Yury Polyachenko is a theoretical chemist and computational physicist pursuing a PhD at Princeton with eight years’ experience in multiscale molecular dynamics, from DFT/ab-initio to coarse-grained models. He develops AI-powered pipelines for ultra-coarse-grained protein models and built a BRD4–chromatin model whose MD predictions about transcriptional condensate nucleation were later experimentally confirmed. His work spans nanopore free-energy calculations, GPU-accelerated MD engines, and DFT studies of plasma processing of 2D materials where he identified sputtering thresholds and adsorption free energies. Comfortable in C++, Python and HPC (MPI/OpenMP/CUDA), he combines rigorous theory with practical tooling and mentorship, having secured fellowships and mentored undergraduates into coauthorship. Ambitious to bring DFT-level accuracy to biological scales, he explores QM/MM and neural-network potentials to bridge quantum and mesoscale simulations.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Condensed Matter and Materials Physics, Bachelor's degree, Condensed Matter and Materials Physics at Moscow Institute of Physics and Technology (State University) (MIPT)
Doctor of Philosophy - PhD, Theoretical Chemistry, Doctor of Philosophy - PhD, Theoretical Chemistry at Princeton University
Contributions:14 commits, 1 PR, 13 pushes in 4 months
waterlammpspythonsimulation
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