Machine learning for design principles for single atom catalysts towards electrochemical reactions

Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, Patrick Ryan Galligan, Hoilun Wong, Zhenjing Liu, Hongwei Liu, Yuting Cai, William A. Goddard and Zhengtang Luo

J. Mater. Chem. A, 2022, 10, 15309
DOI: 10.1039/D2TA02039D

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

Lauren Takahashi, Taku Yamada, Hidekazu Okamoto and Keisuke Takahashi

Catal. Sci. Technol., 2024, 14, 4927
DOI: 10.1039/D4CY00573B

What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2reduction? A data-driven retrospective

Kevin Rossi

Phys. Chem. Chem. Phys., 2023, 25, 6867
DOI: 10.1039/D2CP04576A

Catalysts informatics: paradigm shift towards data-driven catalyst design

Keisuke Takahashi, Junya Ohyama, Shun Nishimura, Jun Fujima, Lauren Takahashi, Takeaki Uno and Toshiaki Taniike

Chem. Commun., 2023, 59, 2222
DOI: 10.1039/D2CC05938J

High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

Shun Nishimura, Son Dinh Le, Itsuki Miyazato, Jun Fujima, Toshiaki Taniike, Junya Ohyama and Keisuke Takahashi

Catal. Sci. Technol., 2022, 12, 2766
DOI: 10.1039/D1CY02206G

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

Fumiya Nishino, Hiroshi Yoshida, Masato Machida, Shun Nishimura, Keisuke Takahashi and Junya Ohyama

Catal. Sci. Technol., 2023, 13, 5576
DOI: 10.1039/D3CY00587A

Fast-Decoding Algorithm for Electrode Processes at Electrified Interfaces by Mean-Field Kinetic Model and Bayesian Data Assimilation: An Active-Data-Mining Approach for the Efficient Search and Discovery of Electrocatalysts

Ken Sakaushi, Aoi Watanabe, Tomoaki Kumeda and Yasushi Shibuta

ACS Appl. Mater. Interfaces, 2022, 14, 22889
DOI: 10.1021/acsami.1c21038

Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering

Aya Fujiwara, Sunao Nakanowatari, Yohei Cho and Toshiaki Taniike

Science and Technology of Advanced Materials, 2025, 26
DOI: 10.1080/14686996.2025.2454219

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

Fernando Garcia-Escobar, Toshiaki Taniike and Keisuke Takahashi

J. Chem. Inf. Model., 2024, 64, 1512
DOI: 10.1021/acs.jcim.3c01952

Rising stars in AI use robotics and automation to accelerate their work

Felicity Nelson

Nature, 2025
DOI: 10.1038/d41586-024-04233-0

Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts

Yoshiki Hasukawa, Fernando Garcia-Escobar, Keisuke Takahashi and Lauren Takahashi

J. Phys. Chem. C, 2025, 129, 391
DOI: 10.1021/acs.jpcc.4c07397

Multi-output machine learning models for kinetic data evaluation : A Fischer–Tropsch synthesis case study

Anoop Chakkingal, Pieter Janssens, Jeroen Poissonnier, Mirella Virginie, Andrei Y. Khodakov and Joris W. Thybaut

Chemical Engineering Journal, 2022, 446, 137186
DOI: 10.1016/j.cej.2022.137186

Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data

Sunao Nakanowatari and Toshiaki Taniike

J. Phys. Chem. Lett., 2025, 16, 6410
DOI: 10.1021/acs.jpclett.5c01010

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

Sunao Nakanowatari, Keisuke Takahashi, Hieu Chi Dam and Toshiaki Taniike

ACS Catal., 2025, 15, 8691
DOI: 10.1021/acscatal.5c00100

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Keisuke Takahashi and Lauren Takahashi

J. Phys. Chem. Lett., 2023, 14, 4726
DOI: 10.1021/acs.jpclett.3c00648

A Hybrid Encoding-Enhanced Machine Learning and Multioptimization Framework for the Optimal Catalyst Design of the Methane-to-Ethylene Process

Qingchun Yang, Lei Zhao, Qiwen Guo, Zhao Wang and Dawei Zhang

Ind. Eng. Chem. Res., 2025, 64, 14512
DOI: 10.1021/acs.iecr.5c00563

Exploring stabilizer formulations for light-induced yellowing of polystyrene by high-throughput experimentation and machine learning

Ken Takimoto, Kengo Takeuchi, Nhan Nu Thanh Ton and Toshiaki Taniike

Polymer Degradation and Stability, 2022, 201, 109967
DOI: 10.1016/j.polymdegradstab.2022.109967

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

Keisuke Takahashi, Lauren Takahashi, Son Dinh Le, Takaaki Kinoshita, Shun Nishimura and Junya Ohyama

J. Am. Chem. Soc., 2022, 144, 15735
DOI: 10.1021/jacs.2c06143

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

Toshiaki Taniike, Aya Fujiwara, Sunao Nakanowatari, Fernando García-Escobar and Keisuke Takahashi

Commun Chem, 2024, 7
DOI: 10.1038/s42004-023-01086-y

Exploration of ethanol-to-butadiene catalysts by high-throughput experimentation and machine learning

Tejkiran P. Jayakumar, Sumanaspurthi P. Suresh Babu, Thanh N. Nguyen, Son D. Le, Ranjithkumar P. Manchan, Panitha Phulkerd, Patchanee Chammingkwan and Toshiaki Taniike

Applied Catalysis A: General, 2023, 666, 119427
DOI: 10.1016/j.apcata.2023.119427

Data-driven Design of Catalytic Materials in Methane Oxidation Based on a Site Isolation Concept

A. Mazheika, M. Geske, M. Müller, S.A. Schunk, F. Rosowski and R. Kraehnert

ACS Catal., 2024, 14, 12297
DOI: 10.1021/acscatal.4c02103