Machine Learning Accelerated Exploration of Ternary Organic Heterojunction Photocatalysts for Sacrificial Hydrogen Evolution,Journal of the American Chemical Society

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Machine Learning Accelerated Exploration of Ternary Organic Heterojunction Photocatalysts for Sacrificial Hydrogen Evolution
Journal of the American Chemical Society ( IF 14.4 ) Pub Date : 2023-12-01 , DOI: 10.1021/jacs.3c10586
Haofan Yang ₁ , Yu Che ₁ , Andrew I Cooper ₁ , Linjiang Chen ₂ , Xiaobo Li ₃

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Donor–acceptor heterojunctions in organic photocatalysts can provide enhanced exciton dissociation and charge separation, thereby improving the photocatalytic activity. However, the wide choice of possible donors and acceptors poses a challenge for the rational design of organic heterojunction photocatalysts, particularly for large ternary phase spaces. We accelerated the exploration of ternary organic heterojunction photocatalysts (TOHP) by using a combination of machine learning and high-throughput experimental screening. This involved 736 experiments in all, out of possible 4320 ternary combinations. The top ten most active TOHPs discovered using this strategy showed outstanding sacrificial hydrogen production rates of more than 500 mmol g^–1 h^–1, with the most active ternary material reaching a rate of 749.8 mmol g^–1 h^–1 under 1 sun illumination. These rates of photocatalytic hydrogen generation are among the highest reported for organic photocatalysts in the literature.

中文翻译：

机器学习加速探索用于牺牲析氢的三元有机异质结光催化剂

有机光催化剂中的供体-受体异质结可以增强激子解离和电荷分离，从而提高光催化活性。然而，可能的供体和受体的广泛选择对有机异质结光催化剂的合理设计提出了挑战，特别是对于大三元相空间。我们通过结合机器学习和高通量实验筛选，加速了三元有机异质结光催化剂（TOHP）的探索。这总共涉及 736 次实验，共有 4320 种可能的三元组合。使用该策略发现的前十种最活跃的TOHP表现出超过500 mmol g ^–1 h ^–1的出色牺牲产氢速率，其中最活跃的三元材料在1个太阳光照下达到749.8 mmol g ^–1 h ^–1的速率。这些光催化制氢速率是文献中报道的有机光催化剂中最高的。

更新日期：2023-12-01

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