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Sunao Nakanowatari

Observed name: Nakanowatari, Sunao
ORCID: 0009-0007-3156-0421
Observed ORCID: 0009-0007-3156-0421
OpenAlex ID: A5060596474
Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis
2024 · Toshiaki Taniike, Aya Fujiwara, et al. · Communications Chemistry · 60
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Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane
2021 · Thanh Nhat Nguyen, Sunao Nakanowatari, et al. · ACS Catalysis · 54
Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane
2022 · Sora Ishioka, Aya Fujiwara, et al. · ACS Catalysis · 51
Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts
2021 · Lauren Takahashi, Thanh Nhat Nguyen, et al. · Chemical Science · 26
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Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane
2021 · Sunao Nakanowatari, Thanh Nhat Nguyen, et al. · ChemCatChem · 23
Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning
2020 · Kanami Sugiyama, Thanh Nhat Nguyen, et al. · ChemCatChem · 21
Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts
2021 · Keisuke Takahashi, Jun Fujima, et al. · The Journal of Physical Chemistry Letters · 12
Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane
2021 · Thanh Nhat Nguyen, Kalaivani Seenivasan, et al. · Molecular Catalysis · 11
Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering
2025 · Aya Fujiwara, Sunao Nakanowatari, et al. · Science and Technology of Advanced Materials · 6
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A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity
2025 · Sunao Nakanowatari, Keisuke Takahashi, et al. · ACS Catalysis · 5
High-Throughput Experimentation Platform for Hydroxide Electrodeposition and Electrochemical Evaluation of Electrocatalysts
2026 · Sunao Nakanowatari, Koichiro Nakamura, et al. · ACS electrochemistry.
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Iterative representation learning organizes a vast catalytic chemical space for efficient catalyst discovery
2026 · Toshiaki Taniike, Wentao Du, et al. · ChemRxiv
A high-throughput experimentation platform for hydroxide electrodeposition and electrochemical evaluation of electrocatalysts
2026 · Sunao Nakanowatari, Koichiro Nakamura, et al. · ChemRxiv
High-Throughput Experimentation on Oxide Photocatalyst: From Large-Scale Screening to Kinetic Insights
2026 · Yohei Cho, Kyo Yanagiyama, et al. · ChemRxiv
High-Throughput Experimentation on Oxide Photocatalyst: From Large-Scale Screening to Kinetic Insights
2026 · Yohei Cho, Kyo Yanagiyama, et al. · ChemRxiv
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Element-dependent charge-supply and charge-transfer balance in oxide photocatalysts identified by high-throughput kinetic screening
2026 · Yohei Cho, Kyo Yanagiyama, et al. · Journal of Materials Chemistry A
Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data
2025 · Sunao Nakanowatari, Toshiaki Taniike · The Journal of Physical Chemistry Letters
Learning Catalyst Design Based on Bias-free Dataset for Oxidative Coupling of Methane
2021 · Sunao Nakanowatari · The Chemical Society of Japan
Works count: 20
Citation count: 269
H-index: 8
I10-index: 8
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