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RESEARCH ARTICLE · The Journal of Physical Chemistry C · 2024

A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery

Parastoo Semnani, Mihail Bogojeski, Florian Bley, Zizheng Zhang, Qiong Wu, Thomas Kneib, Jan Herrmann, Christoph Weisser, F. Patcas, Klaus‐Robert Müller

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A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst DiscoveryOriginalExploration without Explanation: A Conceptual Critique of Black-Box Materials Discovery0Advancements in artificial neural networks and fast pyrolysis of biomass processing: A comprehensive review and a bibliometric analysis14Parametric Optimization of Transition Metal-Based Nanocomposite Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media4PLGA-based long-acting injectable (LAI) formulations32Heterojunction nanomaterials as robust electrocatalysts for efficient water splitting – A review22Maximizing oxygen reduction reaction performance with minimal Pt: A review of high mass activity Pt-M catalysts8Unveiling the Power of Proximity of Prevalent Fe-Based Tandem Catalysts in CO 2 Hydrogenation via Modified Fischer–Tropsch: Crucial Relations toward Industrialization22Catalyst screening for electrochemical ammonia synthesis: a critical review3Computational data as the fuel for AI in chemistry0Designing stable electrocatalysts for sustainable energy conversion3Machine learning approaches to predicting metal catalyst performance in olefin polymerization0Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage2Artificial intelligence-enabled MOS gas sensors: Towards Selective and intelligent detection in complex environments19Catalyst Loading Technology for Fixed-Bed Reactors: From Empirical Heuristics to Data-Driven Intelligent Regulation2AI-guided approaches in organometallic research: cognition in chemistry7Machine-Learning-Guided Discovery and Statistical Analysis of Multielemental Reverse Water–Gas Shift Catalysts1Dual Roles of Coke in Fresh and Modified HY Zeolite Catalyzed Aromatic Alkylation: Mechanisms, Structural Transformations, and Catalyst Regeneration2Machine learning-driven approaches in electrochemical CO2 reduction: Bridging the gap between data and sustainable catalysis7Fast and accurate explanations of distance-based classifiers by uncovering latent explanatory structures1Mechanistic Insights into Iron–Sulfur Clusters for Direct Coal Liquefaction: A Combined First-Principles and Machine Learning Study0Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction0Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes5AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery0Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design0Explainable AI in Electrocatalysis and Photocatalysis: From Catalyst Design to Mechanistic Insights7An Agent-as-a-Service Framework: A Case Study on Hydrogen Evolution0Data-driven optimization of nitrogen-doped carbon mediated sulfate radical advanced oxidation processes: process window identification and mechanistic insights0From Experiment‐Driven to Theory‐ and Data‐Driven: A Computational Paradigm Shift in High‐Entropy Electrocatalyst Design0Familywise Feature Importance Stability in Chemical and Materials Machine Learning1Artificial intelligence‐assisted nanozyme design for medical and environmental applications0Explainable artificial intelligence shines a light on catalysis: methods, applications, and future directions0Machine learning predictive models for optimization of reaction parameter over metal oxide promoters for syngas production via autothermal reforming0High-entropy oxides for oxygen reduction reaction: Linking the composition, defects, and electronic structure with catalytic activity0Artificial intelligence for sustainable chemical synthesis: Advances in catalysis, biocatalysis, solvents, materials, and life cycle assessment0Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0Data-Driven Accelerated Design-Synthesis of Heterogeneous Fenton-like Catalysts for Water Decontamination0

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