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RESEARCH PAPER · Chemistry Letters · 2019

Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables

Mikito Fujinami, Junji Seino, Takumi Nukazawa, Shintaro Ishida, Takeaki Iwamoto, Hiromi Nakai

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10 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete VariablesOriginalMachine Learning for Catalysis Informatics: Recent Applications and Prospects613Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction10An Isolable Tetrasilicon Analogue of a Planar Bicyclo[1.1.0]butane with π‑Type Single-Bonding Character57Machine learning in experimental materials chemistry66Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation39Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest4Reduction of a Bicyclo[1.1.0]tetrasil-1(3)-ene with LiAlH4 Leading to an Isolable Cyclotrisilenide9Deep Forest-Based Intelligent Yield Predicting of Buchwald-Hartwig Coupling Reaction4Low-Valent Silicon Compounds2Machine Learning‐Guided Prediction of Hydroformylation2

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  1. Machine Learning for Catalysis Informatics: Recent Applications and Prospects

    ACS Catalysis · 2019-12-16

    Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa, Ken‐ichi Shimizu

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  2. Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction

    Bulletin of the Chemical Society of Japan · 2020-04-20

    Mikito Fujinami, Hiroki Maekawara, Ryota Isshiki, Junji Seino, Junichiro Yamaguchi, Hiromi Nakai

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  3. An Isolable Tetrasilicon Analogue of a Planar Bicyclo[1.1.0]butane with π‑Type Single-Bonding Character

    Journal of the American Chemical Society · 2020-05-07

    Takumi Nukazawa, Takeaki Iwamoto

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  4. Machine learning in experimental materials chemistry

    Catalysis Today · 2020-08-21

    Balaranjan Selvaratnam, Ranjit T. Koodali

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  5. Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation

    Green Chemistry · 2021-01-01

    Masaru Kondo, Akimasa Sugizaki, Md. Imrul Khalid, H. D. P. Wathsala, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Shinobu Takizawa, Hiroaki Sasai

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  6. Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest

    match Communications in Mathematical and in Computer Chemistry · 2021-01-01

    Jing Dong, Xuechun Mu, Zelin Zhang, Yuqing Zhang, Xiaohui Yang, Puyu Zhang

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  7. Reduction of a Bicyclo[1.1.0]tetrasil-1(3)-ene with LiAlH4 Leading to an Isolable Cyclotrisilenide

    Organometallics · 2021-10-15

    Takumi Nukazawa, Takeaki Iwamoto

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  8. Deep Forest-Based Intelligent Yield Predicting of Buchwald-Hartwig Coupling Reaction

    match Communications in Mathematical and in Computer Chemistry · 2022-01-01

    Xuechun Mu, Jing Dong, Lichao Peng, Xiaohui Yang

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  9. Low-Valent Silicon Compounds

    Elsevier eBooks · 2022-01-01

    Shiori Fujimori, Shigeyoshi Inoue

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  10. Machine Learning‐Guided Prediction of Hydroformylation

    ChemPhysChem · 2024-10-29

    Haonan Shi, Chaoren Shen, Chaoren Shen, Zheng Huang, Zheng Huang, Kaiwu Dong

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