Bulletin of the American Physical Society
APS March Meeting 2023
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N60: Emerging Trends in Molecular Dynamics Simulations and Machine Learning IV
11:30 AM–12:54 PM,
Wednesday, March 8, 2023
Room: Room 419
Sponsoring Units: DCOMP DSOFT GDS DPOLY
Chair: Cheng Chin, University of Chicago
Abstract: N60.00001 : Refinement of Training Schemes for Machine-Learning Interatomic Potentials and Its Applications*
11:30 AM–12:06 PM
Refining the training schemes of MLIPs was one key to this success. In particular, we focused on the design of the cost function. By adding not only the potential energy term but also atomic force and pressure terms to the cost function, the accuracy of the MLIP can be significantly improved. However, we need to understand the importance of adjusting the coefficients of terms to compensate the differences in unit and the data size among them. We have also found that adding a regularization term to the cost function to constrain the latent arbitrariness of the atomic components, such as atomic potential energy of MLIPs, can significantly improve the accuracy of the thermal conductivity. It plays a role in suppressing the extra heat flux generated by the arbitrariness.
In the talk, I will discuss the recent progress in the development of MLIP training scheme and its applications.
These works were carried out in collaboration with K. Nomura, R. K. Kalia, A. Nakano, P. Vashishta, A. Koura, and F. Shimojo.
*This study was supported by JST CREST Grant Number JPMJCR18I2 and JSPS KAKENHI Grant Number 22K03454, Japan. The authors thank the Super-computer Center, the Institute for Solid State Physics, University of Tokyo for the use of the facilities.
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