[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125024-en":3,"doc-seo-125024-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125024,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery","Successful machine learning for catalyst design relies on high-quality, diverse data to generalize to new compositions, yet experimental catalyst discovery often produces scarce data biased toward low-yield outcomes. This work introduces a robust machine learning and explainable AI framework that classifies catalytic yield and attributes component contributions. Built with practices for scarcity and imbalance, it improves most evaluated models on oxidative methane coupling and yields class-aware XAI feature insights aligned with chemical intuition.","arXiv :2407 . 18935v1 [physics .chem-ph] 10 Jul 2024  \nA Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery  \nParastoo Semnani, ∗ ,†,‡,¶ Mihail Bogojeski,†,‡ Florian Bley,†,‡ Zizheng Zhang, § Qiong Wu, § Thomas Kneib, § Jan Herrmann, ∥ Christoph Weisser, ∥ Florina Patcas, ∥  \nand Klaus-Robert Müller ∗ ,†,‡,⊥ ,\\#  \n†Machine Learning Group, TU Berlin, Berlin, Germany ‡Berlin Institute for the Foundations of Learning and Data, Berlin, Germany ¶BASLEARN–TU Berlin/BASF Joint Lab for Machine Learning, TU Berlin, Berlin,  \nGermany  \n§Chair of Statistics and Campus Institute Data Science, Georg-August-University  \nGöttingen, Göttingen, Germany  \n∥BASF SE, Ludwigshafen, Germany  \n⊥Max Planck Institute for Informatics, Saarbrücken, Germany  \n\\#Department of Artificial Intelligence, Korea University, Seoul, South Korea  \nE-mail: [p.semnani@tu-berlin.de](p.semnani@tu-berlin.de) ; [klaus-robert.mueller@tu-berlin.de](klaus-robert.mueller@tu-berlin.de)  \nAbstract  \nThe successful application of machine learning in catalyst design depends on highquality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to the complex interactions of catalyst components, the design of novel catalysts has long relied on trial-and-error, a costly and  \nlabor-intensive process that results in scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the increasing popularity of machine learning applications in this field, most of the efforts so far have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components to the yield. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to the task of determining the yield of different catalyst combinations in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models (such as decision trees, random forest, and gradient boosted trees), logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using explainable AI (XAI) methods. Our analysis found that XAI methods, which provide class-aware explanations, such as Layer-wise Relevance Propagation, managed to identify key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance.  \nIllustration of the abstract is depicted in Figure 1 .  \nIntroduction  \nMachine learning (ML) models have recently become popular in the field of heterogeneous catalyst design. 1–5 The inherent complexity of the interactions between catalyst components is very high, leading to both synergistic and antagonistic effects on catalyst yield that are  \nFigure 1: Visual abstract of ML-guided catalyst design: The figure illustrates the process of oxidative methane coupling, where the catalyst consists of M1-M2-M3/support material. This catalyst is tested in high-throughput screening to determine the yield of each composition. The resulting data is then utilized in various machine learning models, whose performance and feature importance are subsequently analyzed.  \ndifficult to disentangle. Therefore, the discovery of well-performing catalyst","cbCaifkbq3xSfaBm","https://ap.wps.com/l/cbCaifkbq3xSfaBm","pdf",15728284,1,69,"English","en",105,"# Abstract\n# Introduction\n## Challenges in ML-guided catalyst design\n## Proposed ML and explainable AI framework\n## Evaluation on oxidative methane coupling","[{\"question\":\"What problem does the framework address in experimental catalyst discovery?\",\"answer\":\"It addresses data scarcity and strong class imbalance in experimental datasets, where results are biased toward undesired low-yield catalysts.\"},{\"question\":\"How does the framework support yield prediction and interpretation?\",\"answer\":\"It combines machine-learning practices to classify catalytic yield and uses explainable AI to identify which components most contribute to high-yield outcomes.\"},{\"question\":\"Which models and task are used to evaluate the framework?\",\"answer\":\"The framework is applied to oxidative methane coupling yield prediction and evaluated using tree-based models, logistic regression, support vector machines, and neural networks.\"}]","A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery | 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problem does the framework address in experimental catalyst discovery?","Question",{"text":75,"@type":76},"It addresses data scarcity and strong class imbalance in experimental datasets, where results are biased toward undesired low-yield catalysts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework support yield prediction and interpretation?",{"text":80,"@type":76},"It combines machine-learning practices to classify catalytic yield and uses explainable AI to identify which components most contribute to high-yield outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models and task are used to evaluate the framework?",{"text":84,"@type":76},"The framework is applied to oxidative methane coupling yield prediction and evaluated using tree-based models, logistic regression, support vector machines, and neural 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