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RESEARCH PAPER · Nature Communications · 2024

Active learning streamlines development of high performance catalysts for higher alcohol synthesis

Manu Suvarna, Tangsheng Zou, Sok Ho Chong, Yuzhen Ge, Antonio José Martín, Javier Pérez‐Ramírez

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Active learning streamlines development of high performance catalysts for higher alcohol synthesisOriginalData-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization4Machine learning assisted reactor and full process optimization design for alcohol oxidation10A revolutionary paradigm in chemistry and materials science research: self-driving laboratories8Accelerating materials discovery through active learning: Methods, challenges and opportunities12Critical Assessment of Active Learning Benchmarks in Materials Discovery: Undisclosed Baselines and Optimistic Bias0General data management workflow to process tabular data in automated and high-throughput heterogeneous catalysis research6High-Throughput Methods for Accelerated Catalyst Discovery3Leveraging feature gradient for efficient acquisition function maximization in material composition design3Sustainable thermochemical plastic valorization towards a circular economy: a critical review27Synthesis planning for atomically precise metal nanoclusters8Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48Applications of machine learning in surfaces and interfaces30Transformative strategies for bimetallic catalyst screening in biomass conversion through active learning and DFT8Non-thermal plasma and modified catalysts: A synergistic approach to air purification7Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming0Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming12Science acceleration and accessibility with self-driving labs83A Review on Catalytic Ethanol Synthesis via Hydrogenation of Carbon Dioxide21Structural stability of layered oxides for sodium-ion batteries: Insights and strategies10A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research5Neural network-based thermal model for virtual metrology of lunar orbiter temperatures via active and transfer learning3Explainable Artificial Intelligence Elucidates Synthesis‐Structure‐Property‐Function Relationships in Nanostructured Catalysts29Descriptors toward higher alcohols and olefins formation in CO2 hydrogenation on promoted iron catalysts16Data-Driven Strategies for Designing Multicomponent Molten Catalysts to Accelerate the Industrialization of Methane Pyrolysis18Self‐Evolving Discovery of Carrier Biomaterials with Ultra‐Low Nonspecific Protein Adsorption for Single Cell Analysis6Best practices for multi-fidelity Bayesian optimization in materials and molecular research30Biology-aware machine learning for culture medium optimization6Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Efficient optimization of ultra-high temperature ceramic oxidation resistance based on limited data via GAN-enhanced active learning5Theoretical High-Throughput Screening of Single-Atom CO2 Electroreduction Catalysts to Methanol Using Active Learning18Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8Autonomous catalysis research with human–AI–robot collaboration37Iterative Machine Learning-Guided Discovery of Transition Metal Compounds as Catalysts for Li–CO 2 and Li–Air Batteries6Synergistic Plasma Catalysis for CO 2 Conversion: Mechanistic Insights, Advanced Characterization, and Rational Design Strategies15Integrative catalytic pairs driving complex chemical reactions29Design of rust layer stabilizers for weathering steel guided by interpretable machine learning and Bayesian optimization7Building Catalyst Exploration Highways by Integrating High‐Throughput and Machine Learning Technologies11Solar-driven catalytic platforms for closed-loop carbon cycling: CO2 assisting biomass conversion2Spatial-adaptive active learning identifies ultra-durable and highly active catalysts for acidic oxygen evolution reaction9Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices15Sequential active learning for medium optimization in mAb production1AI-powered electronic skin: from smart design to cognitive interaction1Catalytic CO 2 hydrogenation toward net-zero fuels and chemicals1Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow5Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes7Thermo-Catalytic Carbon Dioxide Hydrogenation to Ethanol4Decarbonizing fossil-to-chemicals conversion: reliable real-time optimization under coupled chemical and carbon capture dynamics0Nitrogen-doped activated carbon supported CuCo catalysts with electronic synergy for highly efficient syngas conversion to ethanol1AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity3Generative AI as an Enabler for Process Intensification3Roadmap for transforming heterogeneous catalysis with artificial intelligence27Deciphering the Interfacial Catalysis of Metal–Oxide Nanocatalysts in CO 2 Hydrogenation through a Machine Learning Approach8Machine learning framework for prediction of plasma-based carbon dioxide conversion: Balancing computational efficiency with experimental efforts1Adaptive Experiment Planning for Inverse Design and Understanding: Synergistic Interactions as Key to Optimized Multi-Promoter Formulations1Emerging Trends and Future Prospects of the Catalytic Conversion of Syngas to Higher Alcohols3Adaptive Learning with Gaussian Process Regression: A Comprehensive Review of Methods and Applications6Rational design of MXene composites for high-performance potassium-ion batteries9Liquid-phase thermal decomposition-derived nanoparticles for electrocatalytic applications1From Discovery to Deliverables: Bridging the Machine Learning, Synthesis, and Characterization Gap in Nanoscale Materials1Machine learning boosted optimal parameters identification for higher alcohols synthesis over CuZn-based catalysts with experimental validation1Nanomaterial Integration at Liquid–Liquid Interfaces for Green Catalysis2Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review0Biomass chemical looping: A sustainable pathway for energy and chemicals0Thermal and Photocatalytic Conversion of CO 2 to Ethanol: Advances in Catalyst Design, Challenges, and Opportunities0Transparent reporting for agentic catalysis enabled by artificial intelligence: Community guidelines and a publication checklist2Na promoter synergistic tuning Co-Fe alloy-carbide dual sites: Directing syngas conversion to C2+ alcohols0Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation0Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis0Trendbericht Technische Chemie 20260Data- and AI-Driven Catalysis Science: From Descriptors to Digital Catalysis for Next-Generation Catalysts0Machine Learning-Accelerated Advancements in Electrocatalytic CO2 Reduction0Vanadium–titanium catalysts in oxidative catalysis: from surface structures to reaction mechanisms and applications0Atomically dispersed p-block sites toward destructive PFAS treatment: Direct evidence, mechanistic analogies for CF bond activation, and process intensification0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot0

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