Matthew Johnson
Postdoctoral Researcher at Sandia National Laboratories
Livermore, California, United States
Summary
👤
Senior🎓
Top SchoolMatthew Johnson is a computational chemical kineticist and postdoctoral researcher with 12 years of experience building scientific software that blends automatic mechanism generation, automated quantum chemistry, and AI/ML to produce predictive kinetic models for reactors, screening, and design. He has led core development on the widely used Reaction Mechanism Generator (RMG-Py) and created tools like Pynta and PySIDT to automate rate and thermochemistry workflows and apply SIDT machine learning to chemical property prediction. At MIT and Sandia he invented practical algorithms—transitory sensitivity analysis and Harmonically Forced Saddle Point searching—that accelerate mechanism analysis and transition-state discovery, and he developed a new model for diffusion-limited kinetics at interfaces. Based in Livermore, CA, Matthew combines deep theory, production-grade Python engineering, and active-learning workflows to push surface and heterogeneous kinetics toward scalable, data-driven prediction.
11 years of coding experience
8 years of employment as a software developer
West High SchoolDoctor of Philosophy (Ph.D.), Chemical Engineering, Doctor of Philosophy (Ph.D.), Chemical Engineering at Massachusetts Institute of TechnologyBSE, Chemical Engineering, Senior, BSE, Chemical Engineering, Senior at University of Iowa
Github Skills (5)
kinetics10
python10
nonlinear-dynamics9
molecular-dynamics-simulation9
molecular-simulation9
Programming languages (6)
Github contributions (5)
ReactionMechanismGenerator/RMG-Py
Jan 2017 - Jan 2023
Python version of the amazing Reaction Mechanism Generator (RMG).
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 207 reviews, 1268 commits in 6 years
Contributions summary:Matthew primarily contributed to the core functionalities of the Reaction Mechanism Generator (RMG-Py) project, focused on the implementation of thermodynamic properties of chemical species and kinetic models. They made substantial changes to the kinetics models, including adding new features related to handling Arrhenius and multi-Arrhenius equations with a Blower-Masel formalism. They also fixed bugs and introduced features related to running pressure dependent network exploration in the cantherm module.
mechanismchemical-engineeringchemistrypythonreaction
zadorlab/PySIDT
Oct 2023 - Feb 2025
Contributions:40 reviews, 37 PRs, 66 pushes in 1 year 5 months
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