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Year Title Cites
2026 Benchmarking Machine Learning Methods for Predicting Adiabatic Redox Potentials Andrew Zahrt · Scholar only — 2024 Chemoinformatic catalyst selection methods for the optimization of copper–bis (oxazoline)-mediated, asymmetric, vinylogous mukaiyama aldol reactions Andrew Zahrt · ACS Catalysis · Scholar only 22 2024 Effects of Ring Size and Steric Encumbrance on Boron-to-Palladium Transmetalation from Arylboronic Esters Andrew Zahrt · The Journal of Organic Chemistry · Scholar only 8 2024 Evolutionary features for task-specific machine-learning applications Andrew Zahrt · Chem · Scholar only 1 2024 Machine learning to develop peptide catalysts—successes, limitations, and opportunities Andrew Zahrt · ACS Central Science · Scholar only 27 2023 A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings Andrew Zahrt · Science · Scholar only 147 2023 Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed CN Couplings with Substrate Generalizability Andrew Zahrt · ChemRxiv · Scholar only 5 2023 Extrapolative prediction of enantioselectivity enabled by computer-driven workflow, new molecular representations and machine learning Andrew Zahrt · US Patent 11,664 · Scholar only 7 2022 Complex architecture for reaction condition determination Andrew Zahrt · US Patent App · Scholar only — 2022 Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids Andrew Zahrt · Reaction Chemistry & Engineering · Scholar only 54 2022 Machine-learning-guided discovery of electrochemical reactions Andrew Zahrt · Journal of the American Chemical Society · Scholar only 81 2021 A Conformer‐Dependent, Quantitative Quadrant Model Andrew Zahrt · European Journal of Organic Chemistry · Scholar only 18 2021 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 Andrew Zahrt · Reaction Chemistry & Engineering · Scholar only 31 2021 Dreams, false starts, dead ends, and redemption: a chronicle of the evolution of a chemoinformatic workflow for the optimization of enantioselective catalysts Andrew Zahrt · Accounts of chemical research · Scholar only 64 2021 Leveraging machine learning for enantioselective catalysis: from dream to reality Andrew Zahrt · Chimia · Scholar only 9 2020 Cautionary guidelines for machine learning studies with combinatorial datasets Andrew Zahrt · ACS Combinatorial Science · Scholar only 67 2020 Development of a computer-guided workflow for catalyst optimization. Descriptor validation, subset selection, and training set analysis Andrew Zahrt · Journal of the American Chemical Society · Scholar only 102 2020 Quantitative structure–selectivity relationships in enantioselective catalysis: past, present, and future Andrew Zahrt · Chemical reviews · Scholar only 240 2019 Application of Chemoinformatics in Asymmetric Catalysis Andrew Zahrt · Science · Scholar only — 2019 Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis Andrew Zahrt · Tetrahedron · Scholar only 38 2019 Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning Andrew Zahrt · Science 363 (6424), eaau5631 · Scholar only 782 2018 Elucidating the role of the boronic esters in the Suzuki–Miyaura reaction: structural, kinetic, and computational investigations Andrew Zahrt · Journal of the American Chemical Society · Scholar only 192 2017 Structural, kinetic, and computational characterization of the elusive arylpalladium (II) boronate complexes in the Suzuki–Miyaura reaction Andrew Zahrt · Journal of the American Chemical Society · Scholar only 226 Sulfur-Boron Lewis Pair-Catalyzed Hydrohalogenation of Alkynes via Formal Hydrogen Atom Transfer Andrew Zahrt · Angewandte Chemie (International ed. in English) · Scholar only —
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2026 · other

Benchmarking Machine Learning Methods for Predicting Adiabatic Redox Potentials

Andrew Zahrt

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FieldCVOpenAlexScholar
title Benchmarking Machine Learning Methods for Predicting Adiabatic Redox Potentials (used)
year 2026 (used)
kind other (used)
first author Türtscher (used)

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