Shun Nishimura

Latest

• Catalyst Informatics: Paradigm Shift towards Data-Driven Catalyst Design
• Bayesian-Optimization-Based Improvement of Cu-CHA Catalysts for Direct Partial Oxidation of CH4
• High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3-based catalysts for the oxidative coupling of methane
• Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter
• Relationships among the Catalytic Performance, Redox Activity, and Structure of Cu-CHA Catalysts for the Direct Oxidation of Methane to Methanol Investigated Using In Situ XAFS and UV--Vis Spectroscopies
• Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation
• The Rise of Catalysts Informatics
• Catalytic direct oxidation of methane to methanol by redox of copper mordenite
• Catalytic oxidation of methane to methanol over Cu-CHA with molecular oxygen
• Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2
• Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining
• Factors to influence low-temperature performance of supported Mn--Na2WO4 in oxidative coupling of methane
• Cover Feature: Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data (ChemCatChem 23/2020)
• Data-driven identification of the reaction network in oxidative coupling of the methane reaction via experimental data
• Representing the Methane Oxidation Reaction via Linking First-Principles Calculations and Experiment with Graph Theory
• Revisiting machine learning predictions for oxidative coupling of methane (OCM) based on literature data
• Data driven determination of reaction conditions in oxidative coupling of methane via machine learning
• High-throughput experimentation and catalyst informatics for oxidative coupling of methane
• Front Cover: Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data (ChemCatChem 15/2018)
• Unveiling hidden catalysts for the oxidative coupling of methane based on combining machine learning with literature data