Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Nat Commun. 2021 Sep 6;12(1):5288. doi: 10.1038/s41467-021-25639-8.
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.
尽管在数据获取和算法开发方面取得了最新进展，但机器学习 (ML) 在实际催化剂设计中的应用仍面临巨大挑战，主要是因为其通用性有限且可解释性较差。在此，我们开发了一种理论融合神经网络 (TinNet) 方法，该方法将深度学习算法与化学吸附的 d 带理论相结合，用于预测过渡金属表面的反应性。我们以活性位集上的简单吸附物（例如 *OH、*O 和 *N）作为代表性描述符物种，证明 TinNet 在预测性能方面与纯数据驱动的 ML 方法相当，同时具有内在的可解释性。将物理相互作用的科学知识纳入数据学习中，进一步揭示了化学键的本质，并为 ML 发现具有所需催化性能的新型基序开辟了新途径。
