Evan Spotte-smith
Postdoctoral Research Fellow at Berkeley Lab
Pittsburgh, Pennsylvania, United States
Summary
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Top SchoolEvan Spotte-smith is a computational and data chemist with eight years of experience applying quantum chemistry, statistical mechanics, and machine learning to electrochemical reactivity and energy storage problems. Currently a Carnegie Bosch Institute Fellow and incoming Assistant Professor of Chemical Engineering at Carnegie Mellon University (starting Fall 2025), he focuses on electrolyte decomposition in Li- and Mg-ion batteries and on marrying ML with simulations to optimize (electro)chemical reactions. He has a strong open-source footprint, contributing backend improvements to flagship materials tools like pymatgen and atomate to enable robust DFT workflows and molecular graph analyses. Trained at UC Berkeley (PhD) and Columbia (BS), his work blends high-throughput computation, reaction-network construction, and data-driven modeling for catalysis and sustainability. He also engages with ethical standards and policy for ML in the chemical sciences, reflecting a broader interest in responsible deployment of computational methods. Colleagues describe him as a pragmatic coder-researcher who moves between developing production-ready software and designing physically grounded simulation studies.
7 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Materials Science, GPA: 3.91, Doctor of Philosophy - PhD, Materials Science, GPA: 3.91 at University of California, BerkeleyBachelor’s Degree, Materials Science and Engineering; Sustainable Engineering, GPA: 3.94, Bachelor’s Degree, Materials Science and Engineering; Sustainable Engineering, GPA: 3.94 at Columbia University in the City of New York
English, Spanish
Github Skills (10)
unit-testing10
pymatgen10
networkx10
python10
graph-theory10
computational-materials-science10
fireworks9
database7
databases7
database-api7
Programming languages (9)
Github contributions (5)
materialsproject/pymatgen
Jun 2018 - Jan 2022
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 reviews, 146 commits, 17 PRs in 3 years 8 months
Contributions summary:Evan focused on adding new functionality to the pymatgen library, primarily involving graph theory and molecular structure analysis. They implemented testing files for the graphs.py module, introduced classes for handling structure and molecule graphs, and added methods for finding rings and substituting functional groups. These modifications and additions extend the library's capabilities for analyzing and manipulating molecular structures.
moleculespythonscienceelectronic-structurepowers
hackingmaterials/atomate
Aug 2018 - Jan 2022
atomate is a powerful software for computational materials science and contains pre-built workflows.
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 35 commits, 3 PRs in 3 years 5 months
Contributions summary:Evan primarily contributed to the `atomate` repository by implementing and modifying tasks related to QChem calculations. Their commits focused on the `FragmentMolecule` firetask, including updates to integrate with changes in the `pymatgen` library, bug fixes, and database interaction. These changes indicate a focus on improving and expanding the functionality of the atomate library within the context of computational materials science workflows.
sciencecomputational-materials-sciencecomputational-materialsmaterials-scienceworkflows
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