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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2023

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman

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Machine Learning Strategies for Reaction Development: Toward the Low-Data LimitOriginalDataset Design for Building Models of Chemical Reactivity94Cost-informed Bayesian reaction optimization17Every atom counts: predicting sites of reaction based on chemistry within two bonds0Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts11Baldwin and Whitehead’s Manzamine Alkaloids Biosynthesis Hy-pothesis Involves a Finely Tuned Reactivity of C3 Unit: a High-Throughput Experimentation Approach2Predicting success in Cu-catalyzed C–N coupling reactions using data science59Bayesian optimization assisted screening conditions for visible light-induced hydroxy-perfluoroalkylation5Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation12Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels–Alder Reaction Outcomes34Deep Kernel learning for reaction outcome prediction and optimization19Quantum chemical package Jaguar: A survey of recent developments and unique features62Active learning high coverage sets of complementary reaction conditions4Applying statistical modeling strategies to sparse datasets in synthetic chemistry45Integrating Machine Learning with Limited Datasets to Optimize Azo Dye Synthesis: A Sustainable Approach0Predicting reaction conditions: a data-driven perspective10Prospective active transfer learning on the formal coupling of amines and carboxylic acids to form secondary alkyl bonds6Recommending reaction conditions with label ranking6Machine Learning for Developing Sustainable Polymers12Exploring reaction dynamics involving post-transition state bifurcations based on quantum mechanical ambimodal transition states6Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides18Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features10Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation6Data Limitation Problems of Motor Insurance Policyholders Classification Using Machine Learning Model0Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Bayesian Optimization for Chemical Synthesis in the Era of Artificial Intelligence: Advances and Applications19Performance Evaluation of Classification Multi Algorithms on Small Dataset: A Comparative-Based Analysis0Predicting Substrate Reactivity in Oxidative Homocoupling of Phenols Using Positive and Unlabeled Machine Learning4Automating Deep Learning-Based Generation and Evaluation of De Novo Chemical Reaction with ChemRxnSAGE1Trendbericht Theoretische Chemie 2025 1/2: Maschinelles Lernen für angeregte Zustände0Decoding Regioselectivity in Cu-Catalyzed Borylation of Alkynes: Insights from Machine Learning and Artificial Intelligence2A Concise Bond-Distance Summation Descriptor for Effective Melting Point Prediction of Boronic Acids0Efficient development of a neural network potential for pure silica zeolites1Machine learning approaches to predicting metal catalyst performance in olefin polymerization0Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance2Accelerating Efficient Dimethyl Ether Synthesis through Machine Learning-Based Process Optimization0ChemRANG: Building AI-ready datasets via mechanism-informed curation to learn reaction feasibility boundaries0Unraveling the Electronic Origin of Selectivity in Ambimodal Transition States with Valence Bond Theory0Competition-Derived Relative Reactivity and 3D Electronic-State Analysis of Site- and Facial Selectivity in NaBH4/MeOH Ketone Reductions0Integrating Mechanistic Knowledge into Machine Learning Enables Improved Reaction Prediction in Low‐Data Regimes0

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  17. Recommending reaction conditions with label ranking

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  18. Machine Learning for Developing Sustainable Polymers

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