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RESEARCH PAPER · Bulletin of the Chemical Society of Japan · 2020

Quantum Chemical Reaction Prediction Method Based on Machine Learning

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42 direct citing works

Quantum Chemical Reaction Prediction Method Based on Machine LearningOriginalArtificial intelligence and automation in computer aided synthesis planning88Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest4Nanoarchitectonics on living cells30Progress in Molecular Nanoarchitectonics and Materials Nanoarchitectonics28Organic reactivity from mechanism to machine learning175Fault prediction algorithm for offshore wind energy conversion system based on machine learning5Deep Forest-Based Intelligent Yield Predicting of Buchwald-Hartwig Coupling Reaction4Materials nanoarchitectonics in a two-dimensional world within a nanoscale distance from the liquid phase46Materials Nanoarchitectonics from Atom to Living Cell: A Method for Everything101Bio-interactive nanoarchitectonics with two-dimensional materials and environments62Machine Learning Applications for Chemical Reactions46Mechanistic Inference from Statistical Models at Different Data-Size Regimes37A Capacity-Prediction Model for Exploration of Organic Anodes: Discovery of 5-Formylsalicylic Acid as a High-Performance Anode Active Material14Molecule-to-Material-to-Bio Nanoarchitectonics with Biomedical Fullerene Nanoparticles12Liquid interfacial nanoarchitectonics: Molecular machines, organic semiconductors, nanocarbons, stem cells, and others22Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios27Reaction Impurity Prediction using a Data Mining Approach**9Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks37OCS-TGBM: Intelligent Analysis of Organic Chemical Synthesis Based on Topological Data Analysis and LightGBM1Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2QCD-MLP: A Machine-Learning Framework for Predicting Transformation Products of Organic Micropollutants in Radical-Mediated Systems2An overview of reaction outcome prediction with physics-based and data-driven methods2Mechanistic Insights Into the 1,3‐Dipolar Cycloaddition of Diethyl Phosphorylazidate With Isothiocyanates: A DFT Approach0

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  1. Artificial intelligence and automation in computer aided synthesis planning

    Reaction Chemistry & Engineering · 2020-11-05

    Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean‐Louis Reymond, Ola Engkvist

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  2. 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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  3. Nanoarchitectonics on living cells

    RSC Advances · 2021-01-01

    Katsuhiko Ariga, Rawil F. Fakhrullin

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  4. Progress in Molecular Nanoarchitectonics and Materials Nanoarchitectonics

    Molecules · 2021-03-15

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  5. Organic reactivity from mechanism to machine learning

    Nature Reviews Chemistry · 2021-03-16

    Kjell Jorner, Anna Tomberg, Christoph Alexander Bauer, Christian Sköld, Per‐Ola Norrby

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  6. Fault prediction algorithm for offshore wind energy conversion system based on machine learning

    International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · 2021-12-05

    Jing Yang, Junda Li

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  7. 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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  8. Materials nanoarchitectonics in a two-dimensional world within a nanoscale distance from the liquid phase

    Nanoscale · 2022-01-01

    Katsuhiko Ariga

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  9. Materials Nanoarchitectonics from Atom to Living Cell: A Method for Everything

    Bulletin of the Chemical Society of Japan · 2022-03-22

    Katsuhiko Ariga, Rawil F. Fakhrullin

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  10. Bio-interactive nanoarchitectonics with two-dimensional materials and environments

    Science and Technology of Advanced Materials · 2022-03-25

    Xuechen Shen, Jingwen Song, Cansu Sevencan, David Tai Leong, Katsuhiko Ariga

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  11. Machine Learning Applications for Chemical Reactions

    Chemistry - An Asian Journal · 2022-04-26

    Sanggil Park, Herim Han, Hyungjun Kim, Sunghwan Choi

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  12. Mechanistic Inference from Statistical Models at Different Data-Size Regimes

    ACS Catalysis · 2022-06-17

    Danilo Machado Lustosa, Anat Milo

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  13. A Capacity-Prediction Model for Exploration of Organic Anodes: Discovery of 5-Formylsalicylic Acid as a High-Performance Anode Active Material

    ACS Applied Energy Materials · 2022-07-06

    Takumi Komura, Kosuke Sakano, Yasuhiko Igarashi, Hiromichi Numazawa, Hiroaki Imai, Yuya Oaki

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  14. Molecule-to-Material-to-Bio Nanoarchitectonics with Biomedical Fullerene Nanoparticles

    Materials · 2022-08-05

    Xuechen Shen, Jingwen Song, Kohsaku Kawakami, Katsuhiko Ariga

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  15. Liquid interfacial nanoarchitectonics: Molecular machines, organic semiconductors, nanocarbons, stem cells, and others

    Current Opinion in Colloid & Interface Science · 2022-11-03

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  16. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

    The Journal of Chemical Physics · 2023-01-30

    Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo O. Dral

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  17. Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios

    Journal of Chemical Information and Modeling · 2023-03-23

    Fernando Jaume-Santero, Alban Bornet, Alain Valery, Nona Naderi, David Vicente Alvarez, Dimitrios Proios, Anthony Yazdani, Colin Bournez, Thomas C. Fessard, Douglas Teodoro

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  18. Reaction Impurity Prediction using a Data Mining Approach**

    Chemistry - Methods · 2023-03-27

    Adarsh Arun, Zhen Guo, Simon Sung, Alexei A. Lapkin

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  19. Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

    Advanced Intelligent Systems · 2023-04-21

    Dulyawat Doonyapisut, Padmanathan Karthick Kannan, Byeongkyu Kim, Jung Kyu Kim, Eunseok Lee, Chan‐Hwa Chung

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  20. OCS-TGBM: Intelligent Analysis of Organic Chemical Synthesis Based on Topological Data Analysis and LightGBM

    match Communications in Mathematical and in Computer Chemistry · 2023-12-01

    Yanhui Guo, Lichao Peng, Zixin Li, Mengen Yu, Xue Jiao, Yun Chai, Xiaohui Yang

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  21. Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

    International Journal of Quantum Chemistry · 2025-03-19

    Guo‐Jin Cao

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  22. QCD-MLP: A Machine-Learning Framework for Predicting Transformation Products of Organic Micropollutants in Radical-Mediated Systems

    ACS ES&T Water · 2025-09-18

    Dilhani Senevinanda, Dhimas Dwinandha, Mohamed Elsamadony, Jibao Liu, Mikito Fujinami, Manabu Fujii

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  23. An overview of reaction outcome prediction with physics-based and data-driven methods

    Chemical Society Reviews · 2026-01-01

    Joonyoung F. Joung, Nicholas Casetti, Priyanka Raghavan, Connor W. Coley

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  24. Mechanistic Insights Into the 1,3‐Dipolar Cycloaddition of Diethyl Phosphorylazidate With Isothiocyanates: A DFT Approach

    Journal of Heterocyclic Chemistry · 2026-08-31

    Outaf Fliss, Aicha Zeine, M.A.K. Sanhoury, Nejib Hussein Mekni

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