日時：2026年1月15日（木）　16：00～17：00

場所：長野（工学）キャンパス　E2棟（AICS)　4階コミュニケーションルーム

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講演：笠松 秀輔 准教授（山形大学理学部）

「First-principles statistical thermodynamics for complex oxides accelerated by machine learning」

概要：First-principles simulation can be used to predict many properties of materials if the atomistic structure is well known, but this is rarely the case in complex oxides with varying degrees of order and disorder in element, charge, and spin configurations. In this talk, I will introduce our Python framework abICS (ab Initio Configuration Sampling), which combines first-principles calculations, machine learning, and extended ensemble sampling to characterize order and disorder in complex materials. The use of universal machine-learning interatomic potentials for this task will also be discussed.

講演は英語になります。