Mai Haoxin, Le Tu C, Hisatomi Takashi, Chen Dehong, Domen Kazunari, Winkler David A, Caruso Rachel A
Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
iScience. 2021 Aug 30;24(9):103068. doi: 10.1016/j.isci.2021.103068. eCollection 2021 Sep 24.
New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts.
传统上，新型光催化剂是通过反复试验的方法来确定的。机器学习已显示出巨大的潜力，有望提高从大量潜在材料中发现光催化剂的效率。在此，我们描述了一种使用堆叠元学习算法的多步骤、目标驱动的共识方法，该方法能够可靠地预测光催化剂的带隙和析氢活性。这些模型在小数据集上进行训练，可以快速筛选一个大的空间（超过1000万种材料），以识别有前景的无毒化合物作为候选的光解水催化剂。合成了两种有效的化合物和两种对照物，它们具有最佳的带隙值（约2 eV），但模型预测其没有光活性。它们的实验测量带隙和析氢活性与预测结果一致。值得注意的是，这两种在紫外光和可见光下具有强光活性的化合物是很有前景的可见光驱动光解水催化剂。这项研究证明了机器学习的力量以及大数据在加速下一代光催化剂发现方面的潜力。
