Machine Learning for Accelerated Discovery of Solar Photocatalysts

Robust screening of materials on the basis of structure–property–activity relationships to discover active photocatalysts is a highly sought out aspect of photocatalysis research. Recent advancements in machine learning offer considerable opportunities to evolve photocatalysts discovery practices. Machine learning has largely facilitated various areas of science and engineering, including heterogeneous catalysis, but adaptation of it in photocatalysis research is still at an elementary stage. The scarcity of consistent training data is a major bottleneck, and we foresee the integration of photocatalysis domain knowledge in mainstream machine learning protocols as a viable solution. Here, we present a holistic framework incorporating machine learning and domain knowledge to set directions toward accelerated discovery of solar photocatalysts. This Perspective begins with a discussion on domain knowledge available in photocatalysis which could potentially be leveraged to liaise with machine learning methods. Subsequently, we present prevalent machine learning practices in heterogeneous catalysis tailored to assist discovery of photocatalysts in a purely data-driven fashion. Lastly, we conceptualize various strategies for complementing data-driven machine learning with photocatalysis domain knowledge. The strategies involve the following: (i) integration of theoretical and prior empirical knowledge during the training of machine learning models; (ii) embedding the knowledge in feature space; and (iii) utilizing existing material databases to constrain machine learning predictions. The aforementioned human-in-loop framework (leveraging both human and machine intelligence) could possibly mitigate the lack of interpretability and reliability associated with data-driven machine learning and reinforce complex model architectures irrespective of data scarcity. The concept could also offer substantial benefits to photocatalysis informatics by promoting a paradigm shift away from the Edisonian approach.