Peter Eastman is a senior software engineer with 18 years of experience specializing in high-performance molecular simulation and scientific machine learning, currently building tools at Stanford University. He brings deep expertise in physics-based simulation engines—contributing substantial backend work to OpenMM, ParmEd, MDTraj and PDBFixer—and has improved performance-critical components like neighbor-list builders and vectorized nonbonded forces. His background blends applied physics (PhD, Stanford) with leadership roles in industry, including architect and VP-level engineering positions, giving him both research rigor and product-minded implementation skills. An active open-source maintainer, he has added advanced features such as Drude particle support and AMOEBA force field compatibility, and has helped productionize ML tooling in projects like DeepChem. Notably, he combines low-level C++/GPU optimization experience with practical build-and-release automation across ecosystems such as conda-forge.
18 years of coding experience
7 years of employment as a software developer
B.S., Applied Physics, B.S., Applied Physics at Yale University
Pinewood
Ph.D., Applied Physics, Ph.D., Applied Physics at Stanford University
Contributions:15 releases, 7 reviews, 167 commits in 8 years 8 months
Contributions summary:Peter appears to be the primary developer of the `pdbfixer.py` module, which is the core of the project, responsible for fixing problems in PDB files. The user implemented features for identifying and adding missing atoms, handling non-standard residues, and adding water boxes. They also refactored the code into a class and added support for mmCIF files and membrane building.
OpenMM is a toolkit for molecular simulation using high performance GPU code.
Role in this project:
Backend Developer & Physics Simulation Engineer
Contributions:32 releases, 163 reviews, 4339 commits in 14 years 11 months
Contributions summary:Peter contributed to the OpenMM molecular simulation toolkit, specifically focusing on implementing and optimizing features related to nonbonded interactions within the system. Their work involved enhancing the code's efficiency and stability by refactoring and vectorizing aspects of the CustomNonbondedForce. They also expanded the capabilities of the software by incorporating features such as parameter offsets for nonbonded exceptions and support for the new AMOEBA 2018 force field and GLYCAM.
cudamolecular-dynamicsopenmmgpusimulation
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.