利用图卷积网络研究高熵合金的二氧化碳还原吸附点

H. Oliaei, N. Aluru
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引用次数: 0

摘要

二氧化碳减排是建设更清洁、更安全环境的重要一步。人们对探索高熵合金(HEAs)作为二氧化碳还原活性催化剂的兴趣日益高涨;然而,迄今为止,这种研究主要局限于二元 HEAs。受成功合成八元和二元 HEA 的启发,本文通过开发高保真图神经网络 (GNN) 框架,研究了由 Ag、Au、Cu、Pd、Pt、Co、Ga、Ni 和 Zn 组成的 HEA 的二氧化碳还原反应 (CO2RR) 性能。在此框架内,通过元素的特征化,采用了吸附位点的几何形状和物理特性。特别是利用电负性和原子半径等各种固有属性进行特征化,不仅实现了 CO2RR 性能描述符(即 CO 和 H 吸附能)的监督学习,还实现了吸附物理学的学习以及对未知金属和合金的泛化。所开发的模型分别评估了 35 亿个和 4 亿个可能的 CO 和 H 吸附位点的吸附强度。尽管 AgAuCuPdPtCoGaNiZn 合金的空间巨大,而且训练数据的规模相当小,但 GNN 框架仍表现出很高的准确性和良好的鲁棒性。这项研究为快速筛选和智能合成具有 CO2RR 活性和选择性的 HEA 铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network
Carbon dioxide reduction is a major step toward building a cleaner and safer environment. There is a surge of interest in exploring high-entropy alloys (HEAs) as active catalysts for CO2 reduction; however, so far, it is mainly limited to quinary HEAs. Inspired by the successful synthesis of octonary and denary HEAs, herein, the CO2 reduction reaction (CO2RR) performance of an HEA composed of Ag, Au, Cu, Pd, Pt, Co, Ga, Ni, and Zn is studied by developing a high-fidelity graph neural network (GNN) framework. Within this framework, the adsorption site geometry and physics are employed through the featurization of elements. Particularly, featurization is performed using various intrinsic properties, such as electronegativity and atomic radius, to enable not only the supervised learning of CO2RR performance descriptors, namely, CO and H adsorption energies, but also the learning of adsorption physics and generalization to unseen metals and alloys. The developed model evaluates the adsorption strength of ∼3.5 and ∼0.4 billion possible sites for CO and H, respectively. Despite the enormous space of the AgAuCuPdPtCoGaNiZn alloy and the rather small size of the training data, the GNN framework demonstrated high accuracy and good robustness. This study paves the way for the rapid screening and intelligent synthesis of CO2RR-active and selective HEAs.
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