2024年11月13日NIMSにて、 Dr. Stephan Petersen (GTT-Technologies）によるご講演が第163回構材ゼミとして開催されました。
日時：2024年11月13日（水曜日）13:30-15:00
場所：NIMS 千現地区 先進構造材料研究棟5階カンファレンスルーム
講演題目：Thermodynamic genome for multicomponent materials: CALPHAD, ab initio, and machine learning
講演者：Dr. Stephan Petersen(GTT-Technologies, ManagingDirector) https://gtt-technologies.de/
開催者：大出 真知子（組織熱力学グループ）
講演後の集合写真
Dr. Stephan Petersen（前列右から3番目）
Abstract:
CALPHAD databases are the state-of-the-art for thermodynamic modelling of inorganic materials (metals, ceramics, slags, salts). However, the CALPHAD methodology is still a very manual process and requires the existence and human evaluation of large amounts of experimental data. Commercial CALPHAD databases therefore cover “only” few tens of elements with a focus on selected applications (such as steels, metallurgy or non-oxide ceramics). Ab initio databases on the other hand, such as materialsproject.org or oqmd.org, are not limited by the existence of experimental data and therefore cover larger parts of chemical space. The use of these databases for thermodynamic modelling is however restricted by both the temperature range (usually 0K), limited accuracy with respect to phase equilibria, and the difficulty in describing solution phase thermodynamics. In this presentation we demonstrate that machine learning techniques can be used to bridge the gap between CALPHAD and ab initio databases. This is especially important when modeling the processing of new functional materials or the recovery of minority elements during the recycling of complex end-of-life products.