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闭环多目标优化用于Cu-Sb-S光电催化材料的发现

Yang Bai¹, Zi Hui Jonathan Khoo^1,2, Riko I Made¹

¹Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore, 138634, Republic of Singapore.

Advanced materials (Deerfield Beach, Fla.)

|September 10, 2023

概括

铜反硫化物对水分裂催化有很大的潜力. 智能工作流优化了它们的组成,实现了进化的2.3倍更高的活动.

科学领域:

材料科学材料科学材料科学
催化剂是一种催化剂.
可再生能源可再生能源是可再生能源.

背景情况:

铜反硫化物是地球上丰富的材料,具有广泛的光吸收能力,这使得它们对光电化学水分有希望.
它们在进化中的催化性能对晶体结构和原子组成高度敏感.

研究的目的:

通过探索其组成空间,优化铜硫化物 (Cu-Sb-S) 的光电催化演变.
开发和实施一个集成的工作流,将高吞吐量实验和机器学习结合起来,以优化催化剂.

主要方法:

采用了结合机器人平台,表征技术和机器学习 (ML) 优化模型的闭环工作流.
多目标优化被用来有效地探索Cu-Sb-S组成空间.
在0V与可逆电极 (RHE) 进行光电测试,以评估催化活性.

主要成果:

综合实验-ML循环仅在9个周期内确定了最佳条件.
在F-doped SnO2 (FTO) 玻璃上的优化Cu-Sb-S成分 (9:45:46比) 实现了 -186 μA cm-2.2的光电流.
这种最佳材料具有1.85 eV的带隙和63.2%的Cu1+/Cu物种含量.
针对性搜索产生了一个非明显的CuSbS成分,其活性比随机抽样高2.3倍.

结论:

智能,闭环工作流可以快速优化复杂的材料系统用于催化.
优化的铜硫化物显示出对进化的显著增强的光电催化活性.
这种方法加速了对可持续能源应用的高效和地球丰富的催化剂的发现.

关键词:

高通量实验高通量实验机器学习是机器学习.光电化学水分离器的使用

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