• AuthorHyun Woo KIM,Hyunju CHANG,Jino IM,Seok Ki KIM,Yong Tae KIM,고태욱,이승희,이종걸,현윤경
• JournalJournal of the Korean Physical Society (0374-4884), 77, 680 ~ 688
• Enrollment typeSCIE
• publication date 20201001

Molecular dynamics (MD) simulations are useful in understanding the interaction between solid materials and molecules. However, performing MD simulations is possible only when interatomic potentials are available and constructing such interatomic potentials usually requires additional computational work. Recently, generating interatomic potentials was shown to be much easier when machine learning (ML) algorithms were used. In addition, ML algorithms require new deors for improved performance. Here, we present an ML approach with several categories of atomic deors to predict the parameters necessary for MD simulations, such as the potential energies and the atomic forces.