BetaCitation Atlas

RESEARCH PAPER · Chemical Science · 2024

Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

Adarsh V. Kalikadien, Cecile Valsecchi, Robbert van Putten, Tor Maes, Mikko Muuronen, Natalia Dyubankova, Laurent Lefort, Evgeny A. Pidko

Read original paper ↗

See where this research leads.

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

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalystsOriginalCOBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets16Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning0Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes3Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space8Extrapolative-machine-learning-guided discovery of multi-elemental heterogeneous catalysts for low-temperature NO reduction by H21Data Science Meets Ziegler–Natta Catalysis to Design High-Performance Lewis Bases for Isotactic Polypropylene Production3A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity5Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes4Advances in Gasoline Hydrodesulfurization Catalysts: The Role of Structure–Activity Relationships and Machine Learning Approaches13Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach22Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8Metal-catalyzed homogeneous catalytic transfer hydrogenation: A data-driven approach0Machine learning for mechanistic insights and optimization in CO₂ cycloaddition catalysis5Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence2Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 211Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 22Upcycling biomass tar into value-added chemicals via catalytic steam reforming: Mechanisms, catalyst evaluation and perspectives2Capturing stereochemical information with AI: Driving stereoselectivity prediction and rational design in asymmetric catalysis0Concise Total Synthesis of (+)-Shearilicine: A Machine Learning-Assisted Strategy for Ligand Optimization of an Enantioselective Palladium-Catalyzed α-Arylation1Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions0Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation0Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning0Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements0Regulation of Metal Phthalocyanine Performance in Transfer Hydrogenation via Ligand Engineering: Machine Learning‐Assisted Rational Design0Enabling Data-Efficient Machine Learning through Multi-Target Training Set Design via Greedy Neighborhood Coverage0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

26 direct citing papers saved.

  1. COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets

    Cell Reports Physical Science · 2024-12-20

    Zhen Zhu Cao, Laura Falivene, Albert Poater, Bholanath Maity, Ziyung Zhang, Gentoku Takasao, Sadeed Bin Sayed, Andrea Petta, Giovanni Talarico, Romina Oliva, Luigi Cavallo

    Read original paper ↗ ·

  2. Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning

    Digital Discovery · 2025-01-01

    Taline Kerackian, Clément Wespiser, Matthieu Daniel, Éric Pasquinet, Eugénie Romero

    Read original paper ↗ ·

  3. Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes

    Digital Discovery · 2025-01-01

    Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort, Evgeny A. Pidko

    Read original paper ↗ ·

  4. Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space

    ACS Catalysis · 2025-03-31

    Isaiah O. Betinol, Aleksandra Demchenko, Jolene P. Reid

    Read original paper ↗ ·

  5. Extrapolative-machine-learning-guided discovery of multi-elemental heterogeneous catalysts for low-temperature NO reduction by H2

    Research Square · 2025-04-01

    Yuan Jing, Chenyang Zhang, Mine Shinya, Xiupeng Zhang, Chenxi He, Ningqiang Zhang, Xu Ming Guo, Akihiko Anzai, Kohei Oka, Ryo Toyoshima, Hiroshi Kondoh, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

    Read original paper ↗ ·

  6. Data Science Meets Ziegler–Natta Catalysis to Design High-Performance Lewis Bases for Isotactic Polypropylene Production

    ACS Catalysis · 2025-05-02

    Ziyun Zhang, Laura Falivene, Gentoku Takasao, Giampiero Morini, Alessandro Mignogna, Luigi Cavallo

    Read original paper ↗ ·

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

    ACS Catalysis · 2025-05-08

    Sunao Nakanowatari, KEISUKE G. TAKAHASHI, Hieu‐Chi Dam, Toshiaki Taniike

    Read original paper ↗ ·

  8. Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes

    Journal of Chemical Theory and Computation · 2025-05-09

    Sára Finta, Adarsh V. Kalikadien, Evgeny A. Pidko

    Read original paper ↗ ·

  9. Advances in Gasoline Hydrodesulfurization Catalysts: The Role of Structure–Activity Relationships and Machine Learning Approaches

    ACS Omega · 2025-07-18

    Honglei Sun, Chao Chen, Ran Zhang, Yang Li, Shaohui Ge, Peng Cui

    Read original paper ↗ ·

  10. Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach

    Journal of the American Chemical Society · 2025-08-12

    Shirun Zhao, Shinya Mine, Gang Wang, Weiyang Zhang, Abdellah Ait El Fakir, Bin Yang, Zengwei Qin, Nazmul Hasan MD Dostagir, Koichi Matsushita, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

    Read original paper ↗ ·

  11. Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

    Chemistry - An Asian Journal · 2025-08-28

    Rizvi Syed Aal E Ali, Jiaolong Meng, Xuefeng Jiang

    Read original paper ↗ ·

  12. Metal-catalyzed homogeneous catalytic transfer hydrogenation: A data-driven approach

    Molecular Catalysis · 2025-10-29

    Alfredo Pereira, Oleksandra S. Trofymchuk

    Read original paper ↗ ·

  13. Machine learning for mechanistic insights and optimization in CO₂ cycloaddition catalysis

    Applied Catalysis A General · 2025-11-07

    Yasmin Abdul Wahab, Nur’ain Nadia Shapril, Suzaimi Johari, Mohd Rafie Johan

    Read original paper ↗ ·

  14. Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence

    ACS Catalysis · 2025-11-24

    Guillermo Marcos-Ayuso, David Quesada, María Y. Cobos-Abad, Carlos Lendínez, Sara Fernández-Moyano, Pablo Mauleón, Ramón Goméz Arrayás

    Read original paper ↗ ·

  15. Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 2

    ACS Catalysis · 2025-12-04

    Yuan Jing, Chenyang Zhang, Shinya Mine, Xiupeng Zhang, Chenxi He, Ningqiang Zhang, Xu Ming Guo, Akihiko Anzai, Kohei Oka, Ryo Toyoshima, Hiroshi Kondoh, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

    Read original paper ↗ ·

  16. Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 2

    Journal of the American Chemical Society · 2026-04-10

    Yuan Jing, Kah Wei Ting, Junxian Qin, Katsuya Yamada, Daichi Tajima, Akihiko Anzai, Shinya Mine, Ryo Toyoshima, Hiroshi Kondoh, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

    Read original paper ↗ ·

  17. Upcycling biomass tar into value-added chemicals via catalytic steam reforming: Mechanisms, catalyst evaluation and perspectives

    Renewable and Sustainable Energy Reviews · 2026-05-05

    郎茂林, Xuebing Li, Xuebing Li, Mingqian Cheng, Qian Wang, Yanyu Yang, Changnan Mei, Ming Xie, Qihong Cen, Zewei Liu, Bin Li

    Read original paper ↗ ·

  18. Capturing stereochemical information with AI: Driving stereoselectivity prediction and rational design in asymmetric catalysis

    Chinese Chemical Letters · 2026-06-01

    Li Cheng, Pan‐Lin Shao, Songlin Zhao, Bo Zhang, Guichuan Xing

    Read original paper ↗ ·

  19. Concise Total Synthesis of (+)-Shearilicine: A Machine Learning-Assisted Strategy for Ligand Optimization of an Enantioselective Palladium-Catalyzed α-Arylation

    Journal of the American Chemical Society · 2026-06-02

    Fu‐Qiang Ni, Zi Wang, Zhenxun Li, Bing-Feng Zhang, Peng Zhang, Hai‐Hua Lu

    Read original paper ↗ ·

  20. Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions

    ACS Omega · 2026-07-09

    Abhinav Sai Chalasani, Sourodeep Deb, Aarav Anand, Yuhan Li, Robert W. Downing, Edward Njoo

    Read original paper ↗ ·

  21. Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation

    Journal of the American Chemical Society · 2026-07-14

    Pengfei Du, Abdellah Ait El Fakir, El Fakir, Shinya Mine, Bin Yang, Hongli Pan, Chenxi He, Nazmul Hasan, Yuriko Ando, Jörg W. A. Fischer, Akihiko Anzai, Giles Allison, Ryo Toyoshima, Hiroshi KONDOH, Wei Zhou, Christophe Copéret, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

    Read original paper ↗ ·

  22. Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning

    Journal of the American Chemical Society · 2026-07-23

    Nicholas Hadler, Evelyn Ramirez, Matthew Avaylon, Masha Elkin, N. Ian Rinehart, Michael W. Mahoney, Gunther H. Weber, Joscha Hoche, Jan Gerit Brandenburg, Talita Perciano, John F. Hartwig

    Read original paper ↗ ·

  23. Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements

    ChemistrySelect · 2026-09-01

    Jun Maki, Hiromasa Kaneko

    Read original paper ↗ ·

  24. Regulation of Metal Phthalocyanine Performance in Transfer Hydrogenation via Ligand Engineering: Machine Learning‐Assisted Rational Design

    ChemistrySelect · 2026-09-01

    Peng Wu, Dai-Cai Jiang, Ya‐Lin Li, Le‐Ju Li, Hao Ma, Xuefen Liu, Shu‐Ping Luo

    Read original paper ↗ ·

  25. Enabling Data-Efficient Machine Learning through Multi-Target Training Set Design via Greedy Neighborhood Coverage

    ACS Catalysis · 2026-09-28

    Jakob A. Meckes, Jiang Yi He, Jolene P. Reid

    Read original paper ↗ ·

  26. Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

    Industrial & Engineering Chemistry Research · 2026-09-30

    Michael Bâldea, Linda J. Broadbelt, Marianthi Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas Alan Kwan, Fèlix Llovell, Andrew James Medford, Ilias Mitrai, Joel A. Paulson, Junyi Qiao, Matthew P. Rivera, Kirti Chandra Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Ví­ctor M. Zavala, Huacheng Zhang, Dan Zhao

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.