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首页> 外文期刊>Catalysis science & technology >Computer-aided bimetallic catalyst screening for ester selective hydrogenation
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Computer-aided bimetallic catalyst screening for ester selective hydrogenation

机译：计算机辅助筛查双金属催化剂酯选择性加氢

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摘要

Heterogeneous hydrogenation of esters is a promising chemical process to produce alcohols. However, the selective hydrogenation of dibasic esters is still a challenge for both academia and industry. In this work, taking dimethyl oxalate (DMO) hydrogenation as an example, we have performed microkinetic analysis to explain the trend in the dimethyl oxalate hydrogenation activity and methyl glycolate (MG) selectivity across Ag, Cu, Ni, and Ru, using C and O adsorption energies as two descriptors. Ag is identified to be the best elemental metal catalyst for MG production. An unsupervised machine learning method based on the bisecting k-means hierarchical clustering algorithm is employed to determine the stable adsorption configurations over 1482 A3B1 and 741 A1B1 alloys. Ag3Zn1, Ag3Sn1, and Ag3Mg1 catalysts are selected as promising bimetallic catalyst candidates due to their enhanced catalytic performance and relatively low cost.
机译：异构酯加氢是一个有前途的化学过程产生醇类。然而,氢的选择性加氢对学术界和酯类仍然是一个挑战行业。(DMO)加氢为例,我们有microkinetic进行分析解释草酸二甲酯催化加氢的趋势活动和甲基羧基乙酸(MG)选择性在Ag)、铜、镍、和俄文,使用C和O吸附能量两个描述符。确定是最好的金属元素MG生产的催化剂。机器学习方法基于平分k - means聚类算法用来确定稳定的吸附在1482 A3B1和741 A1B1配置合金。选为有前途的双金属催化剂候选人由于其增强的催化性能和成本相对较低。

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