Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol,ACS Applied Materials & Interfaces

当前位置： X-MOL 学术 › ACS Appl. Mater. Interfaces › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2021-11-17 , DOI: 10.1021/acsami.1c16696
Diptendu Roy ₁ , Shyama Charan Mandal ₁ , Biswarup Pathak ₁

Affiliation

The revolutionary development of machine learning and data science and exploration of its application in material science are huge achievements of the scientific community in the past decade. In this work, we have reported an efficient approach of machine learning-aided high-throughput screening for finding selective earth-abundant high-entropy alloy-based catalysts for CO₂ to methanol formation using a machine learning algorithm and microstructure model. For this, we have chosen earth-abundant Cu, Co, Ni, Zn, and Mg metals to form various alloy-based compositions (bimetallic, trimetallic, tetrametallic, and high-entropy alloys) for selective CO₂ reduction reaction toward CH₃OH. Since there are several possible surface microstructures for different alloys, we have used machine learning along with DFT calculations for high-throughput screening of the catalysts. In this study, the stability of various 8-atom fcc periodic (111) surface unit cells has been calculated using the atomic-size difference factor (δ) as well as the ratio taken from Gibbs free energy of mixing (Ω). Thinking about the simplicity and accuracy, microstructure models by considering the neighboring atoms of the adsorption sites and others as Cu atoms have been considered for different adsorption sites (on-top, bridge, and hollow-hcp). Moreover, the adsorption energies of the *H, *O, *CO, *HCO, *H₂CO, and *H₃CO intermediates have been predicted using the best fitted algorithm of the training set. The predicted adsorption energies have been screened based on the pure Cu adsorption energy. Furthermore, the screened catalysts have been correlated among different adsorption site microstructures. At the end, we were able to find seven active catalysts, among which two catalysts are CuCoNiZn-based tetrametallic, three catalysts are CuNiZn-based trimetallic, and two catalysts are CuCoZn-based trimetallic alloys. Hence, this work demonstrates not an ultimate but an efficient approach for finding new product-selective catalysts, and we expect that it can be convenient for other similar types of reactions in forthcoming days.

中文翻译：

机器学习驱动的高通量筛选用于 CO2 选择性加氢制甲醇的合金基催化剂

机器学习和数据科学的革命性发展及其在材料科学中的应用探索是近十年来科学界的巨大成就。在这项工作中，我们报告了一种有效的机器学习辅助高通量筛选方法，用于使用机器学习算法和微观结构模型寻找选择性地球丰富的高熵合金基催化剂，用于 CO ₂到甲醇的形成。为此，我们选择了地球上含量丰富的 Cu、Co、Ni、Zn 和 Mg 金属来形成各种基于合金的成分（双金属、三金属、四金属和高熵合金），用于对 CH ₃进行选择性 CO ₂还原反应哦。由于不同合金有几种可能的表面微观结构，我们使用机器学习和 DFT 计算来对催化剂进行高通量筛选。在这项研究中，使用原子尺寸差异因子 (δ) 以及从吉布斯混合自由能 (Ω) 中获取的比率计算了各种 8 原子 fcc 周期性 (111) 表面晶胞的稳定性。考虑到简单性和准确性，将吸附位点的相邻原子和其他原子视为 Cu 原子的微观结构模型已被考虑用于不同的吸附位点（顶部、桥接和空心 hcp）。此外，*H、*O、*CO、*HCO、*H ₂ CO和*H ₃的吸附能已使用训练集的最佳拟合算法预测 CO 中间体。基于纯铜吸附能筛选了预测的吸附能。此外，筛选出的催化剂在不同的吸附位点微观结构之间存在关联。最终我们找到了七种活性催化剂，其中两种催化剂为CuCoNiZn基四金属，三种催化剂为CuNiZn基三金属，两种催化剂为CuCoZn基三金属合金。因此，这项工作证明了寻找新产品选择性催化剂的方法不是最终的而是有效的方法，我们希望它可以方便地在未来几天内用于其他类似类型的反应。

更新日期：2021-12-01

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南