Global structure search of two-dimensional materials based on evolutionary algorithms and gaussian process regression

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K03419
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 13020:Semiconductors, optical properties of condensed matter and atomic physics-related
• Research Institution: Osaka University
• Principal Investigator: Hamamoto Yuji 大阪大学, 大学院工学研究科, 助教 (30584734)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 第一原理計算 / 機械学習 / 二次元物質 / 不均一触媒

Outline of Final Research Achievements

We performed machine-learned searches for the nontrivial structures of two-dimensional materials and solid surfaces that are difficult to determine experimentally. We first determined the stable structures of silicene on the Ag(111) surface, clarifying the origin of less-ordered phases that have been unidentified so far. We next determined the stable structures of Pd clusters on the Sr3Ti2O7 surfaces, demonstrating that Pd oxide clusters are superior automotive exhaust gas catalysts that are resistant to the solid solution to the substrate as well as sintering. We also determined the adsorption structures of Pt single atoms on the edges of graphene nanoribbons, discovering more stable adsorption structures under the condition of oxygen reduction reactions than those reported in prior studies.

Free Research Field

物性理論

Academic Significance and Societal Importance of the Research Achievements

本研究では、第一原理計算と機械学習に基づく構造探索手法を組み合わせることで、多様かつ非自明な構造を持つ二次元物質の安定構造を、実験結果からの推測を仮定することなく効率的に決定することができることを実証した。本研究で扱った二次元物質は、その特異な構造的・電子的特性により電子デバイスや触媒担体など様々な産業応用が期待されてきたが、未だほとんど実現していない。本研究の成果や、同様の手法で得られる二次元物質の安定構造の情報を活用することで、より効率的な電子デバイスや不均一触媒などの開発が可能になると期待できる。