Raul Flores
Machine Learning Engineer - Materials Science
Emeryville, California, United States
Summary
👤
Senior🎓
Top SchoolRaul Flores is a machine learning engineer and computational materials scientist with eight years of interdisciplinary experience applying ML, quantum mechanics, and high-throughput workflows to energy materials from biofuels to next‑generation fuel cell catalysts and solar devices. Trained at Stanford and seasoned at Berkeley Lab, he has built active learning pipelines and Gaussian Process models to accelerate catalyst discovery, modeled ferroelectric switching for microelectronics, and led outreach and career‑networking initiatives for postdocs. Now based in Emeryville and working at CFD Research Corporation, he blends hands‑on atomistic simulation, Python workflow automation, and HPC expertise to turn physical insight into scalable data‑driven materials solutions. Notably, his work couples physically inspired features with probabilistic ML to prioritize experiments, reducing candidate search space while improving predictive confidence.
8 years of coding experience
8 years of employment as a software developer
BS Chemical Engineering, BS Chemical Engineering at University of KansasChemical Engineering Chemical Engineering, Chemical Engineering Chemical Engineering at Stanford University
English, Spanish
Github Skills (83)
ipywidgets10
interactive10
jupyter-widget10
plotly10
jupyter10
jupyter-notebook10
regl10
visualization10
jupyterlab10
wsl210
pyodide9
dashboard9
flask-mongoengine9
plotly-dash9
notebook9
Programming languages (6)
Github contributions (5)
raulf2012/TEMPLATES
Aug 2019 - Feb 2025
Contributions:2 pushes, 1 branch in 5 years 7 months
raulf2012/CatLearn
Dec 2018 - Jun 2024
A machine learning environment for atomic-scale modeling in surface science and catalysis.
Contributions:11 pushes, 2 branches in 5 years 6 months
catalysisscale-modelingsciencesurface-sciencemachine-learning
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