[16p-A31-1~20] 17.1 Carbon nanotubes & other nanocarbon materials

4:30 PM - 4:45 PM JST(7:30 AM - 7:45 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[16p-A31-11] Exploration of New Superhard Carbon Allotropes by a Bond-Order-Based Machine-Learning Interatomic Potential

〇(D)Ikuma Kohata¹, Kaoru Hisama², Keigo Otsuka¹, Shigeo Maruyama¹ (1.The Univ. of Tokyo, 2.Shinshu Univ.)

Keywords:

Machine-learning interatomic potential,Superhard carbon allotropes

Many of carbon allotropes have unique physical properties, and research to discover new allotropes has been active. In this study, we developed a machine-learning interatomic potential that can exhaustively and rapidly compute the energies of atomic structures from a small number of training data and parameters by incorporating physical constraints based on the bond order into the machine-learning model. Using this interatomic potential, we discovered unknown superhard allotropes by the random structure search.