Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. We consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.用于催化剂研发的材料空间极为广阔。传统上，研究人员常借助火山曲线来解析最优催化剂的特性，为新型材料筛选提供物理层面的理论依据。其生成依赖于凭借专业知识选取的单一描述符（descriptor）。本研究推出一款基于Python的软件DescMAP，可自动完成描述符筛选、火山图生成以及结构敏感反应的活性位点识别工作。针对结构敏感反应，本软件纳入了传统的基于能量的描述符与几何描述符。DescMAP与虚拟动力学实验室（Virtual Kinetic Laboratory，VLab）集成，可实现多种功能。该软件支持以电子表格或模板文件作为输入以保障灵活性，并输出可交互的图表以供后续处理。本研究以(111)密排表面上乙烷无氧脱氢制乙烯，以及不同铂（Pt）晶面上甲烷完全氧化这两个反应为例，展示了DescMAP的功能。该软件可便捷地拓展应用至其他复杂化学反应体系，实现潜在催化剂的快速筛选。