Soham Chattopadhyay is a Postdoctoral Research Associate at Los Alamos National Laboratory with nine years of experience developing computational methods for Markovian diffusion in materials. He combines rigorous PhD-level materials science with machine learning—particularly physics-informed neural networks—to accelerate Kinetic Monte Carlo transport coefficient calculations and cut millions of migration-barrier evaluations. His work also spans analytical Green’s function models for interstitial dumbbell diffusion and high-performance numerical implementations in C++ using MPI and CUDA. Based in Los Alamos after training at UIUC and IISc, he blends deep theory with practical, parallelized code, making him adept at translating complex stochastic models into scalable simulation tools. An under-the-radar strength is his knack for reducing computational cost in materials modeling through physics-aware ML, enabling studies that would otherwise be intractable.
9 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng, Materials Engineering, Master of Engineering - MEng, Materials Engineering at Indian Institute of Science (IISc)
Bachelor of Technology - BTech, Metallurgical Engineering, Bachelor of Technology - BTech, Metallurgical Engineering at National Institute of Technology Durgapur
Doctor of Philosophy - PhD, Materials Science and Engineering, 4.0/4.0, Doctor of Philosophy - PhD, Materials Science and Engineering, 4.0/4.0 at University of Illinois Urbana-Champaign
Contributions:545 pushes, 14 branches in 2 years 4 months
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