Rhys Goodall

New York City Metropolitan Area United States

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Summary

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Senior

🎓

Top School

Rhys 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.

9 years of coding experience

7 years of employment as a software developer

EnterpriseTECH, Business, EnterpriseTECH, Business at Cambridge Judge Business School

PhD, Physics, PhD, Physics at University of Cambridge

BatteryMBA, Business, BatteryMBA, Business at BatteryMBA

Github Skills (7)

python10

materials-informatics10

computational-materials-science10

data-structure9

algorithm9

data-structures9

algorithms9

Programming languages (12)

ShellC++CRustOCamlTeXPHPHTML

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

Contributions:30 reviews, 180 commits, 29 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.

pymatgenpythonsciencematerials-sciencematerials-informatics

CompRhys/CompRhys

Sep 2020 - Feb 2022

Contributions:4 commits, 3 pushes, 1 branch in 1 year 5 months

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