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RESEARCH PAPER · ChemCatChem · 2018

The Rise of Catalyst Informatics: Towards Catalyst Genomics

KEISUKE G. TAKAHASHI, Lauren Takahashi, Itsuki Miyazato, Jun Fujima, Yuzuru Tanaka, Takeaki Uno, Hiroko Satoh, Koichi Ohno, Mayumi Nishida, Kenji Hirai, Junya Ohyama, Thanh Nhat Nguyen, Shun Nishimura, Toshiaki Taniike

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The Rise of Catalyst Informatics: Towards Catalyst GenomicsOriginalDescriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling37MatCALO: Knowledge-enabled machine learning in materials science35The emergence of Heusler alloy catalysts70Machine Learning for Computational Heterogeneous Catalysis356Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning55Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data90Using Artificial Intelligence To Forecast Water Oxidation Catalysts107Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of Ontology26Machine Learning and Scaling Laws for Prediction of Accurate Adsorption Energy51Machine Learning for Catalysis Informatics: Recent Applications and Prospects613High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane178Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts51A Primer about Machine Learning in Catalysis – A Tutorial with Code33Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Classification of platinum nanoparticle catalysts using machine learning46Structure-performance relationship of Mg(OEt)2-based Ziegler-Natta catalysts33Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data30Machine learning in experimental materials chemistry66Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data35Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: Statistical analysis of literature data and its experimental validation96Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review440Towards Experimental Handbooks in Catalysis62Open Data in Catalysis: From Today's Big Picture to the Future of Small Data50Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining31Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning21The pure and representative types of disordered platinum nanoparticles from machine learning12Representing the Methane Oxidation Reaction via Linking First-Principles Calculations and Experiment with Graph Theory13Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles10Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning13Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O223Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane54A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts79Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122Redesign chemical processes to substitute the use of fossil fuels: A viewpoint of the implications on catalysis37Catalytic Methylation of m-Xylene, Toluene, and Benzene Using CO2 and H2 over TiO2-Supported Re and Zeolite Catalysts: Machine-Learning-Assisted Catalyst Optimization40Open Catalyst 2020 (OC20) Dataset and Community Challenges765Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem8Unveiling gas‐phase oxidative coupling of methane via data analysis7Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts12Exploring Metal Cluster Catalysts Using Swarm Intelligence: Start with Hydrogen Adsorption5Extracting kinetic information in catalysis: an automated tool for the exploration of small data6High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3 -based catalysts for the oxidative coupling of methane16Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46The Rise of Catalysts Informatics1Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts25Mass-based biocatalyst metrics to guide protein engineering and bioprocess development50Kinetic Modeling of Ethene Oligomerization on Bifunctional Nickel and Acid β Zeolites9Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12Multi-output machine learning models for kinetic data evaluation : A Fischer–Tropsch synthesis case study50Data in Materials and Catalysts Informatics4Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems3Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation26Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities50Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations14Catalysts informatics: paradigm shift towards data-driven catalyst design61Improving scientific image processing accessibility through development of graphical user interfaces for scikit-image4Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation48The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts322Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater.12Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane8Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation10Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach61Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: an AI Approach Integrating Theoretical and Experimental Data1Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts24Advancing catalysis research through FAIR data principles implemented in a local data infrastructure – a case study of an automated test reactor12Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis1Parametric analysis of CO2 hydrogenation via Fischer-Tropsch synthesis: A review based on machine learning for quantitative assessment23Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs)19Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data49Recent advances in catalyst design, performance, and challenges of metal-heteroatom-co-doped biochar as peroxymonosulfate activator for environmental remediation64Toward accelerated discovery of solid catalysts using extrapolative machine learning approach10Catalysis in the digital age: Unlocking the power of data with machine learning75CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence0CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence2Web-based graphical user interface for automated materials feature engineering for machine learning0Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts1Ensemble Model Approach for Predicting the Yield of Dehydrogenation Products during the Oxidative Dehydrogenation of n-Butane2AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics11Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space8NHC-Cracker: A Platform for the In Silico Engineering of N-Heterocyclic Carbenes for Diverse Chemical Applications915 Years of Progress on Transition Metal-Based Electrocatalysts for Microbial Electrochemical Hydrogen Production: From Nanoscale Design to Macroscale Application24Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach22Statistical and Predictive Analysis of Supported Catalytically Active Metal Solutions (SCALMS) in Propane Dehydrogenation2Machine learning (ML)-assisted development of 2D green catalysts to support sustainability3Multiscale modelling in ammonia synthesis: Integrating CFD and process simulation - A review3Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts0Dynamic catalysts and the digital age: Synthetic consequences2Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 211Web-based graphical interface for catalyst gene design and profiling0Accelerating Multi-Elemental Catalyst Discovery with Interpretable Machine Learning and Automated Experimentation0Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 22Heterogeneous catalysts for the direct and oxidative dehydrogenation of n-butane to butadiene: A comprehensive review covering thermodynamics, catalyst development, kinetics and catalyst informatics0Catalytic valorization of lignocellulosic biomass: progress, challenges, and emerging opportunities in metal-based catalysis0Reaction-Depth Learning Enables Apparent Yield Extrapolation in Catalyst Informatics0

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  3. The emergence of Heusler alloy catalysts

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  4. Machine Learning for Computational Heterogeneous Catalysis

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  5. Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

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  6. Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

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  7. Using Artificial Intelligence To Forecast Water Oxidation Catalysts

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  8. Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of Ontology

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  9. Machine Learning and Scaling Laws for Prediction of Accurate Adsorption Energy

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  11. High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

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  18. Machine learning in experimental materials chemistry

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  19. Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

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  20. Revealing property-performance relationships for efficient CO2 hydrogenation to higher hydrocarbons over Fe-based catalysts: Statistical analysis of literature data and its experimental validation

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  21. Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

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  48. The Rise of Catalysts Informatics

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