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William H. Green
Researcher at Massachusetts Institute of Technology
Publications - 366
Citations - 17579
William H. Green is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Combustion & Catalysis. The author has an hindex of 67, co-authored 343 publications receiving 13845 citations. Previous affiliations of William H. Green include University of California, Berkeley & University of Cambridge.
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Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms ☆
TL;DR: The RMG software package also includes CanTherm, a tool for computing the thermodynamic properties of chemical species and both high-pressure-limit and pressure-dependent rate coefficients for chemical reactions using results from quantum chemical calculations.
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Prediction of Organic Reaction Outcomes Using Machine Learning
TL;DR: A model framework for anticipating reaction outcomes that combines the traditional use of reaction templates with the flexibility in pattern recognition afforded by neural networks is reported.
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A robotic platform for flow synthesis of organic compounds informed by AI planning
Connor W. Coley,Dale A. Thomas,Justin A. M. Lummiss,Jonathan N. Jaworski,Christopher P. Breen,Victor L. Schultz,Travis Hart,Joshua Fishman,Luke Rogers,Hanyu Gao,Robert W. Hicklin,Pieter Plehiers,Joshua Byington,John S. Piotti,William H. Green,A. John Hart,Timothy F. Jamison,Klavs F. Jensen +17 more
TL;DR: An approach toward automated, scalable synthesis that combines techniques in artificial intelligence (AI) for planning and robotics for execution is described, representing a milestone on the path toward fully autonomous chemical synthesis.
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A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W. Coley,Wengong Jin,Luke Rogers,Timothy F. Jamison,Tommi S. Jaakkola,William H. Green,Regina Barzilay,Klavs F. Jensen +7 more
TL;DR: A supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s) is presented.
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Machine Learning in Computer-Aided Synthesis Planning
TL;DR: Two critical aspects of CASP and recent machine learning approaches to both challenges are focused on, including the problem of retrosynthetic planning and anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan.