On the Origin of Scission Selectivity for Phosphoranyl Radicals in Photoredox Catalysis
Wearing, E. R.; Kaushik, N. P.; Hugie, S. T.; Sigman, M. S.; Doyle, A. G.
Chemical Catalysis, Photochemistry, MLR
Catalytic Enantioselective Aziridination of Z-Disubstituted Alkenes with Aspartyl β-Turn-Based Dirhodium(II) Metallopeptides and Assessment of Catalyst Scaffold Versatility Copy
Pighetti, M.; Miller, B. R.; Ehrlacher, A. K.; Sigman, M. S.; Miller, S. J.
Chemical Catalysis
Using Data Science Tools to Explore Rate Matching in a Nickel-Catalyzed Cross-Electrophile Coupling of Alkyl and Aryl Halides (Cl, Br) with a Tridentate Monoanionic Ligand Copy
Takenaka, H.; Ring, A. J.; Hazra, H.; Wild, T. H.; Powell, S. M. R.; Tang, T.; Reisman, S. E.; Sigman, M. S.
MLR, Electrocatalysis
Machine Learning-Guided Scope Selection to Balance Performance and Substrate Similarity Copy
Roediger, S.; Sigman, M. S.; Doyle, A. G.
Machine Learning
Direct Access to Chiral Piperidines by Enantioselective HAT-Initiated C(sp3)–H Oxidation Copy
Muthuramalingam, S.; Call, A.; Lefebvre, Q.; Sigman, M. S.; Costas, M.
Chemical Catalysis
Understanding Selectivities in Gold(I)-Catalyzed Intramolecular C(sp3)–H Bond Functionalization through Predictive Statistical Analysis
Arto, O.; Gallarati, S.; Hinge, S.; Miguélez, R.; Merino, I.; Niemeyer, J.; Sigman, M. S.; Barrio, P.
Data-Driven Predictions of Diastereoselectivity in Crystallization-Induced Diastereomer Transformations
McCormack, S. L.; Sherman, E. R.; Johnson, J. S.; Sigman, M. S.
MLR
BUNNY: An N,N-Bidentate Nitrogen Ligand Descriptor Library Development and Application to Modeling of Ni-Catalyzed Asymmetric Cross-Electrophile Coupling Reactions
Gutiérrez-Valencia, N. E.; Schleinitz, J.; Wild, T. H.; Williams, W. L.; Mantin, D.; Doyle, A. G.; Reisman, S. E.; Sigman, M. S.**
Chemical Catalysis, Machine Learning
Catalytically promiscuous PLP-dependent aminotransferases are biocatalysts for C–C bond formation
Kim, A. T.; Howard, J. R.; Cuba Caceres, A. G.; Chong, K. I.; Aye, W. A.; Sigman, M. S.; Narayan, A. R. H.
ACS Cent. Sci. 2026, 12 (7): 1029–1037.
Biocatalysis
Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning
Dalmau, D.; Jacot-Descombes, L.; Kalikadien, A.; Manzanilla, B.; Pidko, E. A.; Jorner, K.; Sigman, M. S.; Alegre-Requena, J. V.
Machine Learning
Assessment of Complementary Catalysts in an Uncharted Enantioselective Reaction of Sulfondiimines
Gaster, E.; Slocumb, H. S.; Jurić, Z.; Myers, T. E.; Lim, N.-K.; Malig, T. C.; Toste, F. D.; Gosselin, F.; Sigman, M. S.; Miller, S. J.
Chemical Catalysis
Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research
LeSueur, A.; Bianchi, P.; Gallarati, S.; Lefave, S. J.; Sigman, M. S.
MLR
Addressing Steric and Electronic Limitations in Nickel Photoredox C–N Cross-Coupling Reactions
Ghosh, I.; Düker, J.; Pillitteri, S.; Dellisanti, A.; Feuerer, P.; Philipp, M.; Wild, T.; Johansson, M. J.; Gschwind, R. M.; Sigman, M. S.; König, B.
Chemical Catalysis
Using Data Science and Experimental Tools to Determine Molecular Design Principles for Commercial Quinone Posolytes
McCormack, S. L.; Dong, J.; Kwabi, D. G.; Sigman, M. S.
Batteries, MLR
Transferable enantioselectivity models from sparse data
Gallarati, S.; Bucci, E. M.; Doyle, A. G.; Sigman, M. S.
Chemical Catalysis, Machine Learning
Data Science and High-Throughput Spectroelectrochemistry Guided Interrogation of Sulfonate Anions for OMIECs
Collins, G. W.; McCormack, S. L.; Forthman, K. S.; Sigman, M. S.; Bischak, C. G.
MLR, HTE
Ligand-Controlled Chemodivergent Bismuth Catalysis
Mele, L.; Engel, P. D.; Cadge, J. A.; Peciukenas, V.; Choi, H.; Sigman, M. S.; Cornella, J.
J. Am. Chem. Soc. 2025, 147, 46, 42406–42415
Chemical Catalysis
Introduction: Weak Interactions in Chemistry and Biology
Milo, A.; Sigman, M. S.; Jacobsen, E. N.
Review
Bisphosphine ligand conformer selection to enhance descriptor database representation: improving statistical modeling outcomes
Cadge, J. A.; Hart, S. D.; Walroth, R. C.; Mack, K. A.; Sigman, M. S.
Chemical Catalysis, MLR
Experimental Lineage and Computational Analysis of a General Aminoxyl-Based Oxidation Catalyst: Generality from Substrate-Specific Interactions
Rozema, S. D.; Tampellini, N.; Rein, J.; Sigman, M. S.; Lin, S.; Miller, S. J.
Transition States, Chemical Catalysis
Development of a Heteroleptic Palladium Catalyst for the Fluorination of Arylboronic Acids using Data Science Tools
Deolka, S.; Dalmau, D.; Garcia Roca, A.; Vasylecskyi, S.; Samha, M. H.; Sigman, M. S.
Chemical Catalysis, Machine Learning
Enantioselective Synthesis of Complex Carbocycles by C–H Insertion of Aryl/Aryl Carbenes
Ruiz, J. M.; Turner, D. R.; Gao, M.; Guo, W.; Shahvali, T.; Andrade, E. J.; Kong, W.; Miller, B, R.; Fettinger, J. C.; Sigman, M. S.; Tantillo, D. J.; Shaw, J. T.
Chemical Catalysis, Transition States
Pd-catalyzed desulfonylative fluorination of electron-deficient (hetero)aryl sulfonyl fluorides
Hall, J. R.; Romer, N. P.; Spiller, T. E.; Sigman, M. S.; Sanford, M. S.
Machine Learning, Chemical Catalysis
General Access to Chiral Piperidines via Enantioselective Catalytic C(sp3)-H Oxidation using Manganese Catalysts
Muthuramalingam, S.; Call, A.; Lefebvre, Q.; Sigman, M. S.; Costas, M.
Chemical Catalysis, MLR
A Data Science-Guided Approach for the Development of Nickel-Catalyzed Homo-Diels–Alder Reactions
Cadge, J. A.; Lozano, C.; Merriman, M. T.; Oblad, P.; Sigman, M. S.; Reisman, S. E.
J. Am. Chem. Soc. 2025, 147, 34, 31175-31186.
Machine Learning, Chemical Catalysis
DNA-Scaffolded Ultrahigh-Throughput Reaction Screening
Cox, C.; Pimentel, E.; Miller, B.; Nesbitt, D.; LeMonds, J.; Hartman, E.; Hancock, T.; Kennedy, R.; Coon, J.; Sigman, M.; Martell, J.
Machine Learning
Considerations in Pursuing Reaction Scope Generality
Zacate, S. B.; Dantas, J. A.; Lin, S.; Doyle, A.G; Sigman, M. S.
Review
Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive N-Arylations of Cyclic α-Substituted Amines in Aqueous Media
Ickes, A. R.; Liles, J. P.; Borlinghaus, N.; Henle, J.; Swiatowiec, R.; Prakash Kaushik, N.; Braje, W. M.; Harper, K. C.; Shekhar, S.; Sigman, M. S.
Chemical Catalysis, Machine Learning, HTE
Shaping of a Reactive Manganese Catalyst Enables Access to Polyfunctionalized Cyclohexanes via Enantioselective C(sp3)‒H Bond Oxidation of 1,3-meso Diethers
Palone, A.; Call, A.; Garcia-Roca, A.; Luis, J. M.; Sigman, M. S.; Bietti, M.; Nevado, C.; Miquel Costas, M.
Chemical Catalysis, MLR
Machine learning workflows beyond linear models in low-data regimes
Dalmau, D.; Sigman, M. S.; Alegre-Requena, J. V.
Machine Learning
Data Science-Guided Development of Deoxyfluorination Reagents with Enhanced Reactivity, Practicality, and Safety
Ruos, M.; Romer, N.; Deichert, J.; Alabanza, L. M.; Gandhi, S.; Brown, G.; Walroth, R.; Cruz, K.; Gosselin, F.; Hong, A.; Sigman, M. S.; Doyle, A.
Chemical Catalysis, Machine Learning
Investigating Reactivity and Selectivity in a Palladium-Catalyzed Heteroleptic Ligand System for Electrophilic Arene Fluorination
Deolka, S.; Samha, M. H.; Garcia Roca, A.; Haug, G. C.; Howard, J. R.; Dalmau, D.; Sandres, J.; Vasylevskyi, S.; VanderLinden, R. T.; Paton, R. S.; Sigman, M. S.
Machine Learning, Chemical Catalysis
Macrocycles of Saxitoxin: Insights into the Structure of Zetekitoxin AB
Li, W.; Paladugu, S. R.; Liles, J. P.; Karthikeyan, M.; Chase, K.; Raghuraman, S.; Sigman, M. S.; Looper, R. E.
MLR
Data-Driven Workflow for the Development and Discovery of N-Oxyl Hydrogen Atom Transfer Catalysts
Yang, C.; Wild, T.; Rakova, Y.; Maldonado, S.; Sigman, M. S.; Stephenson, C. R. J.
Machine Learning, Chemical Catalysis
Predicting the stability of base-mediated C–H carboxylation adducts using data science tools
Eckhoff, M.; Garcia-Roca, A.; Deolka, S.; Seidel, L.; Sigman, M. S.; Proppe, J.
Angew. Chem. Int. Ed., 2026, 65, 2, e04934.
MLR, Chemical Catalysis
Development of a Thioetherification of Heteroarene Electrophiles with Broad Scope via a Proton Transfer Dual Ionization Mechanism
Zwick, C. R.; Eckhoff, M.; Henle, J. J.; Bay, A. V.; Swiatowiec, R.; Shekhar, S.; Yang, C.; Shou, Y.; Wall, A. L.; Hentry, R. F.; Hoskins, J. N.; Sigman, M. S.
Machine Learning
Understanding Catalytic Enantioselective C–H Bond Oxidation at Nonactivated Methylenes Through Predictive Statistical Modeling Analysis
Call, A.; Palone, A.; Liles, J. P.; Romer, N. P.; Read, J. A.; Luis, J. M. Sigman, M. S.; Bietti, M.; Costas, M.
Chemical Catalysis, Machine Learning
Interrogation of Enantioselectivity in the Photomediated Ring Contractions of Saturated Heterocycles
Kim, S. F.; Liles, J. P.; Lux, M. C.; Park, H.; Jurczyk, J.; Soda, Y.; Yeung, C. S.; Sigman, M. S.; Sarpong, R.
Photochemistry, MLR
Applying statistical modeling strategies to sparse datasets in synthetic chemistry
Haas, B. C.; Kalyani, D.; Sigman, M. S.
Review
Chemical education in digital chemistry
Fung, F. M.; Lederbauer, M.; Choo, Y. S. L.; Gehring, T.; Jablonka, K. M.; Jorner, K.; Schwaller, P.; Sullivan M. B.; Volkamer, A.; Sigman, M. S.; Liao, K.; Windle, C.
Review
Computational Library Enables Pattern Recognition of Noncovalent Interactions and Application as a Modern Linear Free Energy Relationship
Read, J. A.; Ball, T. E.; Miller, B. R.; Jacobsen, E. N.; Sigman, M. S.
MLR, Chemical Catalysis
Cheminformatic Analysis of Core-Atom Transformations in Pharmaceutically Relevant Heteroaromatics
Bartholomew, G. L.; Karas, L. J.; Eason, R. M.; Yeung, C. S.; Sigman, M. S.; Sarpong, R.
Ni-Catalyzed Enantioselective Desymmetrization: Development of Divergent Acyl and Decarbonylative Cross-Coupling Reactions
Hernandez-Mejias, A.; Shimozono, A.; Hazra, A.; Richter, S.; Tong, Z.; Langille, N.; Quasdof, K.; Parsons, A.; Sigman, M.; Reisman, S.
Electrocatalysis
SMARTpy: A Python Package for the Generation of Cavity-Specific Steric Molecular Descriptors and Applications to Diverse Systems
Miller, B.; Cammarota, R.; Sigman, M.
Chemical Catalysis
Multi-Threshold Analysis for Chemical Space Mapping of Ni-Catalyzed Suzuki-Miyaura Couplings
LeSueur, A.; Tau, N.; Sigman, M.
Chemical Catalysis, Machine Learning
Cross-coupling reactions with nickel, visible light, and tert-butylamine as a bifunctional additive
Duker, J.; Philipp, M.; Lentner, T.; Cadge, J.; Lavarda, J.; Gshwind, R.; Sigman, M. S.; Ghosh, I.; Koenig, B.
MLR, Chemical Catalysis
Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines
Feng, K.; Raguram, E. R.; Howard, J. R.; Peters, E.; Liu, C.; Sigman, M. S.; Buchwald, S. L.
J. Am. Chem. Soc. 2024, 146, 39, 26609-26615.
Chemical Catalysis
Small-molecule properties define partitioning into biomolecular condensates
Thody, S. A.; Clements, H. D.; Baniasadi, H.; Lyon, A. S.; Sigman, M. S.
Machine Learning
Modular synthesis of aryl amines from 3-alkynyl-2-pyrones
Gardner, K. E.; Lescure, L.; Hardy, M. A.; Tan, J.; Sigman, M. S.; Paton, R. S.; Sarpong, R.
MLR
Development of Modular Nitrenium Bipolar Electrolytes for Possible Applications in Symmetric Redox Flow Batteries
Varenikov, A.; Gandelman, M.; Sigman, M. S.
J. Am. Chem. Soc. 2024, 146, 28, 19474-19488,
Batteries, MLR
Tricyclononenes and tricyclononadienes as efficient monomers for controlled ROMP: understanding structure–propagation rate relationships and enabling facile post-polymerization modification
Kilgallon, L. J.; McFadden, T. P.; Sigman, M. S.; Johnson, J. A.
Polymers, MLR
Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization
McFadden, T. P.; Cope, R. B.; Muhlestein, R.; Layton, D. J.; Lessard, J. J.; Moore, J. S.; Sigman, M. S.
J. Am. Chem. Soc. 2024, 146, 24, 16375-16380.
Polymers, MLR
Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation
Sheng, H.; Sun, J.; Rodriguez, O.; Hoar, B.; Zhang, W.; Xiang, D.; Tang, T.; Hazra, A.; Min, D.; Doyle, A.; Sigman, M.; Costentin, C.; Gu, Q.; Rodriguez-Lopez, J.; Liu, C.
Electrocatalysis
Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes
Romer, N. P.; Min, D. S.; Wang, J. Y.; Walroth, R. C.; Mack, K. A.; Sirois, L. E.; Gosselin, F.; Zell, D.; Doyle, A. G.; Sigman, M. S.
Chemical Catalysis, MLR
Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines
Haas, B. C.; Hardy, M. A.; Sowndarya S. V., S.; Adams, K.; Coley, C. W.; Paton, R. S.; Sigman, M. S.
Digital Discovery, 2025, 4, 222-223.
MLR
Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science
Matthews, A. D.; Peters, E.; Debenham, J. S.; Gao, Q.; Nyamiaka, M. D.; Pan, J.; Zhang, L.; Dreher, S. D.; Krska, S. W.; Sigman, M. S.; Uehling, M. R.
MLR, Chemical Catalysis
Electrocatalytic Asymmetric Nozaki–Hiyama–Kishi Decarboxylative Coupling: Scope, Applications, and Mechanism
Gao, Y.; Jiang, B.; Friede, N. C.; Hunter, A. C.; Boucher, D. G.; Minteer, S. D.; Sigman, M. S.; Reisman, S. E.; Baran, P. S.
Electrocatalysis
Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling
Akana, M. E.; Tcyrulnikov, S.; Akana-Schneider, B. D.; Reyes, G. P.; Monfette, S.; Sigman, M. S.; Hansen, E. C.; Weix, D. J.
J. Am. Chem. Soc. 2024, 146, 5, 3043-3051.
MLR, Chemical Catalysis
Predicting success in Cu-catalyzed C–N coupling reactions using data science
Samha, M.; Karas, L.; Vogt, D.; Odogwu, E.; Elward, J.; Crawford, J.; Steves, J.; Sigman, M.
Chemical Catalysis, HTE
Deconvoluting Nonlinear Catalyst–Substrate Effects in the Intramolecular Dirhodium-Catalyzed C–H Insertion of Donor/Donor Carbenes Using Data Science Tools
Souz, L. W.; Miller, B. R.; Cammarota, R. C.; Lo, A.; Lopez, I.; Shiue, Y.; Bergstrom, B. D.; Dishman, S. N.; Fettinger, J. C.; Sigman, M. S.; Shaw, J. T.
MLR, Chemical Catalysis
Synthesis of Dihydropyrazoles via Palladium-Catalyzed Cascade Heterocyclization/Carbonylation/Arylation of β,γ-Unsaturated N-Tosyl Hydrazones
Dantas, J. A.; Hardy, M. A.; Costa, M. O.; De Oliveira, C. P.; Cabral, T. L. G.; Tormena, C. F.; Sigman, M. S.; Ferreira, M. A. B.
Chemical Catalysis, MLR
Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation
Haas, B. C.; Lim, N.-K.; Jermaks, J.; Gaster, E.; Guo, M. C.; Malig, T. C.; Werth, J.; Zhang, H.; Toste, F. D.; Gosselin, F.; Miller, S. J.; Sigman, M. S
Chemical Catalysis, MLR
Conformation Dependent Features of Bisphosphine Ligands
Stenfors, B. A.; Cadge, J. A.; Aikonen, S.; Luchini, G.; Wahlers, J.; Koh, K.; Muuronene, M.; Menche, M.; Pfeifle, M.; Keto, A.; Paton, R. S.; Sigman, M. S.; Weist, O.
MLR, Chemical Catalysis
Dataset Design for Building Models of Chemical Reactivity
Raghavan, P.; Haas, B. C.; Ruos, M. E.; Schleinitz, J.; Doyle, A. G.; Reisman, S. E.; Sigman, M. S.; Coley, C. W.
Review
Data science enabled discovery of a highly soluble 2,2′-bipyrimidine anolyte for application in a flow battery
Pancoast, A. R.; McCormack, S. L.; Galinat, S.; Walser-Kuntz, R.; Jett, B. M.; Sanford, M. S.; Sigman, M. S.
Batteries, MLR
14N to 15N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing
Bartholomew, G. L.; Kraus, S. L.; Karas, L. J.; Carpento, F.; Bennett, R.; Sigman, M. S.; Yeung, C. S.; Sarpong, R.
Chemical Catalysis
An Asymmetric Aromatic Finkelstein Reaction: A Platform for Remote Diarylmethane Desymmetrization
Morack, T.; Myers, T. E.; Karas, L. J.; Hardy, M. A.; Mercado, B. Q.; Sigman, M.S.; Miller, S. J.
Chemical Catalysis, MLR
Identifying structure-function relationships to modulate crossover in nonaqueous redox flow batteries
Jett, B.; Flynn, A.; Sigman, M. S.; Sanford, M. S.
Batteries, MLR
Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides
van Dijk, L.; Haas, B. C.; Lim, N.-K.; Clagg, K.; Dotson, J. J.; Treacy, S. M.; Piechowicz, K. A.; Roytman, V. A.; Zhang, H.; Toste, F. D.; Miller, S. J.; Gosselin, F.; Sigman, M. S.
J. Am. Chem. Soc. 2023, 145, 38, 20959–20967.
Chemical Catalysis
Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction
Clements, H. D.; Flynn, A. R.; Nicholls, B. T.; Grosheva, D.; Lefave, S. J.; Merriman, M. T.; Hyster, T. K.; Sigman, M. S.
J. Am. Chem. Soc. 2023, 145, 32, 17656-17664.
Biocatalysis
Catalyst-Controlled Enantioselective and Regiodivergent Addition of Aryl Boron Nucleophiles to N-Alkyl Nicotinate Salts
Ortiz, K. G.; Dotson, J. J.; Robinson, D. J.; Sigman, M. S.; Karimov, R. R.
MLR, Chemical Catalysis
Generality-oriented optimization of enantioselective aminoxyl radical catalysis
Rein, J.; Rozema, S. D.; Langner, O. C.; Zacate, S. B.; Hardy, M. A.; Siu, J. C.; Mercado, M. Q.; Sigman, M. S.; Miller, S. J.; Lin, S.
MLR, Chemical Catalysis
A Physical Organic Chemistry Approach to Developing Cyclopropenium-Based Energy Storage Materials for Redox Flow Batteries
Walser-Kuntz, R.; Yan, Y.; Sigman, M. S.; Sanford, M. S.
Batteries
Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques
Tang, T.; Hazra, A.; Min, D. S.; Williams, W. L.; Jones, E.; Doyle, A. G.; Sigman, M. S.
Electrocatalysis, MLR
Data science enables the development of a new class of chiral phosphoric acid catalysts
Liles, J. P.; Rouget-Virbel, C.; Wahlman, J. L. H.; Rahimoff, R.; Crawford, J. M.; Medlin, A.; O'Connor, V. S.; Li, J.; Roytman, V. A.; Toste, F. D.; Sigman, M. S.
Chemical Catalysis, MLR
A Physical Organic Approach towards Statistical Modeling of Tetrazole and Azide Decomposition
Rein, J.; Meinhardt, J. M.; Hoftstra Wahlman, J. L.; Sigman, M. S.; Lin, S.
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Gensch, T; dos Passos Gomes, G.; Friederich, P.; Peters, E.; Gaudin, T.; Pollice, R.; Jorner, K.; Nigam, A. Lindner-D'Addario; M.; Sigman, M. S.*; Aspuru-Guzik, A.*
MLR, Chemical Catalysis
Catalytic asymmetric C–H insertion reactions of vinyl carbocations
Nistanaki, S. K.; Williams, C. G.; Wigman, B.; Wong, J. J.; Haas, B. C.; Popov, S.; Werth, J.; Sigman, M. S.*; Houk, K.N.*; Nelson, H. M.*
Chemical Catalysis, MLR
Benzotriazoles as Low-Potential Anolytes for Non-aqueous Redox Flow Batteries
Yan, Y.; Zhang, L.; Wasler-Kuntz, R.; Vogt, D. B.; Sigman, M. S.; Yu, G.*; Sanford, M. S.*
Batteries
Exploring Structure–Function Relationships of Aryl Pyrrolidine-Based Hydrogen-Bond Donors in Asymmetric Catalysis Using Data-Driven Techniques
Samha, M. H.; Wahlman, J. L. H.; Read, J. A.; Werth, J.; Jacobsen, E. N.; and Sigman, M. S.*
MLR, Chemical Catalysis
Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions
Ryan C. Cammarota, Wenbin Liu, John Bacsa, Huw M. L. Davies*, and Matthew S. Sigman*
MLR, Chemical Catalysis
Atroposelective Negishi Coupling Optimization Guided by Multivariate Linear Regression Analysis: Asymmetric Synthesis of KRAS G12C Covalent Inhibitor GDC-6036
Xu, J.; Grosslight, S.; Mack, K. A.; Nguyen, S. C.; Clagg, K.; Lim, N. K.; Timmerman, J. C.; Shen, J.; White, N. A.; Sirois, L. E.; Han, C.; Zhang, H.; Sigman, M. S.; Gosselin, F.
MLR, Chemical Catalysis
Comparing Halogen Atom Abstraction Kinetics for Mn(I), Fe(I), Co(I), and Ni(I) Complexes by Combining Electroanalytical and Statistical Modeling
Tang, T.; Friede, N. C.; Minteer, S. D.; Sigman, M. S.
MLR, Electrocatalysis
Investigating Oxidative Addition Mechanisms of Allylic Electrophiles with Low-Valent Ni/Co Catalysts using Electroanalytical and Data Science Techniques
Tang, T.; Jones, E.; Wild, T.; Hazra, A.; Minteer, S. D.; Sigman, M. S.
Electrocatalysis
Data Science-Driven Analysis of Substrate-Permissive Diketopiperazine Reverse Prenyltransferase NotF: Applications in Protein Engineering and Cascade Biocatalytic Synthesis of (−)-Eurotiumin A
Kelly, S. P.; Shende, V. V.; Flynn, A. R.; Dan, Q.; Ye, Y.; Smith, J. L.; Tsukamoto, S.; Sigman, M. S.; Sherman, D. H.
Biocatalysis, MLR
Noncovalent Stabilization of Radical Intermediates in the Enantioselective Hydroamination of Alkenes with Sulfonamides
Xu, E. Y.; Werth, J.; Roos, C. B.; Bendelsmith, A. J.; Sigman, M. S.; Knowles, R. R.
Chemical Catalysis, MLR
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Gensch, T; dos Passos Gomes, G.; Friederich, P.; Peters, E.; Gaudin, T.; Pollice, R.; Jorner, K.; Nigam, A. Lindner-D'Addario; M.; Sigman, M. S.; Aspuru-Guzik, A.
Chemical Catalysis
Leveraging Regio- and Stereoselective C(sp3)–H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias
Boni, Y. T.; Cammarota, R. C.; Liao, K.; Sigman, M. S.; Davies, H. M. L.
MLR, Chemical Catalysis
Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions
Dotson, J. J.; van Dijk, L.; Timmerman, J. C.; Grosslight, S.; Walroth, R. C.; Gosselin, F.; Püntener, K.; Mack, K. A.; Sigman, M. S.
MLR, Chemical Catalysis
Implication of Phosphine Featurization Methods in the Design and Analysis of High-Throughput Experiments
Crawford, J. M.; Gensch, T.; Sigman, M. S.; Elward, J. M.; Steves, J. E.
Statistical Analysis of Catalytic Performance in Ethylene/Methyl Acrylate Copolymerization Using Palladium/Phosphine-Sulfonate Catalysts
Akita, S.; Guo, J.Y.; Seidel, F.; Sigman, M.; Nozaki, K.
MLR, Chemical Catalysis
Electrochemical Cobalt-Catalyzed Selective Carboxylation of Benzyl Halides with CO2 Enabled by Low-Coordinate Cobalt Electrocatalysts
Malapit, C. A.; Tanwar, M.; Pendergast, A. D.; Udyavara, S.; Beck, W. D.; Smith, R. E.; Kadic, S.; Primo, T.; Wu, A. D.; Stone, T.; White, H. S.*; Neurock, M.*; Sigman, M. S.*; Minteer, S. D.*
Electrocatalysis
Design and Application of a Screening Set for Monophosphine Ligands in Metal Catalysis
Tobias Gensch, T.*; Smith, S. R.; Colacot, T. J.; Timsina, Y.; Xu, G.; Glasspoole, B. W.; Sigman, M. S.*
MLR, Chemical Catalysis
Predicting Relative Efficiency of Amide Bond Formation using Multivariate Linear Regression
Haas, B. C.; Goetz, A. E.*; Bahamonde, A.; McWilliams, J. C.; Sigman, M. S.*
MLR
Development of High Energy Density Diaminocyclopropenium-Phenothiazine Hybrid Catholytes for Non-Aqueous Redox Flow Batteries
Yan, Y.; Vogt, D. B.; Sigman, M. S.; Sanford, M. S.*
MLR, Batteries
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