Itsuki Miyazato

Latest

• Designing transformer oil immersion cooling servers for machine learning and first principle calculations
• Designing two-dimensional dodecagonal boron nitride
• 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
• Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts
• Catalytic direct oxidation of methane to methanol by redox of copper mordenite
• 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
• Direct Design of Catalysts in Oxidative Coupling of Methane via High-Throughput Experiment and Deep Learning
• Representing catalytic and processing space in methane oxidation reaction via multioutput machine learning
• Unveiling gas-phase oxidative coupling of methane via data analysis
• Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts
• Controlling electronic structure of single-layered $$$$$backslash$hbox $$HfX$$$$ _ $$3$$ $$ HfX 3 ($$$backslash$hbox $$X= S$$ $$ X= S, Se) trichalcogenides through systematic Zr doping
• Data-driven identification of the reaction network in oxidative coupling of the methane reaction via experimental data
• Rising Sun Envelope Method: An Automatic and Accurate Peak Location Technique for XANES Measurements
• Transition of wide-band gap semiconductor h-BN (BN)/P heterostructure via single-atom-embedding
• Automatic oxidation threshold recognition of XAFS data using supervised machine learning
• High-throughput experimentation and catalyst informatics for oxidative coupling of methane
• The rise of catalyst informatics: towards catalyst genomics
• Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations
• Front Cover: Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data (ChemCatChem 15/2018)
• Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning
• Redesigning the materials and catalysts database construction process using ontologies
• Revealing the multi hydrogen bonding state within iron doped amorphous carbon
• Searching for hidden perovskite materials for photovoltaic systems by combining data science and first principle calculations
• Unveiling hidden catalysts for the oxidative coupling of methane based on combining machine learning with literature data
• Electronic structure of boron based single and multi-layer two dimensional materials