Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

Cited by (205)

Items 101–200 of 205

1. Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions
   Oyawale Adetunji Moses, Wei Chen, Mukhtar Lawan Adam, Zhuo Wang, Kaili Liu, Junming Shao, Zhengsheng Li, Wentao Li, Chensu Wang, Haitao Zhao et al. · 2021 · Materials Reports Energy
2. Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys
   Chuan Hong Zhou, Jia Yue Zhao, Peng Fei Liu, Jianfu Chen, Sheng Dai, Hua Gui Yang, P. Hu, Haifeng Wang · 2021 · Chemical Science
3. Trends in computational molecular catalyst design
   Ademola Soyemi, Tibor Szilvási · 2021 · Dalton Transactions
4. Surface chemistry dictates stability and oxidation state of supported single metal catalyst atoms
   Konstantinos Alexopoulos, Dionisios G. Vlachos · 2020 · Chemical Science
5. Compact atomic descriptors enable accurate predictions via linear models
   Claudio Zeni, Kevin Rossi, Aldo Glielmo, Stefano de Gironcoli · 2021 · The Journal of Chemical Physics
6. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
   Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo O. Dral · 2023 · The Journal of Chemical Physics
7. Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability
   Gbolade O. Kayode, M. M. Montemore · 2023 · JACS Au
8. Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2
   Junya Ohyama, Airi Hirayama, Nahoko Kondou, Hiroshi Yoshida, Masato Machida, Shun Nishimura, Kenji Hirai, Itsuki Miyazato, KEISUKE G. TAKAHASHI · 2021 · Scientific Reports
9. Expedited Screening of Active and Regioselective Catalysts for the Hydroformylation Reaction
   Matthew D. Wodrich, Michael Busch, Clémence Corminbœuf · 2018 · Helvetica Chimica Acta
10. A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
    Ali Malek, Qianpu Wang, Stefan Baumann, Olivier Guillon, Michael Hermann Eikerling, Kourosh Malek · 2021 · Frontiers in Energy Research
11. The Role of AI in Driving the Sustainability of the Chemical Industry
    Alessandra Toniato, Oliver Tobias Schilter, Teodoro Laino · 2023 · CHIMIA International Journal for Chemistry
12. The CSD and knowledge databases: from answers to questions
    Alexander P. Shevchenko, Roman A. Eremin, Vladislav A. Blatov · 2020 · CrystEngComm
13. Representations and strategies for transferable machine learning improve model performance in chemical discovery
    Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik · 2022 · The Journal of Chemical Physics
14. Data Mining the C−C Cross‐Coupling Genome
    Boodsarin Sawatlon, Matthew D. Wodrich, Benjamin Meyer, Alberto Fabrizio, Clémence Corminbœuf · 2019 · ChemCatChem
15. Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review
    Alicja Mikołajczyk, Uladzislau Zhdan, Sylvain Antoniotti, Adam Smoliński, Karolina Jagiełło, Piotr Skurski, Moussab Harb, Tomasz Puzyn, Jarosław Polański · 2023 · Green Chemistry
16. Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding
    Stuart C. Smith, Christopher S. Horbaczewskyj, Theo F. N. Tanner, Jacob J. Walder, Ian J. S. Fairlamb · 2024 · Digital Discovery
17. A conceptual study of transfer learning with linear models for data-driven property prediction
    Bowen Li, Srinivas Rangarajan · 2021 · Computers & Chemical Engineering
18. Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery
    Nicholas Casetti, Javier Emilio Alfonso Ramos, Connor W. Coley, Thijs Stuyver · 2023 · Chemistry - A European Journal
19. OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion
    François Cornet, Bardi Benediktsson, Bjarke Hastrup, Mikkel Nørgaard Schmidt, Arghya Bhowmik · 2024 · Digital Discovery
20. Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis
    Stefan P. Schmid, Leon Schlosser, Frank Glorius, Kjell Jorner · 2024 · Beilstein Journal of Organic Chemistry
21. Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning
    Sergio Pablo‐García, Albert Sabadell‐Rendón, Ali J. Saadun, Santiago Morandi, Javier Pérez‐Ramírez, Núria López · 2022 · ACS Catalysis
22. On the use of chemical bonding descriptors in machine learning
    Michele Tomasini, Lucia Caporaso, Martí Gimferrer, Albert Poater · 2025 · Coordination Chemistry Reviews
23. Using Classifiers To Predict Catalyst Design for Polyketone Microstructure
    Yin-Pok Wong, Hyuk‐Joon Jung, Shiyun Lin, Matthew Shammami, Hootan Roshandel, Henry M. Dodge, Scott M. Chapp, Lorenzo C. Ruiz De Castilla, Dunwei Wang, Loi H. Do et al. · 2025 · Journal of the American Chemical Society
24. Machine learning corrected alchemical perturbation density functional theory for catalysis applications
    Charles D. Griego, Lingyan Zhao, Karthikeyan Saravanan, John A. Keith · 2020 · AIChE Journal
25. Neuro-genetic machine learning framework accelerates the optimization of Ag/MnOx catalyst for total oxidation of toluene
    Jiaqian Yang, Zhiping Ye, Guanjie Wang, Liang Zhao, Xiuwen Zhang, Jiade Wang, Yufan Jin · 2021 · Applied Catalysis A General
26. Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis
    Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Román‐Leshkov, Heather J. Kulik · 2022 · Journal of Chemical Theory and Computation
27. Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis
    Li‐Cheng Yang, Lu‐Jing Zhu, Shuo‐Qing Zhang, Xin Hong · 2022 · Chinese Journal of Chemistry
28. Exploring chemical space in the search for improved azoheteroarene-based photoswitches
    Sergi Vela, Constantin R. Krüger, Clémence Corminbœuf · 2019 · Physical Chemistry Chemical Physics
29. Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l inear‐scaling relationships to statistical learning techniques
    Sergio Pablo‐García, Rodrigo García‐Muelas, Albert Sabadell‐Rendón, Núria López · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
30. Probing Substrate Scope with Molecular Volcanoes
    Boodsarin Sawatlon, Matthew D. Wodrich, Clémence Corminbœuf · 2020 · Organic Letters
31. Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces
    Philomena Schlexer, Tej Salil Choksi, Verena Streibel, Frank Abild‐Pedersen · 2021 · Physical Chemistry Chemical Physics
32. Reaction development: a student's checklist
    Jasper L. Tyler, Dirk H. Trauner, Frank Glorius · 2025 · Chemical Society Reviews
33. Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**
    Julius Seumer, Jonathan Kirschner Solberg Hansen, Mogens Brøndsted Nielsen, Jan Halborg Jensen · 2023 · Angewandte Chemie
34. Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis
    Eduardo Aguilar-Bejarano, Ender Özcan, Raja K. Rit, Hongyi Li, Hon Wai Lam, Jonathan C. Moore, Simon Woodward, Grazziela Patrocinio Figueredo · 2025 · iScience
35. Grand challenges on accelerating discovery in catalysis
    Alessandra Toniato, Alain C. Vaucher, Teodoro Laino · 2021 · Catalysis Today
36. Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions
    Thanapat Worakul, Rubén Laplaza, Shubhajit Das, Matthew D. Wodrich, Clémence Corminbœuf · 2024 · ACS Catalysis
37. Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms
    Magnus Strandgaard, Julius Seumer, Jan Halborg Jensen · 2024 · Chemical Science
38. Beyond predefined ligand libraries: a genetic algorithm approach for de novo discovery of catalysts for the Suzuki coupling reactions
    Julius Seumer, Jan Halborg Jensen · 2025 · PeerJ Physical Chemistry
39. Modeling the Catalyst Activation Step in a Metal–Ligand Radical Mechanism Based Water Oxidation System
    Nitish Govindarajan, Evert Jan Meijer · 2019 · Inorganics
40. Assessing the persistence of chalcogen bonds in solution with neural network potentials
    Veronika Jurásková, Frédéric Célerse, Rubén Laplaza, Clémence Corminbœuf · 2022 · The Journal of Chemical Physics
41. Genetic Algorithms for the Discovery of Homogeneous Catalysts
    Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Rubén Laplaza, Clémence Corminbœuf · 2023 · CHIMIA International Journal for Chemistry
42. AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
    Morán-González, Lucía, Betten, Jørn Eirik, Kneiding, Hannes, Balcells, David · 2024 · Journal of Chemical Information and Modeling
43. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie
44. Comparative evaluation of artificial neural networks for the performance prediction of Pt‐based catalysts in water gas shift reaction
    Changsu Kim, Jiyong Kim · 2022 · International Journal of Energy Research
45. Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning
    Michael G. Taylor, Aditya Nandy, Connie C. Lu, Heather J. Kulik · 2021 · The Journal of Physical Chemistry Letters
46. A bridge between trust and control: computational workflows meet automated battery cycling
    Peter Kraus, Edan Bainglass, Francisco Ramirez, Enea Svaluto‐Ferro, Loris Ercole, Benjamin Kunz, Sebastiaan P. Huber, Nukorn Plainpan, Nicola Marzari, Corsin Battaglia et al. · 2024 · Journal of Materials Chemistry A
47. Quantum Chemistry Meets Machine Learning
    Alberto Fabrizio, Benjamin Meyer, Raimón Fabregat, Clémence Corminbœuf · 2019 · CHIMIA International Journal for Chemistry
48. Reaction-conditioned generative model for catalyst design and optimization with CatDRX
    Apakorn Kengkanna, Yuta Kikuchi, Takashi Niwa, Masahito Ohue · 2025 · Communications Chemistry
49. Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts
    Kunal M. Lodaya, Nathan D. Ricke, Kelly Chen, Troy Van Voorhis · 2023 · The Journal of Physical Chemistry C
50. Turning chemistry into information for heterogeneous catalysis
    Sergio Pablo‐García, Moises Alvarez Moreno, Núria López · 2020 · International Journal of Quantum Chemistry
51. Machine learning meets quantum mechanics in catalysis
    James P. Lewis, Pengju Ren, Xiaodong Wen, Yongwang Li, GuanHua Chen · 2023 · Frontiers in Quantum Science and Technology
52. Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
    Raimón Fabregat, Alberto Fabrizio, Edgar A. Engel, Benjamin Meyer, Veronika Jurásková, Michele Ceriotti, Clémence Corminbœuf · 2022 · Journal of Chemical Theory and Computation
53. Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions
    Youfu Ma, Xianwei Zhang, Lin Zhu, Xiaowei Feng, Jamal A.H. Kowah, Jun Rong Jiang, Lisheng Wang, Lihe Jiang, Xu Liu · 2023 · Molecules
54. Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction
    Giovanna S. Tâmega, Mateus Oliveira Costa, Ariel de Araujo Pereira, Marco A. B. Ferreira · 2024 · The Chemical Record
55. Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal Model Catalyst Surfaces for Improved Oxygen Reduction Reaction: Machine-Learning-Assisted Exploration
    Yoshihiro Chida, Sae Dieb, Hiraku Masui, Arata Umehara, Keitaro Sodeyama, Toshimasa Wadayama · 2025 · ACS Applied Materials & Interfaces
56. Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors
    Diêgo Ulysses Melo, Leonardo Martins Carneiro, Mauricio Domingues Coutinho-Neto, Paula Homem‐de‐Mello, Fernando H. Bartoloni · 2025 · The Journal of Organic Chemistry
57. AI-guided approaches in organometallic research: cognition in chemistry
    Anshul Yadav, Astha Kamboj, Kavita Poonia · 2026 · Discover Chemistry.
58. Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study
    Qingfu Ye, Yu Zhao, Jun Zhu · 2023 · Artificial Intelligence Chemistry
59. SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data
    Manu Suvarna, Rubén Laplaza, Romain Graux, Núria López, Clémence Corminbœuf, Kjell Jorner, Javier Pérez‐Ramírez · 2025 · ACS Catalysis
60. Uncovering the Activity of Alkaline Earth Metal Hydrogenation Catalysis Through Molecular Volcano Plots
    Shubhajit Das, Bart De Tobel, Mercedes Alonso, Clémence Corminbœuf · 2021 · Topics in Catalysis
61. Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions
    Jingru Lu, David C. Leitch · 2023 · ACS Catalysis
62. Orbital-scale understanding of the surface effect of the MgCl2 in the Ziegler–Natta catalyst for ethylene polymerization: A computational study
    Kefeng Xie, Anping Huang, Pingsheng Zhang, Xiong Wang, Yonghui Zhang, Chunjin Ai · 2024 · Chemical Engineering Science
63. Scaling relationships and volcano plots of homogeneous transition metal catalysis
    Li‐Cheng Yang, Xin Hong · 2020 · Dalton Transactions
64. Combining Hammett σ constants for Δ-machine learning and catalyst discovery
    V. Diana Rakotonirina, Marco Bragato, Stefan Heinen, O. Anatole von Lilienfeld · 2024 · Digital Discovery
65. Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management
    Tingfa Zhou, Chao Hu · 2026 · Environmental Research
66. Toward AI/ML-assisted discovery of transition metal complexes
    Hongni Jin, Kenneth M. Merz · 2024 · Annual reports in computational chemistry
67. Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme
    P. D. Varuna S. Pathirage, Brody Quebedeaux, Shahzad Akram, Konstantinos D. Vogiatzis · 2025 · The Journal of Physical Chemistry A
68. Application of High‐Entropy Materials in Promoting Electrocatalytic Nitrogen Cycle
    Yiwen Su, Shurong Li, Xinzhong Wang, Jiashu Chen, Fujing Xu, Lehlogonolo Rudolf Kanyane, Nicholus Malatji, Jing Yang, Guangping Zheng · 2025 · Small Methods
69. Volcano Plots of Reaction Yields in Cross-Coupling Catalysis
    Jialu Chen, Ruiqin Q. Zhang · 2022 · The Journal of Physical Chemistry Letters
70. Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
    Puck van Gerwen, Matthew D. Wodrich, Rubén Laplaza, Clémence Corminbœuf · 2023 · Machine Learning Science and Technology
71. Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability
    Sagar Ghorai, Ruben Staub, Yu Harabuchi, Takashi Nakano, Alexandre A. Varnek, Satoshi Maeda · 2026 · Journal of Chemical Theory and Computation
72. Machine learning-based yield prediction for transition metal-catalyzed cross-coupling reactions
    C. Rajalakshmi, Vivek Vijay, Abhirami Vijayakumar, Shajila Salim, Sherin Susan Cherian, Parvathi Santhoshkumar, John B Kottooran, Ann Miriam Abraham, G. Krishnaveni, C. S. Anjanakutty et al. · 2024 · Theoretical Chemistry Accounts
73. Hammett-Inspired Product Baseline for Data-Efficient Δ-ML in Chemical Space
    V. Diana Rakotonirina, Marco Bragato, Guido Falk von Rudorff, O. Anatole von Lilienfeld · 2025 · Journal of Chemical Theory and Computation
74. Methoxycyclization of 1,5‐Enynes by Coinage Metal Catalysts: Is Gold Always Superior?
    Matthew D. Wodrich, Clémence Corminbœuf · 2021 · Helvetica Chimica Acta
75. Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems
    José Ferraz-Caetano · 2022 · Current Organocatalysis
76. Density Functional Theories and Coordination Chemistry
    Chantal Daniel · 2020 · Elsevier eBooks
77. Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models
    Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Нугманов Рамиль Ирекович · 2025 · Journal of Cheminformatics
78. Boosting Homogeneous Hydrogenation of CO2 via the Data‐Driven Approach
    Min Zhang, Peng Li, Guangchao Liang · 2025 · Chemistry - An Asian Journal
79. Are we there yet? Adventures on a road trip through machine learning as a computational chemist
    Heather J. Kulik · 2025 · APL Computational Physics
80. Zeolite encapsulated organometallic complexes as model catalysts
    Ethan P. Iaia, Ademola Soyemi, Tibor Szilvási, James W. Harris · 2023 · Dalton Transactions
81. Accessing the usefulness of atomic adsorption configurations in predicting the adsorption properties of molecules with machine learning
    Walter Malone, Johnathan von der Heyde, Abdelkader Kara · 2024 · Physical Chemistry Chemical Physics
82. Improving generative inverse design of molecular catalysts in small data regime
    François Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel Nørgaard Schmidt, Arghya Bhowmik · 2025 · Machine Learning Science and Technology
83. Discovery of Alloy Catalysts for Ammonia Decomposition by Machine Learning-Based Prediction of Adsorption Energies
    Byung Chul Yeo, So Yun Jeong, Jun Su Kim, Dong-Hun Kim · 2024 · 대한금속재료학회지
84. From Subnanometric Clusters toward Single-Atom Catalysts
    Afshan Mohajeri · 2020 · ACS symposium series
85. Method to Construct Volcano Relations by Multiscale Modeling: Building Bridges between the Catalysis and Biosimulation Communities
    Kai Steffen Exner, Anela Ivanova · 2021 · The Journal of Physical Chemistry B
86. Integrated machine learning framework for computer-aided chemical product design
    Qilei Liu, Haitao Mao, Lei Zhang, Linlin Liu, Jian Du · 2021 · Elsevier eBooks
87. Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications
    Jolene P. Reid · 2022 · Elsevier eBooks
88. Machine learning the quantum flux–flux correlation function for catalytic surface reactions
    Brenden Galen Pelkie, Stéphanie Valleau · 2022 · Digital Discovery
89. Cross-coupling catalysts for inactive substrates from a three-dimensional virtual screening
    Arron L. Burnage, Hannes Kneiding, Ainara Nova, David Balcells · 2025 · Journal of Catalysis
90. Accelerated discovery of M 6 @g-N 4 catalysts for CO 2 electroreduction via machine learning and DFT: descriptor engineering and activity trend validation
    Ping Cheng, Hongyang Xu, Xiaoxiao Wang, Xiaoxiang Wang, Tianci Wang, Chenyuan Yu, Wencong Sun, Yun Li, Weijia Huang, Chunguang Chen · 2026 · Physical Chemistry Chemical Physics
91. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold
    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha · 2021 · ChemRxiv
92. Revising Message Passing Graph Neural Networks for Catalyst Design
    Ruslan Yu. Lukin, Maxim Faleev, Rostislav Grigorev · 2022 · ChemRxiv
93. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
    Aditya Nandy, Heather J. Kulik · 2024 · Wiley eBooks
94. Predicting Reactivity with Machine Learning
    Lauriane Jacot‐Descombes, Kjell Jorner · 2025 · Wiley eBooks
95. Trendbericht Physikalische Chemie 2022: In‐situ‐Spektroskopie und Katalyse
    Robert Geitner · 2022 · Nachrichten aus der Chemie
96. Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
    Jon Paul Janet · 2022 · ACS symposium series
97. Computational Modeling of 4d and 5d Transition Metal Catalysts
    Ross James Urquhart, Tell Tuttle · 2023 · Elsevier eBooks
98. Computational design of transition metal catalysts for hydrodefluorination of trifluoromethylarenes using hydrosilane
    Thanapat Worakul, Boodsarin Sawatlon, Panida Surawatanawong · 2024 · Catalysis Science & Technology
99. Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
    Thijs Stuyver, Javier Emilio Alfonso Ramos · 2025 · Wiley eBooks
100. Electronic-Structure Informatics Guided Identification of Actinide/Lanthanide Selectivity Factors
     Tsuyoshi Sumiyoshi, Nahoko Kuroki, Hirotoshi Mori · 2026 · Inorganic Chemistry