Xinyang Li is an Associate Scientist and theoretical chemist with 10 years of experience combining molecular dynamics, high-performance computing, and software development to tackle complex chemical problems. He developed physics-informed deep neural networks for excited-state nonadiabatic dynamics and implemented an excited-state module in the hippynn package, bridging quantum calculations and PyTorch-based ML. His work at Los Alamos included orchestrating open-source projects, automated documentation pipelines, and asynchronous hyperparameter tuning with Ray and Ax, showing both research depth and reproducible engineering. Earlier academic work produced highly cited research on nuclear quantum effects and polariton chemistry using ab initio MD and enhanced sampling across millions of CPU hours. Now at Merck and seeking research scientist roles in machine learning for chemistry, he brings a rare blend of domain expertise, production-minded ML engineering, and community-driven open-source practice. Outside work he’s a hands-on tinkerer—homelabbing and 3D printing—that often informs practical tooling and experimental workflows.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Science - BS, Chemistry, Bachelor of Science - BS, Chemistry at Wuhan University
Doctor of Philosophy - PhD, Theoretical Chemistry, Doctor of Philosophy - PhD, Theoretical Chemistry at University of Rochester
Contributions:2 PRs, 126 pushes, 28 branches in 2 years 9 months
python-librarypythonmachine-learningatomistic
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