Rhys Goodall
Member Of Technical Staff at Radical AI
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolRhys Goodall is a machine-learning-driven materials scientist and Member of Technical Staff based in New York with nine years of interdisciplinary experience applying ML to targeted materials discovery and battery R&D. He developed influential frameworks (Roost and Wren) during his PhD at Cambridge and has published in high-impact journals while bridging academic research and industry productization. At Chemix he built custom AI models for accelerated battery development, and at Radical AI he continues scaling ML for materials problems. An active contributor to the widely used pymatgen project, he has improved phase diagram tools and Voronoi neighbor calculations—work that demonstrates attention to numerical stability in scientific software. Trained in physics with business and battery-specialist credentials, he combines deep domain knowledge with practical engineering to move models from research into real-world discovery workflows.
8 years of coding experience
5 years of employment as a software developer
EnterpriseTECH Business, EnterpriseTECH Business at Cambridge Judge Business SchoolPhD Physics, PhD Physics at University of CambridgeBatteryMBA, BatteryMBA at BatteryMBA
Github Skills (7)
python10
materials-informatics10
computational-materials-science10
data-structure9
algorithm9
data-structures9
algorithms9
Programming languages (12)
Github contributions (5)
materialsproject/pymatgen
Apr 2020 - Jul 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:30 reviews, 180 commits, 27 PRs in 2 years 3 months
Contributions summary:Rhys primarily focused on debugging and improving the `pymatgen` library, specifically within the `analysis/local_env.py` and `analysis/phase_diagram.py` modules. Their work involved identifying and fixing bugs related to Voronoi neighbor calculations and decomposition energy computations, along with adjustments for improved numerical stability. They also introduced improvements to the phase diagram functionalities and made minor corrections.
moleculespythonscienceelectronic-structurepowers
CompRhys/CompRhys
Sep 2020 - Feb 2022
Contributions:4 commits, 3 pushes, 1 branch in 1 year 5 months
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