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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2019

Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst

Eric A. Walker, Joshua A. Kammeraad, Jonathan Goetz, Michael T. Robo, Ambuj Tewari, Paul M. Zimmerman

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Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and CatalystOriginalWhat Does the Machine Learn? Knowledge Representations of Chemical Reactivity31Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?12Graph Theory Model of Dry Reforming of Methane Using Rh(111)23Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions4Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions1Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference16CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**29Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries11DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Trends in computational molecular catalyst design27Organic reactivity from mechanism to machine learning175Automation and computer-assisted planning for chemical synthesis189Kernel Methods for Predicting Yields of Chemical Reactions2Inferring experimental procedures from text-based representations of chemical reactions80Machine Learning Force Fields: Recent Advances and Remaining Challenges127Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Computer-aided retrosynthetic design: fundamentals, tools, and outlook37Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES35Kernel Methods for Predicting Yields of Chemical Reactions47Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow13Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach16How a quantum computer could accurately solve a hydrogen-air combustion model4Predicting reaction conditions from limited data through active transfer learning78Prediction of the Chemical Context for Buchwald‐Hartwig Coupling Reactions15Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature Texts72Machine intelligence for chemical reaction space124Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing40Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions30A Categorical Approach to Synthetic Chemistry2Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center32Iron-sensitive protein conjugates formed with a Wittig reaction precursor in ionic liquid4Predictive Chemistry Augmented with Text Retrieval17Reagent prediction with a molecular transformer improves reaction data quality36A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.076Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Predicting solvents with the help of Artificial Intelligence3Cheminformatics and artificial intelligence for accelerating agrochemical discovery37AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs9Machine learning in reaction engineering0Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target3ORDerly: Data Sets and Benchmarks for Chemical Reaction Data34Efficiency-optimized Diels-Alder reactions based on random forest1Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis9Predicting reaction conditions: a data-driven perspective10Probability Guided Chemical Reaction Scopes2Recommending reaction conditions with label ranking6Using machine learning methods to predict the diabatic bond dissociation energy of non-heme iron complexes0A categorical model for organic chemistry1Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction7Robust Chemical Reaction Condition Recommendations via Label Mix Strategy3Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions1Reaction Performance Prediction and Reaction Condition Optimization0A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools3Motif-Based Graph Learning for Synthetic Reaction Condition Prediction0The Stubborn Persistence of Toxic Solvents in Chemical Synthesis1The Stubborn Persistence of Toxic Solvents in Chemical Synthesis0Machine Learning-Guided Strategy Selection and Condition Prediction for Photoredox Catalysis0

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  1. What Does the Machine Learn? Knowledge Representations of Chemical Reactivity

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  2. Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?

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  3. Graph Theory Model of Dry Reforming of Methane Using Rh(111)

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  4. Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions

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  5. Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions

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  6. Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference

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  7. CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

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  8. Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries

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  18. Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES

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  21. Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

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