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 science
• Medicine
• Internal medicine
• Computational chemistry
• Biological system
• Programming language
• Artificial Intelligence
• Data Mining
• Database
• Physical chemistry
• Statistics
• Economic growth
• Organic chemistry
• Geography
• Virology
• Psychology
• Data science
• Economics
• Cognitive science
• Nanotechnology

Selected publications

• Supporting information for: "Digitized dataset of aqueous dissociation constants"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-06

  datasetOpen accessSenior author

  Supporting information for the manuscript Digitized dataset of aqueous dissociation constants. This repository includes the models and data splits used in the manuscript, as well as data files that illustrate data overlaps between commonly-used training sets and test sets. Note: this is NOT the official repository for the full dataset; this is only for the trained model and information supporting the associated manuscript. The latest version of the full IUPAC dataset can be found at: https://doi.org/10.5281/zenodo.7236452

  PublisherDOI

• Digitized dataset of aqueous dissociation constants

  ChemRxiv · 2026-01-16

  articleOpen accessSenior author

  The acid dissociation constant (pKa) quantifies the acidity of a compound, which is crucial for applications including drug design, environmental fate studies, and chemical synthesis. However, high-quality open-source digital pKa datasets are scarce, which limits the ability for researchers to search for properties of individual compounds, while also limiting the potential of data-driven predictive models. In this work, we release the IUPAC Digitized pKa Dataset, a digital version of a critically-assessed collection of data compiled up to 1970. The dataset includes metadata such as temperature, measurement method, assessed reliability of data, and chemical identifiers such as SMILES and InChI strings. The dataset spans 24,222 entries across 10,564 unique molecules, making it the largest FAIR open-source dataset publicly available for aqueous pKa data. Herein, we detail the data digitization and checking process, and assess the informational space spanned by the data. We compare the new digital dataset to other widely-used datasets. Several pKa predictors have been trained using these other datasets, but often have not been reliably tested due to overlap between the training and test data. We use the data to train a macroscopic pKa predictor and determine its accuracy using overlap-free test data. The full dataset is available at https://doi.org/10.5281/zenodo.7236452, and the models and data splits used in this study are available at https://doi.org/10.5281/zenodo.18165948.

  PublisherOA PDFDOI

• QuantumPioneer: Scalable generation of quantum chemical data for solution-phase hydrogen transfer reactions

  ChemRxiv · 2026-05-18

  articleOpen accessSenior author

  High-fidelity quantum chemical (QM) datasets that jointly resolve reaction thermochemistry, kinetics, and solvation at scale remain scarce, especially for radical chemistry. We introduce QuantumPioneer, an open-access reaction-centered QM database and workflow for small organic molecules, focused on peroxyl-mediated hydrogen atom transfer (HAT) and the corresponding homolytic bond dissociation reactions. QuantumPioneer contains 348,258 species (2–21 heavy atoms), 167,237 validated HAT transition states (TS) with corresponding reaction energies and homolytic bond dissociation energies (BDEs), and over 100 million COSMO-RS solvation free energies (∆Go solv) and enthalpies (∆Ho solv) across 295 solvents. The workflow uses ωB97X-D/def2-SVP geometries, DLPNO-CCSD(T)-F12d/def2-TZVP single-point energies, empirical thermochemical corrections, transition-state theory, and COSMORS BP-TZVPD-FINE solvation in a single high-throughput pipeline. Our benchmarks show reliable accuracy, with mean absolute errors compared to experimental data of 0.82 kcal/mol for gas-phase enthalpies of formation, 1.60 kcal/mol for C–H BDEs, 1.45 kcal/mol for HAT barriers, and 0.57 kcal/mol for ∆Go solv values. We demonstrate two predictive applications. First, we show that a combined BDE and HAT-barrier model identifies experimentally observed oxidative degradation sites in drug-like molecules with a 91% top-5 hit rate and 80% site-level recall. Second, a QM-parameterized Abraham model enables rapid solvation energy estimates at near-COSMO-RS accuracy within its training domain, reproducing ∆Go solv and ∆Ho solv

  PublisherDOI

• Partitioning Parameters of <i>N-</i> Nitrosamines: An Intercomparison of Determination Methods

  The Journal of Physical Chemistry B · 2026-03-25

  articleOpen access

  N-Nitrosamines make up a class of carcinogenic industrial pollutants that lack well-characterized physicochemical properties. Classical approaches to determine octanol–water partition coefficient (Kow) values are laborious, slow, and challenged by experimental error. Alternative methods include quantum chemical estimation (e.g., COSMO-RS), quantitative structure–property relationship (QSPR) models, and high-performance liquid chromatographic (HPLC) measurements; however, systematic compound-by-compound comparisons of these methods for chemical classes remain lacking. This study evaluates the performance of four methods (shake-flask, HPLC retention time, QSPR, and COSMO-RS estimation) for the log Kow determination. Shake-flask measurements for N-nitrosodiemthylamine (−0.54), N-nitrosomorpholine (−0.54), N-nitrosopiperidine (0.64), and N-nitrosodibutylamine (2.54) were compared to previously reported values, where the omission of quality control procedures (i.e., mutual solvent saturation and sufficient equilibration time) led to variations in measurements up to 0.64 log units. Among alternative methods, the COSMO-RS calculation in this study performed the best, relative to direct experimental measurement, with a root mean absolute error (RMSE) of 0.12 and improved accuracy compared to previous estimations. QSPR determination was comparable to that of COSMO-RS (RMSE of 0.14). Two methods of HPLC determination demonstrated the worst performance (RMSEs of 0.27 and 0.45). This study highlights the weaknesses in using the presented HPLC methods for compound classes that include polar molecules, demonstrates improved performance of theoretical calculations, and reports partitioning data for known (n = 8) and recently characterized (n = 7) N-nitrosamines found in the environment.

  PublisherOA PDFDOI

• A novel and comprehensive CH4/NH3/H2 mechanism incorporating new physics

  ChemRxiv · 2026-04-07

  articleOpen accessSenior author

  Ammonia is a promising carbon-free energy carrier; however, its low reactivity and strong tendency to form nitrogen oxides remain major challenges for practical combustion applications. Blending ammonia with hydrogen and/or methane is one strategy to facilitate ammonia combustion, but this changes the radical pool and introduces cross chemistry that is not fully understood. Thus, it remains unclear how different fuel blends promote or suppress the formation of pollutants such as NOx and N2O. In this work, we expand our recently developed NH3/H2 mechanism to model NH3/H2/CH4 blends using an automated reaction-mechanism generator. For many important reactions, kinetic and thermochemical parameters are sourced from existing libraries, whereas rate rules and groupadditivity rules are applied when no data are available. To treat the composition dependence of key reactions, the linear-mixture rule in reduced pressure (Burke rule) is applied. Importantly, no kinetic and thermochemical parameters are tuned to reproduce specific modeling targets. The mechanisms are validated against laminarburning velocities and species profiles from jet-stirred and flow reactors across binary and ternary fuel blends, and good agreement with experiment is observed. Reaction-path and rate-of-production analyses reveal that blending H2 or CH4 enhances fuel-driven NOx formation primarily by increasing H, O, and OH concentrations rather than introducing new NOx-forming reaction pathways. We present a detailed analysis in which we demonstrate how different ternary blends affect NOx emissions.

  PublisherOA PDFDOI

• Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next

  ACS Engineering Au · 2026-01-06 · 4 citations

  articleOpen accessSenior author

  Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AI-augmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains.

  PublisherDOI

• Curated digital datasets of acid dissociation constants in dipolar aprotic solvents

  ChemRxiv · 2026-04-20

  articleOpen accessSenior author

  The acid dissociation constant is a key thermodynamic property, but large compilations of highly trustworthy data in non-aqueous solvents are scarce. This work presents a newly-digitized compilation, with corrections, of pK a data published originally by Kosuke Izutsu in 1990 as an official publication of the International Union of Pure and Applied Chemistry (IUPAC). The data span the six solvents dimethylacetamide, hexamethylphosphoramide, 4-methylpentan-2-one (or methyl isobutyl ketone), N-methyl-2-pyrrolidone, nitromethane, and sulfolane. Because the original data were not critically evaluated, we also include proposed systematic corrections to the data, which resolve major inconsistencies in the original data compilation. We also present, analyze, and discuss a digital version of the new IUPAC 2025 dataset for neutral acids in the solvents dimethyl sulfoxide, acetonitrile, dimethyl formamide, pyridine, acetone, tetrahydrofuran, and propylene carbonate, compiled by Leito and collaborators. We discuss the creation and contents of these digital data compilations, and analyze not only the pK a values, but also the overall reliability, methodology, and other available metadata. The Izutsu dataset is available at: doi.org/10.5281/zenodo.19626584 and the IUPAC 2025 dataset at doi.org/10.5281/zenodo.12608876.

  PublisherDOI

• Supporting information for: "Digitized dataset of aqueous dissociation constants"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-06

  datasetOpen accessSenior author

  Supporting information for the manuscript Digitized dataset of aqueous dissociation constants. This repository includes the models and data splits used in the manuscript, as well as data files that illustrate data overlaps between commonly-used training sets and test sets. Note: this is NOT the official repository for the full dataset; this is only for the trained model and information supporting the associated manuscript. The latest version of the full IUPAC dataset can be found at: https://doi.org/10.5281/zenodo.7236452

  PublisherDOI

• QuantumPioneer Dataset

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-15

  datasetOpen accessSenior author

  Datasets associated with the work "QuantumPioneer: Scalable generation of quantum chemical data for solution-phase hydrogen transfer reactions" GitHub: https://github.com/QuantumPioneer Refer to the SCHEMA.md file for details of different datasets released here.

  PublisherDOI

• Mechanism Reduction and Validation of Ammonia/Hydrogen/Air Combustion Leveraging First-Principles-Derived Kinetics

  ChemRxiv · 2026-05-06

  articleOpen access

  This work presents a reduced mechanism for NH3/H2/air combustion derived directly from a high-fidelity, firstprinciples-based parent model [1] without empirical modification of kinetic or thermochemical parameters. Starting from a 53-species detailed mechanism, an iterative reduction strategy was used to obtain a 23-species reduced mechanism while preserving the parent chemistry. The reduced mechanism was assessed against a broad validation suite including ignition delay times, laminar burning velocities, jet-stirred reactor species profiles, plug flow reactor species evolution, extinction strain rates, and turbulent computational fluid dynamics (CFD) fidelity tests. Across the canonical validation configurations, the reduced mechanism’s results closely matched those of the detailed parent mechanism, indicating that the dominant ignition, flame-propagation, intermediate-species, and NOxformation pathways are preserved. Relative to representative reduced mechanisms in the literature, the present mechanism provided consistently balanced performance over pure NH3 and NH3/H2 conditions, while avoiding the empirical retuning commonly used in compact ammonia-fuel chemistry. In a CFD simulation of a turbulent partially premixed NH3/H2/N2-air jet flame, the reduced mechanism reproduced the detailed-mechanism predictions of temperature, NH, and NO with negligible visible loss of fidelity while reducing computational cost by approximately a factor of five. The resulting mechanism provides a physically grounded, CFD-practical chemistry model for predictive simulations of ammonia/hydrogen combustion across wide ranges of conditions.

  PublisherOA PDFDOI

Recent grants

• ITR: A New Data Model and Extensible Software for Predictive Chemical Kinetics

  NSF · $429k · 2003–2006

• Collaborative Research: Cyberinfrastructure and Research Facilities: Process Informatics for Chemical Reaction Systems

  NSF · $1.2M · 2005–2010

Frequent coauthors

• Colin A. Grambow

  90 shared

• Alon Grinberg Dana

  Technion – Israel Institute of Technology

  82 shared

• Matthew S. Johnson

  Sandia National Laboratories

  78 shared

• Mengjie Liu

  Henan University of Science and Technology

  67 shared

• A. Mark Payne

  Massachusetts Institute of Technology

  64 shared

• Nathan W. Yee

  Massachusetts Institute of Technology

  62 shared

• Kehang Han

  57 shared

• Agnes Jocher

  Technical University of Munich

  55 shared

Labs

• MIT ChemEPI

Education

• Ph.D., Chemical Engineering

  Massachusetts Institute of Technology

  1989

• M.S., Chemical Engineering

  Massachusetts Institute of Technology

  1984

• B.S., Chemical Engineering

  University of California, Berkeley

  1982

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)