高度なAIが原子構造と量子技術を結び付ける（Advanced AI links atomic structure and quantum technology）

2025-09-17 オークリッジ国立研究所

オークリッジ国立研究所（ORNL）の研究チームは、ベイズ深層学習を活用して原子構造と物質特性の関係を効率的に解明する新手法を開発した。従来は大規模領域を走査し分光測定を行う必要があったが、このAIは自律的に重要領域を探索・解析し、時間を大幅に短縮。実証例として、特異な電子特性を持つ磁性半金属「ユウロピウム亜鉛ヒ化物」での原子構造と電子特性の結びつきを明らかにした。この方法は幅広い材料に適用可能で、量子科学やAI応用研究の推進に寄与すると期待されている。

A scanning tunneling microscope and machine learning algorithm autonomously search for atomic structures. This image shows a vacancy defect on europium zinc arsenide. Credit: Ganesh Narasimha/ORNL, U.S. Dept. of Energy

＜関連情報＞

走査トンネル顕微鏡における能動学習によるマルチスケール構造-特性相関の解明 Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Ganesh Narasimha,Dejia Kong,Paras Regmi,Rongying Jin,Zheng Gai,Rama Vasudevan & Maxim Ziatdinov
npj Computational Materials Published:19 June 2025
DOI:https://doi.org/10.1038/s41524-025-01642-1

Abstract

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn₂As₂, a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.