Keisuke Takahashi

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Hokkaido University

Japan

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About

Keisuke Takahashi is a Professor at Hokkaido University in Japan. His research focuses on materials science and involves innovative approaches such as machine learning for feature engineering, catalyst discovery, and the design of advanced materials. Recent publications reflect his expertise in automated systems, AI in materials research, and the synthesis of functional materials like perovskites and gold phosphides.

Recent Grants

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実験・計算・データ科学の統合によるメタン変換触媒の探索・発見と反応機構の解明・制御

Open Date: 2017-04-01

Close Date: 2022-03-31

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Collaborators (4)

Lauren Takahashi

Assistant Professor

Hokkaido University

JAPAN

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Masato Machida

JAPAN

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Hajime Suzuki

Hokkaido University

JAPAN

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Hiroshi Yoshida

Kanazawa University

JAPAN

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Keisuke Takahashi

Research Interests

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About

Recent Grants

Articles (28)

Web-based graphical user interface for automated materials feature engineering for machine learning

Development of an Open-Source 3D-Printed Material Synthesis Robot FLUID: Hardware and Software Blueprints for Accessible Automation in Materials Science

Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts

AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics

Design of low temperature La2O3 oxidative coupling of methane catalysts using feature engineering and automated sampling

Designing and synthesizing perovskites with targeted bandgaps via tailored descriptors

Red Phosphorus‐Induced Transformation of Gold Mesh into Crystalline Gold Phosphide Mesh

Uncovering Design Principles in 2D Honeycomb–Kagome Magnetic Materials through High-Throughput Calculation

A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity

AI and Automation: Democratizing automation and the evolution towards true AI-autonomous robotics

Development of graphical user interface for design of experiments via Gaussian process regression and its case study

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

Theoretical Investigation of Cyanographene and Cyanographite for Potential Sodium Ion Battery

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

High-throughput calculation for screening of Formamidinium Halide Perovskite for Solar Cells

High-throughput calculation for the screening of formamidinium halide perovskite for solar cells

Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis

Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production <i>via</i> catalysis network analysis

Investigation of the Active-Site Structure of Cu-CHA Catalysts for the Direct Oxidation of Methane to Methanol Using In Situ UV–Vis Spectroscopy

Water Purification by 2‐Dimensional Dodecagonal Nitride and Graphenylene via First Principles Calculations

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Data-Driven Design and Understanding of Noble Metal-Based Water–Gas Shift Catalysts from Literature Data

The value of negative results in data-driven catalysis research

Catalysts informatics: paradigm shift towards data-driven catalyst design

Improving scientific image processing accessibility through development of graphical user interfaces for scikit-image

Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species

Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

Searching chemical action and network (SCAN): an interactive chemical reaction path network platform

Collaborators (4)

Lauren Takahashi

Masato Machida

Hajime Suzuki

Hiroshi Yoshida

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