한승우 교수.pdf