William H. Green
· Hoyt Hottel Professor in Chemical EngineeringMassachusetts Institute of Technology · Chemical Engineering
Active 1802–2026
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About
William H. Green is the Hoyt Hottel Professor in Chemical Engineering at MIT and serves as the Director of the MIT Energy Initiative. His research focuses on chemical engineering principles, energy, and sustainability. As a distinguished faculty member, he contributes to the academic and research community at MIT, emphasizing advancements in energy-related fields and sustainable chemical processes.
Research topics
- Computer Science
- Chemistry
- Physics
- Thermodynamics
- Computational chemistry
- Medicine
- Biological system
- Data Mining
- Internal medicine
- Programming language
Selected publications
JAMA · 2022 · 409 citations
Importance: Continuous positive airway pressure (CPAP) and high-flow nasal oxygen (HFNO) have been recommended for acute hypoxemic respiratory failure in patients with COVID-19. Uncertainty exists regarding the effectiveness and safety of these noninvasive respiratory strategies. Objective: To determine whether either CPAP or HFNO, compared with conventional oxygen therapy, improves clinical outcomes in hospitalized patients with COVID-19-related acute hypoxemic respiratory failure. Design, Sett…
Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation
Journal of Chemical Information and Modeling · 2021 · 286 citations
Senior authorCorrespondingIn chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since th…
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Journal of Medicinal Chemistry · 2020 · 241 citations
synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS…
RMG Database for Chemical Property Prediction
Journal of Chemical Information and Modeling · 2022 · 148 citations
Senior authorCorrespondingThe Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermoch…
Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation
2020 · 42 citations
Senior authorCorrespondingIn chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since th…
Recent grants
Frequent coauthors
- 90 shared
Colin A. Grambow
- 82 shared
Alon Grinberg Dana
Technion – Israel Institute of Technology
- 78 shared
Matthew S. Johnson
Sandia National Laboratories
- 67 shared
Mengjie Liu
Henan University of Science and Technology
- 64 shared
A. Mark Payne
Massachusetts Institute of Technology
- 62 shared
Nathan W. Yee
Massachusetts Institute of Technology
- 57 shared
Kehang Han
- 55 shared
Agnes Jocher
Technical University of Munich
Labs
Education
- 1989
Ph.D., Chemical Engineering
Massachusetts Institute of Technology
- 1984
M.S., Chemical Engineering
Massachusetts Institute of Technology
- 1982
B.S., Chemical Engineering
University of California, Berkeley
Awards & honors
- AIChE’s R. H. Wilhelm Award in Chemical Reaction Engineering…
- Inaugural Fellow of the Combustion Institute (2018)
- Elected Fellow of the American Association for the Advanceme…
- C.M. Mohr Award for Outstanding Undergraduate Teaching (2006…
- Richard A. Glenn Award (2004, 2009, 2013)
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