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从多个来源的实验数据中提取准确的描述符,以发现高活性矿OER催化剂

Jingzhou Wang¹, Huachao Xie¹, Yuanqing Wang¹

¹Materials Genome Institute, Shanghai University, Shanghai 200444, China.

Journal of the American Chemical Society

|May 9, 2023

概括

研究人员开发了一种新的方法来发现氧化反应的氧化物催化剂. 这种方法使用多源数据更有效地识别高活性催化剂.

科学领域:

材料科学
催化剂
计算化学

背景情况:

矿氧化物作为氧化演化反应 (OER) 的催化剂具有前景.
由于缺乏有效的发现方法,对OER催化剂探索矿的巨大化学空间具有挑战性.
来自多个实验来源的不一致数据阻碍了准确的描述器开发.

研究的目的:

开发一种新的计算方法,以加速发现OER的氧化物催化剂.
解决多来源实验数据集数据不一致的问题.
为OER确定新的高活性矿催化剂.

主要方法:

在确定独立性选和分散操作员框架内实施符号受限的多任务学习方法.
从各种出版物中整合了13个实验数据集,以获得准确的描述.
开发了一个新的2D描述符 (d_B,n_B) 用于预测催化活性.

主要成果:

新的二维描述符 (d_B,n_B) 显示了高的通用性,预测准确性和大量表面对应性.
发现了数百个未报告的矿候选物,其预测的OER活性超过了基准 (Ba0.5Sr0.5Co0.8Fe0.2O3).
实验验证证了三种高度活性的矿催化剂:SrCo0.6Ni0.4O3,Rb0.1Sr0.9Co0.7Fe0.3O3和Cs0.1Sr0.9Co0.4Fe0.6O3.

结论:

开发的方法有效地处理不一致的多源数据以加速催化剂的发现.
新的二维描述器提供了一个强大的工具来识别高性能矿OER催化剂.
这种以数据为导向的方法在催化领域取得了重大进展,并为更广泛的应用提供了潜力.

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