ISSP - The institute for Solid State Physics

Seminar
文字のサイズ: (小) / (中) / (大)
[理論セミナー]
Automatic design of functional molecules and materials
日程 : 2017年7月25日(火) 16:00 - 17:00 場所 : 物性研究所本館6階 第5セミナー室 (A615) 講師 : Prof. Koji Tsuda 所属 : Graduate School of Frontier Sciences, University of Tokyo 世話人 : Naoki KAWASHIMA (63260)
e-mail: kawashima@issp.u-tokyo.ac.jp

The scientific process of discovering new knowledge is often characterized as search from a space of candidates, and machine learning can accelerate the search by properly modeling the data and suggesting which candidates to apply experiments on. In many cases, experiments can be substituted by first principles calculation. I review two basic machine learning techniques called Bayesian optimization and Monte Carlo tree search. I also show successful case studies including Si-Ge nanostructure design, optimization of grain boundary structures and discovery of low-thermal-conductivity compounds from a database.

Reference:
S. Ju, T. Shiga, L. Feng, Z. Hou, K. Tsuda, and J. Shiomi, Designing Nanostructures for Phonon Transport via Bayesian Optimization, Physical Review X, 7, 021024, 2017.

S. Kiyohara, H. Oda, K. Tsuda and T. Mizoguchi, Acceleration of stable interface structure searching using a kriging approach, Japanese Journal of Applied Physics, 55, 045502, 2016.

A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput, and I. Tanaka, Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization, Physical Review Letters, 115, 205901, 2015.


(公開日: 2017年07月10日)