BetaCitation Atlas

RESEARCH PAPER · ACS Catalysis · 2022

Mechanistic Inference from Statistical Models at Different Data-Size Regimes

Danilo Machado Lustosa, Anat Milo

Read original paper ↗

See where this research leads.

37 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

Mechanistic Inference from Statistical Models at Different Data-Size RegimesOriginalA Statistical Modeling Approach to Catalyst Generality Assessment in Enantioselective Synthesis3Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands110Data science enabled discovery of a highly soluble 2,2′-bipyrimidine anolyte for application in a flow battery11Machine learning classifies catalytic-reaction mechanisms3Genetic Algorithms for the Discovery of Homogeneous Catalysts14Putting Chemical Knowledge to Work in Machine Learning for Reactivity12Small Data Can Play a Big Role in Chemical Discovery38Small Data Can Play a Big Role in Chemical Discovery7Predicting synthetic viability1Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis43Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity2Prediction of Reaction Orthogonality using Machine Learning1Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions8Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science20Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis1Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis11Computational design of transition metal catalysts for hydrodefluorination of trifluoromethylarenes using hydrosilane0Automated de Novo Design of Olefin Metathesis Catalysts: Computational and Experimental Analysis of a Simple Thermodynamic Design Criterion13PythiaCHEM : a user-friendly machine learning toolkit for chemistry2Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity25Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation1Multi‐Threshold Analysis for Chemical Space Mapping of Ni‐Catalyzed Suzuki‐Miyaura Couplings13Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction8Applying statistical modeling strategies to sparse datasets in synthetic chemistry45Connecting the complexity of stereoselective synthesis to the evolution of predictive tools8Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling1Probability Guided Chemical Reaction Scopes2Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics69Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation in Asymmetric Catalysis6Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence12Approaches to the Interpretation of Machine Learning Models Trained with Big Experimental Kinetic Data: An Example of the Suzuki–Miyaura Reaction0Beyond Тraining Data Volume: Predicting Nonlinear Effects in Catalytic Reactions with Multiple Kinetic Regimes via Machine Learning0Charting the influence of deuterium sources in hydrogen isotope exchange using a cobalt(III) catalytic platform5Synthesis of optically active target molecules0Data-driven modelling of Hoveyda–Grubbs-II-type olefin metathesis precatalyst initiation: mechanistic insights and predictive models0Construction of an Interpretable Regression Model for Yield Prediction and Mechanistic Insight Enabled by Automated Reaction Path Exploration1Machine Learning for Synthetic Organic Chemistry: Methods, Applications, and Best Practices0

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

37 direct citing papers saved.

  1. A Statistical Modeling Approach to Catalyst Generality Assessment in Enantioselective Synthesis

    ChemRxiv · 2022-08-09

    Junshan Lai, Jiajing Li, Isaiah O. Betinol, Yutao Kuang, Jolene P. Reid

    Read original paper ↗ ·

  2. Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

    Journal of the American Chemical Society · 2022-12-27

    Jordan J. Dotson, Lucy van Dijk, Jacob C. Timmerman, Samantha Grosslight, Richard C. Walroth, Francis Gosselin, Kurt Püntener, Kyle A. Mack, Matthew S. Sigman

    Read original paper ↗ ·

  3. Data science enabled discovery of a highly soluble 2,2′-bipyrimidine anolyte for application in a flow battery

    Chemical Science · 2023-01-01

    Adam R. Pancoast, Sara L. McCormack, Shelby Galinat, Ryan Walser-Kuntz, Brianna Jett, Melanie Sarah Sanford, Matthew S. Sigman

    Read original paper ↗ ·

  4. Machine learning classifies catalytic-reaction mechanisms

    Nature · 2023-01-25

    Danilo Machado Lustosa, Anat Milo

    Read original paper ↗ ·

  5. Genetic Algorithms for the Discovery of Homogeneous Catalysts

    CHIMIA International Journal for Chemistry · 2023-02-22

    Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Rubén Laplaza, Clémence Corminbœuf

    Read original paper ↗ ·

  6. Putting Chemical Knowledge to Work in Machine Learning for Reactivity

    CHIMIA International Journal for Chemistry · 2023-02-22

    Kjell Jorner

    Read original paper ↗ ·

  7. Small Data Can Play a Big Role in Chemical Discovery

    Angewandte Chemie International Edition · 2023-03-23

    Hadas Shalit Peleg, Anat Milo

    Read original paper ↗ ·

  8. Small Data Can Play a Big Role in Chemical Discovery

    Angewandte Chemie · 2023-03-23

    Hadas Shalit Peleg, Anat Milo

    Read original paper ↗ ·

  9. Predicting synthetic viability

    Nature Synthesis · 2023-04-11

    Inbal L. Eshel, Anat Milo

    Read original paper ↗ ·

  10. Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis

    Journal of the American Chemical Society · 2023-07-06

    Teresa M. Karl, Samir Bouayad‐Gervais, Julian A. Hueffel, Theresa Sperger, Sebastian Wellig, Sherif James Kaldas, Uladzislava Dabranskaya, Jas S. Ward, Kari T. Rissanen, Graham J. Tizzard, Franziska Schoenebeck

    Read original paper ↗ ·

  11. Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

    ChemRxiv · 2023-08-08

    Alexandre Schoepfer, Rubén Laplaza, Matthew D. Wodrich, Jérôme Waser, Clémence Corminbœuf

    Read original paper ↗ ·

  12. Prediction of Reaction Orthogonality using Machine Learning

    ChemRxiv · 2023-09-29

    Hootan Roshandel, Santiago Vargas, Amy J. Lai, Anastassia N. Alexandrova, Paula L. Diaconescu

    Read original paper ↗ ·

  13. Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions

    ACS Catalysis · 2023-11-21

    Jingru Lu, David C. Leitch

    Read original paper ↗ ·

  14. Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science

    ACS Catalysis · 2023-12-05

    Adrian D. Matthews, Ellyn Peters, John S. Debenham, Qi Gao, Maya D. Nyamiaka, Jianping Pan, Li‐Kang Zhang, Spencer D. Dreher, Shane W. Krska, Matthew S. Sigman, Mycah R. Uehling

    Read original paper ↗ ·

  15. Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

    Angewandte Chemie · 2023-12-28

    Bartosz M. Matysiak, Dean Thomas, Leroy Cronin

    Read original paper ↗ ·

  16. Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

    Angewandte Chemie International Edition · 2023-12-28

    Bartosz M. Matysiak, Dean Thomas, Leroy Cronin

    Read original paper ↗ ·

  17. Computational design of transition metal catalysts for hydrodefluorination of trifluoromethylarenes using hydrosilane

    Catalysis Science & Technology · 2024-01-01

    Thanapat Worakul, Boodsarin Sawatlon, Panida Surawatanawong

    Read original paper ↗ ·

  18. Automated de Novo Design of Olefin Metathesis Catalysts: Computational and Experimental Analysis of a Simple Thermodynamic Design Criterion

    Journal of Chemical Information and Modeling · 2024-01-10

    Marco Foscato, Giovanni Occhipinti, Sondre H. Hopen Eliasson, Vidar Remi Jensen

    Read original paper ↗ ·

  19. PythiaCHEM : a user-friendly machine learning toolkit for chemistry

    ChemRxiv · 2024-01-12

    Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew J. Langton, Fernanda Duarte

    Read original paper ↗ ·

  20. Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

    ACS Catalysis · 2024-06-04

    Alexandre A. Schoepfer, Rubén Laplaza, Matthew D. Wodrich, Jérôme Waser, Clémence Corminbœuf

    Read original paper ↗ ·

  21. Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation

    ChemRxiv · 2024-06-18

    Isaiah O. Betinol, Yutao Kuang, Junshan Lai, Christopher Yousofi, Jolene P. Reid

    Read original paper ↗ ·

  22. Multi‐Threshold Analysis for Chemical Space Mapping of Ni‐Catalyzed Suzuki‐Miyaura Couplings

    European Journal of Organic Chemistry · 2024-09-11

    Austin K. LeSueur, Nari Tao, Abigail G. Doyle, Matthew S. Sigman

    Read original paper ↗ ·

  23. Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction

    The Chemical Record · 2024-11-05

    Giovanna S. Tâmega, Mateus Oliveira Costa, Ariel de Araujo Pereira, Marco A. B. Ferreira

    Read original paper ↗ ·

  24. Applying statistical modeling strategies to sparse datasets in synthetic chemistry

    Science Advances · 2025-01-01

    Brittany C. Haas, Dipannita Kalyani, Matthew S. Sigman

    Read original paper ↗ ·

  25. Connecting the complexity of stereoselective synthesis to the evolution of predictive tools

    Chemical Science · 2025-01-01

    Jiajing Li, Jolene P. Reid

    Read original paper ↗ ·

  26. Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling

    Challenges and advances in computational chemistry and physics · 2025-01-01

    José Ferraz-Caetano, Filipe Teixeira, M. Natália D. S. Cordeiro

    Read original paper ↗ ·

  27. Probability Guided Chemical Reaction Scopes

    SSRN Electronic Journal · 2025-01-01

    Inbal L. Eshel, Shahar Barkai, Sergio Barranco, Mónica H. Pérez‐Temprano, Anat Milo

    Read original paper ↗ ·

  28. Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics

    Chemical Engineering Journal · 2025-02-20

    Leandro Goulart de Araujo, Léa Vilcocq, Pascal Fongarland, Yves Schuurman

    Read original paper ↗ ·

  29. Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation in Asymmetric Catalysis

    ACS Catalysis · 2025-05-09

    Isaiah O. Betinol, Yutao Kuang, Junshan Lai, Christopher Yousofi, Jolene P. Reid

    Read original paper ↗ ·

  30. Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence

    Journal of the American Chemical Society · 2025-07-18

    Julian A. Hueffel, Mathilde Rigoulet, Sebastian Wellig, Theresa Sperger, Jas S. Ward, Kari T. Rissanen, Franziska Schoenebeck

    Read original paper ↗ ·

  31. Approaches to the Interpretation of Machine Learning Models Trained with Big Experimental Kinetic Data: An Example of the Suzuki–Miyaura Reaction

    Kinetics and Catalysis · 2025-10-01

    Alexander F. Schmidt, Anna A. Kurokhtina, Elizaveta V. Larina, Nadezhda A. Lagoda

    Read original paper ↗ ·

  32. Beyond Тraining Data Volume: Predicting Nonlinear Effects in Catalytic Reactions with Multiple Kinetic Regimes via Machine Learning

    Research Square · 2025-10-22

    Alexander F. Schmidt, Anna A. Kurokhtina, Elizaveta V. Larina, Nadezhda A. Lagoda

    Read original paper ↗ ·

  33. Charting the influence of deuterium sources in hydrogen isotope exchange using a cobalt(III) catalytic platform

    Nature Catalysis · 2025-11-25

    Sergio Barranco, Inbal L. Eshel, Jiayu Zhang, Marco Di Matteo, Anat Milo, Mónica H. Pérez‐Temprano

    Read original paper ↗ ·

  34. Synthesis of optically active target molecules

    Elsevier eBooks · 2025-12-12

    Claudio Trombini

    Read original paper ↗ ·

  35. Data-driven modelling of Hoveyda–Grubbs-II-type olefin metathesis precatalyst initiation: mechanistic insights and predictive models

    Catalysis Science & Technology · 2026-01-01

    Henning Remm, Viktoria H. Gessner, Tobias Gensch

    Read original paper ↗ ·

  36. Construction of an Interpretable Regression Model for Yield Prediction and Mechanistic Insight Enabled by Automated Reaction Path Exploration

    Journal of the American Chemical Society · 2026-06-19

    Takahiro Doba, Yu Harabuchi, Yuuya Nagata, Satoshi Maeda

    Read original paper ↗ ·

  37. Machine Learning for Synthetic Organic Chemistry: Methods, Applications, and Best Practices

    Angewandte Chemie Novit · 2026-08-28

    Niklas Hölter, Felix Katzenburg, Florian Boser, Frank Glorius

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