Prior to independent career:
(17) Effects of Ring Size and Steric Encumbrance on Boron-to-Palladium Transmetalation from Arylboronic Esters. Delaney, C.P.; Zahrt, A.F.; Kassel, V.M.; Denmark, S.E. J. Org. Chem. 2024, XXX, XXX-XXX. (link)
(16) Machine Learning to Develop Peptide Catalysts─Successes, Limitations, and Opportunities. Schnitzer, T.;* Schnurr, M.; Zahrt, A.F.;* Sakhaee, N.; Denmark, S.E.; Wennemers, H. ACS Cent. Sci. 2024, 14, 2642-2655. (link)
(15) Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions. Olen C.; Zahrt A.F.; Reilly S.; Schultz D.; Emerson K.; Candito D.; Strotman, N.; Denmark, S.E. ACS Catal. 2024, 14, 2642–2655. (link)
(14) A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings. Rinehart N.I.; Saunthwal R.K.; Wellauer J.; Zahrt A.F.; Schlemper L.; Shved A.S.; Bigler, R.; Fantasia, S.; Denmark, S.E. Science. 2023. 381, 965-972. (link)
(13) Machine-Learning-Guided Discovery of New Electrochemical Reactions. Zahrt, A.F.; Mo, Y.; Nandiwale, K.Y.; Shprints, R.; Heid, E.; Jensen, K.F. J. Am. Chem. Soc. 2022, 144, 22599–22610. (link)
(12) Continuous Stirred-Tank Reactor Cascade Platform for Self-Optimization of Reactions Involving Solids. Nandiwale, K.Y.; Hart, T.; Zahrt, A.F.; Nambiar, A.M.K.; Mahesh, P.T.; Mo, Y.; Nieves-Remacha, M.J.; Johnson, M.D.; García-Losada, P.; Mateos, C.; Rincón, J. A.; Jensen, K.F. React. Chem. Eng. 2022, 7, 1315-1327. (link)
(11) Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality. Rinehart, N.I.; Zahrt, A.F.; Henle, J.J.; Denmark, S.E. CHIMIA. 2021, 75, 592-597. (link)
(10) Dreams, False Starts, Dead Ends and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts. Rinehart, N.I.; Zahrt, A.F.; Henle, J.J.; Denmark, S.E. Acc. Chem. Res. 2021, 54, 2041–2054. (link)
(9) Deciding Which Reactions to Run First and Which to Run Next: Computational Methods for Training Set Selection, Active Learning, and Error Assessment applied to Catalyst Design. Zahrt, A.F.; Rose, B.R, Darrow, T.R.; Henle, J.J.; Denmark, S.E. React. Chem. Eng., 2021, 6, 694-708. (link)
(8) The Conformer-Dependent, Quantitative Quadrant Model. Zahrt, A.F.;* Rinehart, N.I.;* Denmark, S.E. Eur. J. Org. Chem. 2021, 2021, 2343-2354. (link)
(7) Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets. Zahrt, A.F.; Henle, J.J.; Denmark, S.E. ACS Comb. Sci. 2020. 22, 586-591. (link)
(6) Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis. Henle, J.J.;* Zahrt, A.F.;* Rose, B.T.; Darrow, W.T.; Denmark, S.E. J. Am. Chem. Soc. 2020, 142, 11578-11592. (link)
(5) Quantitative Structure-Selectivity Relationships (QSSR)in Enantioselective Catalysis: Past, Present and Future. Zahrt, A.F.; Athavale, S.; Denmark, S.E. Chem Rev. 2020, 120, 1620-1689. (link)
(4) Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis. Zahrt, A.F.; Denmark, S.E. Tetrahedron. 2019, 75, 1841-1851. (link)
(3) Prediction of Higher Selectivity Catalysts by Computer Driven Workflow and Machine Learning. Zahrt, A.F.;* Henle, J.J.;* Rose, B.T.; Wang, Y.; Darrow, W.T.; Denmark, S.E. Science. 2019, 363, eaau5631. (link)
(2) Elucidating the Role of the Boronic Esters in the Suzuki−Miyaura Reaction: Structural, Kinetic, and Computational Investigations. Thomas, A. A; Zahrt, A. F.; Delaney, C. P.; Denmark, S.E. J. Am. Chem. Soc. 2018, 140, 4401-4416. (link)
(1) Structural, Kinetic, and Computational Characterization of the Elusive Arylpalladium(II)boronate Complexes in the Suzuki-Miyaura Reaction. Thomas, A.A.; Wang, H.; Zahrt, A.F.; Denmark, S.E. J. Am. Chem. Soc. 2017, 139, 3805-3821. (link)
*Denotes equal contribution