Andrew F. Zahrt

University of Illinois Urbana-Champaign — United States

Materials Science · Physical Sciences

13h-index1.8kcitations25works8.82yr avg
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Research Topics

Machine Learning in Materials Science(11), Asymmetric Hydrogenation and Catalysis(10), Computational Drug Discovery Methods(9), Catalytic Cross-Coupling Reactions(6), Catalytic C–H Functionalization Methods(5)

Publications25 total

Evolutionary features for task-specific machine-learning applications
Chem·2024· 1 cited
Effects of Ring Size and Steric Encumbrance on Boron-to-Palladium Transmetalation from Arylboronic Esters
The Journal of Organic Chemistry·2024· 4 cited
Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions
ACS Catalysis·2024· 13 cited
Machine Learning to Develop Peptide Catalysts─Successes, Limitations, and Opportunities
ACS Central Science·2024· 17 citedOpen Access
A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings
Science·2023· 108 cited
Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions
2023· 3 citedOpen Access
Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions
2023Open Access
Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed C-N Couplings with Substrate Generalizability
2023· 1 citedOpen Access
Machine-Learning-Guided Discovery of Electrochemical Reactions
Journal of the American Chemical Society·2022· 66 citedOpen Access
Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed C-N Couplings with Substrate Generalizability
2022· 3 citedOpen Access
Machine Learning-Guided Discovery of New Electrochemical Reactions
2022Open Access
Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids
Reaction Chemistry & Engineering·2022· 49 citedOpen Access
Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality
CHIMIA International Journal for Chemistry·2021· 6 citedOpen Access
A Conformer‐Dependent, Quantitative Quadrant Model
European Journal of Organic Chemistry·2021· 9 cited
Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts
Accounts of Chemical Research·2021· 56 cited
Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next
Reaction Chemistry & Engineering·2021· 22 cited
Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next
2020Open Access
Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next
2020· 1 citedOpen Access
Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets
ACS Combinatorial Science·2020· 58 cited
Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis
Journal of the American Chemical Society·2020· 94 cited
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Frequent Co-authors

Scott E. Denmark(21), Jeremy Henle(7), N. Ian Rinehart(6), Brennan T. Rose(3), William T. Darrow(3), Rakesh K. Saunthwal(3), Joël Wellauer(3), Lukas Schlemper(3), Alexander S. Shved(3), Raphael Bigler(3), Serena Fantasia(3), Yiming Mo(3), Kakasaheb Y. Nandiwale(3), Klavs F. Jensen(3), Casey L. Olen(3), Sean W. Reilly(3), Danielle M. Schultz(3), Khateeta M. Emerson(3), David A. Candito(3), Neil A. Strotman(3)