Richard D. Braatz
· Edwin R. Gilliland ProfessorMassachusetts Institute of Technology · Chemical Engineering
Active 1992–2026
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About
Richard D. Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at MIT. His research focuses on chemical engineering, with particular emphasis on areas related to his department's expertise. As a faculty member, he contributes to the academic community through teaching and research, although specific details about his research interests and contributions are not provided in the page text.
Research topics
- Computer Science
- Machine Learning
- Artificial Intelligence
- Engineering
- Political Science
- Reliability engineering
- Waste management
- Operating system
- Data science
- Business
Selected publications
Chemical Society Reviews · 2022 · 1738 citations
footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. R…
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Nature · 2020 · 1022 citations
Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime
Journal of The Electrochemical Society · 2021 · 244 citations
Forecasting the health of a battery is a modeling effort that is critical to driving improvements in and adoption of electric vehicles. Purely physics-based models and purely data-driven models have advantages and limitations of their own. Considering the nature of battery data and end-user applications, we outline several architectures for integrating physics-based and machine learning models that can improve our ability to forecast battery lifetime. We discuss the ease of implementation, advan…
Model‐Based Optimization of Fed‐Batch In Vitro Transcription
ChemBioChem · 2025-10-09 · 4 citations
articleOpen accessSenior authorCorrespondingRecent developments in RNA vaccines and therapeutics have motivated the need for process engineering strategies to optimize the in vitro transcription (IVT) reaction for RNA synthesis. Specifically, practitioners seek to maximize the production of RNA and the incorporation of the 5-prime cap to the end of each RNA molecule while minimizing the use of expensive reagents. Fed-batch IVT is a promising technique for achieving these goals but is difficult to optimize by purely experimental means. Her…
End-to-end digital twin software for continuous mRNA manufacturing
International Journal of Pharmaceutics · 2025-10-22 · 3 citations
articleSenior authorCorresponding
Recent grants
Frequent coauthors
- 98 shared
Dennis S. Bernstein
University of Michigan–Ann Arbor
- 97 shared
Stephen Yurkovich
The University of Texas at Dallas
- 97 shared
Hong Yue
- 97 shared
Rodolphe Sepulchre
- 97 shared
Hesuan Hu
Xi'an Jiaotong University
- 97 shared
Scott Ploen
Jet Propulsion Laboratory
- 82 shared
Reginald B. H. Tan
- 75 shared
Joshua D. Isom
University of Cambridge
Labs
Education
- 1986
Ph.D., Chemical Engineering
Massachusetts Institute of Technology
- 1982
M.S., Chemical Engineering
Massachusetts Institute of Technology
- 1980
B.S., Chemical Engineering
University of California, Berkeley
Awards & honors
- John R. Ragazzini Education Award (2023)
- Elected to the National Academy of Engineering (2019)
- AIChE Separation Division Innovation Award (2019)
- Elected AIChE Fellow (2018)
- Automatica Paper Prize (2017)
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