Auerbachらの研究チームは、人工知能を使って、炭素捕捉に有望な材料の合成可能性をランク付けしています。(Auerbach and Team Use Artificial Intelligence to Rank The Synthesizability Of Materials That Show Promise for Carbon Capture)


2022-10-27 マサチューセッツ大学アマースト校

国際研究チームが、材料科学における長年の課題である、コンピュータで作成した膨大なデータベースの中から実際に製造するのに適した構造を特定するために人工知能(AI)を応用した研究を「Digital Discovery」誌に発表した。


ソーティングハットを用いた仮想ゼオライトの合成可能性のランク付け Ranking the synthesizability of hypothetical zeolites with the sorting hat

Benjamin A. Helfrecht,  Giovanni Pireddu, Rocio Semino,  Scott M. Auerbach  and  Michele Ceriotti
Digital Discovery  Published:12 Oct 2022


Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “Zeolite Sorting Hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si–O distances around 3.2–3.4 Å as the key discriminatory factor.