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在酸中氧化水的高性能氧化物电催化剂:贝叶斯优化发现和实验测试

Jacques A Esterhuizen^1,2, Aarti Mathur^1,2, Bryan R Goldsmith^1,2

¹Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2136, United States.

Journal of the American Chemical Society

|February 19, 2024

概括

兴奋剂显著提高氧化反应 (OER) 的氧化催化剂,减少能量损失和溶解. 机器学习加速了这些先进的催化材料的发现.

科学领域:

材料科学
电化学
催化剂

背景情况:

氧化 (Ir) 是酸性介质中氧化演化反应 (OER) 的重要但昂贵的催化剂.
开发具有成本效益的替代品或改善IR利用对于高效的OER催化是必不可少的.

研究的目的:

探索基于Ir的混合氧化物的广相空间,以提高OER性能.
确定有前途的兴奋剂,可以提高OER活性和稳定性,同时降低Ir含量.

主要方法:

结合机器学习辅助的贝叶斯优化和密度函数理论 (DFT) 的工作流程用于材料选.
合成并描述了Ir-Mo混合氧化物作为薄膜电催化剂.

主要成果:

确定了 (Mo) 作为耐酸基OER催化剂的有前途的剂.
与纯净的Ir相比,Ir-Mo混合氧化物显示出~30mV的超电位.
在Ir-Mo氧化物中达到比纯Ir低24%的溶解率.

结论:

是一种高度有效的增强基于Ir的OER催化剂.
机器学习加速了新型催化材料的发现和优化.
开发的工作流程展示了人工智能在指导材料科学研究方面的潜力.

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