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RESEARCH PAPER · Nature Catalysis · 2023

The value of negative results in data-driven catalysis research

Toshiaki Taniike, KEISUKE G. TAKAHASHI

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The value of negative results in data-driven catalysis researchOriginalClosed-Loop Optimization of Catalysts for Oxidative Propane Dehydrogenation with Co2 Using Artificial Intelligence0Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15Toward the Golden Age of Materials Informatics: Perspective and Opportunities23Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6TiO2/WO3/graphene for photocatalytic H2 generation and benzene removal: Widely employed still an ambiguous system29Machine Learning-Accelerated Discovery of Novel 2D Ferromagnetic Materials with Strong Magnetization1Machine learning-accelerated discovery of novel 2D ferromagnetic materials with strong magnetization21Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts24Closed-loop optimization of catalysts for oxidative propane dehydrogenation with CO2 using artificial intelligence7Molecular Simulation Meets Machine Learning15Towards physics-informed explainable machine learning and causal models for materials research34GLAS: an open-source easily expandable Git-based scheduling architecture for integral lab automation3Interpretable machining learning assisted insights into bifunctional squaramide catalyzed ring-opening polymerization of lactide3Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts11Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis1Development of graphical user interface for design of experiments via Gaussian process regression and its case study2Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Paving the road towards automated homogeneous catalyst design32Roadmap on Data-Centric Materials Science0MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models6Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane14Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science2Recent advances in catalyst design, performance, and challenges of metal-heteroatom-co-doped biochar as peroxymonosulfate activator for environmental remediation64Machine learning in energy storage material discovery and performance prediction77Roadmap on data-centric materials science27Modern Automation in Organic Synthesis Laboratories5Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions21Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case1Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case14Machine Learning Accelerated Discovery of Entropy-Stabilized Oxide Catalysts for Catalytic Oxidation58FACS‐Sortable Triple Emulsion Picoreactors for Screening Reactions in Biphasic Environments6Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning12A FAIR research data infrastructure for high-throughput digital chemistry4Generative AI for design of nanoporous materials: review and future prospects41Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling1Selectively monitoring the operando temperature of active metal nanoparticles during catalytic reactions by X-ray absorption nanothermometry44Graphene-modified g-C3N4/ α-Fe2O3 systems for light-induced hydrogen generation5Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies5Expanding the Synthetic Playbook with a Biochemical Twist: The 58th Bürgenstock Conference0Expanding the Synthetic Playbook with a Biochemical Twist: The 58th Bürgenstock Conference0Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach22Machine-Learning-Accelerated Development of High-Nickel NCM Cathodes via Multivariable Co-optimization3Best Practices for Machine Learning-Assisted Protein Engineering5Mechanistically guided univariate structure-selectivity relationship in chromium-catalyzed ethylene tetramerization0Reliability of milligram-scale high-throughput screening for evaluating polyolefin chemical recycling in the melt with heterogeneous catalysis3Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 211What Do Large Language Models Know About Materials?2DFT modeling of electrocatalytic water splitting for improvement of experimental relevance9Data-driven strategies for decoding structure–reactivity relationships in porous electrocatalysis0Decoupling mass transport from spin chemistry in the electro-fenton process under magnetic fields0Large language models for porous materials: from text mining to autonomous laboratory6Sustaining the growth of the materials informatics ecosystem0Toward efficient reporting for catalytic methane reforming data: a framework and review for standardized scientific data reporting1Towards a sustainable high-quality materials data ecosystem: frameworks and strategies6Reframing “disappointing” data at Life Science Alliance1Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 22Integrated Experimental–Theoretical and Data-Driven Multiphysics Analysis of Material Properties in Coatings, Pretreatments, Interfaces, and Artificial Intelligence-Assisted Reliability for Medical and Biomedical Devices0Transparent reporting for agentic catalysis enabled by artificial intelligence: Community guidelines and a publication checklist2Catalyst and Catalysis Co-Exploration in Methane Utilization0Data-richness-driven machine learning for predictive design of high-performance g-C₃N₄ photocatalysts in NO removal0Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation0Machine Learning Meets Photocatalytic Water Splitting for Sustainable Hydrogen Production0Interpretable data-driven discovery of high-performance multielement metal oxide electrocatalysts for oxygen evolution reaction from a small hybrid dataset0High-throughput study of doped ZrO2 catalysts for ethanol-to-propylene conversion0Machine learning–assisted inverse design of PEMFC cathode ORR catalysts: A two-level framework from performance targets to device validation0Structure-activity relationships of lanthanide-doped ZrO2 catalysts in ethanol-to-propylene conversion0Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization0Ni- and Cu-based catalysts for hydrogen production via steam reforming: Mechanisms, active structure evolution, and stability0

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  10. Closed-loop optimization of catalysts for oxidative propane dehydrogenation with CO2 using artificial intelligence

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