Keisuke Takahashi

Latest

• The value of negative results in data-driven catalysis research
• Catalyst Informatics: Paradigm Shift towards Data-Driven Catalyst Design
• Bayesian-Optimization-Based Improvement of Cu-CHA Catalysts for Direct Partial Oxidation of CH4
• Data in Materials and Catalysts Informatics
• Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane
• 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
• Hydrogen adsorption studies of TiFe surfaces via 3-d transition metal substitution
• 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
• Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts
• Catalytic direct oxidation of methane to methanol by redox of copper mordenite
• Catalytic oxidation of methane to methanol over Cu-CHA with molecular oxygen
• Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts
• Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2
• Direct Design of Catalysts in Oxidative Coupling of Methane via High-Throughput Experiment and Deep Learning
• Extraction of catalyst design heuristics from random catalyst dataset and their utilization in catalyst development for oxidative coupling of methane
• Factors to influence low-temperature performance of supported Mn--Na2WO4 in oxidative coupling of methane
• Learning catalyst design based on bias-free data set for oxidative coupling of methane
• Mining hydroformylation in complex reaction network via graph theory
• Quick approach for optimization of monodisperse microsphere synthesis with a knowledge sharing strategy powered by machine learning
• Representing catalytic and processing space in methane oxidation reaction via multioutput machine learning
• rsc. li/chemical-science
• 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
• 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
• First-Principles Design of Cu12shellFecore Core--Shell Clusters Assembled with K3O into Hexameric Rings: Implications for Gas-Storage Materials
• Multidimensional classification of catalysts in oxidative coupling of methane through machine learning and high-throughput 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
• 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
• Tuning the hydrogen storage properties of TiFe clusters via Zr substitution
• Automatic oxidation threshold recognition of XAFS data using supervised machine learning
• Creating machine learning-driven material recipes based on crystal structure
• Data driven determination in growth of silver from clusters to nanoparticles and bulk
• 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
• The rise of catalyst informatics: towards catalyst genomics
• Visualizing scientists’ cognitive representation of materials data through the application of ontology
• Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations
• Aryl fluoride functionalized graphene oxides for excellent room temperature ammonia sensitivity/selectivity
• Committee machine that votes for similarity between materials
• Designing a tunable magnet using cluster-assembled iron
• Electronic structure of octagonal boron nitride nanotubes
• Front Cover: Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data (ChemCatChem 15/2018)
• Functionalized Single-Atom-Embedded Bilayer Graphene and Hexagonal Boron Nitride
• Outstanding Reviewers for Physical Chemistry Chemical Physics in 2017
• 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
• Tuning the Electronic Structure of an Aluminum Phosphide Nanotube through Configuration of the Lattice Geometry
• Uncovering periodicity and hidden trends responsible for predicting the magnetic moment of body centered cubic crystal
• Unveiling hidden catalysts for the oxidative coupling of methane based on combining machine learning with literature data
• Descriptors for predicting the lattice constant of body centered cubic crystal
• Designing Mg7 cluster-assembled two dimensional crystal
• Electronic structure of boron based single and multi-layer two dimensional materials
• Role of descriptors in predicting the dissolution energy of embedded oxides and the bulk modulus of oxide-embedded iron
• Structural stability and electronic properties of an octagonal allotrope of two dimensional boron nitride
• Structural, Electronic, and Transport Properties of Hybrid SrTiO3-Graphene and Carbon Nanoribbon Interfaces
• Toward Two-Dimensional Superatomic Honeycomb Structures. Evaluation of [Ge9 (Si (SiMe3)) 3]- as Source of Ge9--Cluster Building Blocks for Extended Materials
• Unveiling descriptors for predicting the bulk modulus of amorphous carbon
• A Need for Exploratory Visual Analytics in Big Data Research and for Open Science
• Designing square two-dimensional gold and platinum
• Enhanced hydrogen desorption properties of LiAlH 4 by doping lithium metatitanate
• Graphene decorated with Fe nanoclusters for improving the hydrogen sorption kinetics of MgH 2--experimental and theoretical evidence
• Hydrophobic and antioxidant effects in In, Sn, and Sb based two dimensional materials
• Material synthesis and design from first principle calculations and machine learning
• Materials informatics: a journey towards material design and synthesis
• Prediction of the dopant activity of chemical compounds against ammonia borane with key descriptors: electronegativity and crystal structures
• Reactivity of Two-Dimensional Au9, Pt9, and Au18Pt18 against Common Molecules
• Revealing the multibonding state between hydrogen and graphene-supported Ti clusters
• Reversible hydrogen uptake by BN and BC3 monolayers functionalized with small Fe clusters: a route to effective energy storage
• Selective growth of noble gases at metal/oxide interface
• Dependence of constituent elements of AB5 type metal hydrides on hydrogenation degradation by CO2 poisoning
• Iron oxide cluster induced barrier-free conversion of nitric oxide to ammonia
• Low temperature pollutant trapping and dissociation over two-dimensional tin
• Supporting Information- Low temperature pollutant trapping and dissociation over two-dimensional tin
• The effect of point defects on diffusion pathway within $α$-Fe
• Enhancing the hydrogen storage capacity of TiFe by utilizing clusters
• Low temperature hydrogenation of iron nanoparticles on graphene
• Physical properties of $α$-Fe upon the introduction of H, He, C, and N
• The growth of Fe clusters over graphene/Cu (111)
• Chemisorption of hydrogen on Fe clusters through hybrid bonding mechanisms
• H2 dissociation over NbO: The first step toward hydrogenation of Mg
• Strain field of interstitial hydrogen atom in body-centered cubic iron and its effect on hydrogen-dislocation interaction
• The catalytic effect of Nb, NbO and Nb2O5 with different surface planes on dehydrogenation in MgH2: Density functional theory study
• The stabilization of Fe, Ru, and Os clusters upon hydrogenation
• The structural and electronic properties of small osmium clusters (2--14): A density functional theory study
• Phase Transition of Mg during Hydrogenation of Mg--Nb2O5 Evaporated Composites
• A global optimization scheme for bimetallic nanoparticles