Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
School of Chemistry and Chemical Engineering, Qufu Normal University, Qufu, Shandong 273165, P. R. China.
J Phys Chem Lett. 2023 May 25;14(20):4760-4765. doi: 10.1021/acs.jpclett.3c00719. Epub 2023 May 15.
The adsorption energy of adsorbed molecules on single-atom catalysts is a key indicator of the catalytic activity of the catalysts. Developing a generic and interpretable structure-property prediction model from numerous influencing factors is a challenging task. In this work, we constructed a machine learning (ML) model from first-principles calculations of the adsorption energy data of O on Ni(II), Co(II), Cu(II), Fe(II), Fe(III), and Mn(II) single-atom catalysts supported on 15 different N-C substrates under various spin states. A mathematic formula is proposed to predict the adsorption energy by a novel data-driven descriptor derived from physically meaningful factors such as geometric distances and atomic charges. This data-driven descriptor is relevant to only the geometrical configuration of the adsorbate, while the parameters in the linear formulas contain only substrate-specific information. This ML model with the ability to decouple variables will greatly advance the understanding of metal-N-C single-atom catalysts and help in the design of new substrates to modulate catalytic activity.
吸附分子在单原子催化剂上的吸附能是衡量催化剂催化活性的一个关键指标。从众多影响因素中开发出一个通用且可解释的结构-性质预测模型是一项具有挑战性的任务。在这项工作中，我们构建了一个机器学习（ML）模型，该模型基于第一性原理计算，对吸附能数据进行了研究，研究对象是负载在 15 种不同 N-C 基底上的 Ni(II)、Co(II)、Cu(II)、Fe(II)、Fe(III)和 Mn(II)单原子催化剂，其中包含了各种自旋态下 O 的吸附。我们提出了一种数学公式，通过一种新的数据驱动描述符来预测吸附能，该描述符源自物理意义上的因素，如几何距离和原子电荷。这个数据驱动的描述符只与吸附物的几何构型有关，而线性公式中的参数只包含基底特有的信息。这种具有变量解耦能力的 ML 模型将极大地促进对金属-N-C 单原子催化剂的理解，并有助于设计新的基底来调节催化活性。
