Keisuke Takahashi

Hokkaido University — Japan

Materials Science · Physical Sciences

48h-index8.3kcitations447works2.82yr avg
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Research Topics

Machine Learning in Materials Science(66), Catalysis and Oxidation Reactions(54), Catalytic Processes in Materials Science(46), Graphene research and applications(22), Spine and Intervertebral Disc Pathology(22)

Publications446 total

Sulfur-Rich Benzothiadiazole-Based Conjugated Polymers for Efficient CO <sub>2</sub> Electroreduction
Energy & Fuels·2025
Web-based graphical interface for catalyst gene design and profiling
Science and Technology of Advanced Materials Methods·2025Open Access
Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts
The Journal of Physical Chemistry Letters·2025
Unique Phase Evolution and Multielemental Effects in Natural Rhodochrosite for Methane Oxidation
Chemistry - A European Journal·2025
Red Phosphorus‐Induced Transformation of Gold Mesh into Crystalline Gold Phosphide Mesh
physica status solidi (RRL) - Rapid Research Letters·2025
Uncovering Design Principles in 2D Honeycomb–Kagome Magnetic Materials through High-Throughput Calculation
The Journal of Physical Chemistry Letters·2025
A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity
ACS Catalysis·2025· 2 cited
Development of an Open-Source 3D-Printed Material Synthesis Robot FLUID: Hardware and Software Blueprints for Accessible Automation in Materials Science
ACS Applied Engineering Materials·2025· 4 cited
AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics
Chemical Science·2025· 2 citedOpen Access
Web-based graphical user interface for visualizing and analyzing chemical reaction networks
Reaction Chemistry & Engineering·2025Open Access
Photocatalytic dissolution of cellulose for hydrogen and nanofiber production: unveiling crucial factors <i>via</i> experiments and informatics
RSC Sustainability·2025· 1 citedOpen Access
Designing and synthesizing perovskites with targeted bandgaps <i>via</i> tailored descriptors
Chemical Science·2025· 2 citedOpen Access
Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning
Journal of Catalysis·2024· 5 cited
ABO<sub>4</sub> as an Active Catalyst Structure for Direct Partial CH<sub>4</sub> Oxidation as Identified through Screening of Supported Catalysts
ACS Catalysis·2024· 5 cited
Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts
The Journal of Physical Chemistry C·2024· 1 cited
A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity
2024Open Access
Web-based graphical user interface for automated materials feature engineering for machine learning
Science and Technology of Advanced Materials Methods·2024Open Access
Design of low temperature La<sub>2</sub>O<sub>3</sub> oxidative coupling of methane catalysts using feature engineering and automated sampling
Catalysis Science & Technology·2024· 2 cited
Automatic Identification of X-ray Absorption Fine Structure Spectra via Machine Learning
The Journal of Physical Chemistry C·2024· 4 cited
Unbiased Dataset for Methane Dry Reforming and Catalyst Design Guidelines Obtained by High-Throughput Experimentation and Machine Learning
2024
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Frequent Co-authors

Itsuki Miyazato(6), Lauren Takahashi(6), Jun Ishihara(6), Susumi Hatakeyama(6), Shun Nishimura(5), Junya Ohyama(5), Toshiaki Taniike(5), Yuzuru Tanaka(5), M. Kawasaki(4), Yoshinori Tokura(4), Jun Fujima(4), Naoki Itoh(4), Thanh Nhat Nguyen(3), Kikuro Fukushima(3), M. Kato(3), Ryutaro Yoshimi(2), Atsushi Tsukazaki(2), Ashutosh Thakur(2), Nobutsune Takezawa(2), Hirokazu Kobayashi(2)