Chi Chen
Senior Director, Quantum Applications
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolChi Chen is a Senior Director leading quantum applications at IonQ with a decade-plus research pedigree and nine years in industry focused on AI-driven materials discovery. Previously he led Quantum Accurate AI and AI-accelerated materials discovery at Microsoft, shipping Copilot integrations and LLM agents for scientific workflows. His academic work produced 70+ papers and 14,000+ citations and delivered foundational tools like MEGNet and M3GNet, powering large-scale materials predictions (>30M entries) and open resources such as Matterverse.ai. Chi combines hands-on ML engineering (improving MEGNet and contributing LAMMPS integrations to pymatgen) with experimental validation of new solid electrolytes, ceramics, and polymers, closing the loop from simulation to lab. Based in Redmond, he drives a mission to accelerate science by marrying quantum computing, modern AI, and multiscale materials simulation.
9 years of coding experience
5 years of employment as a software developer
Bachelor's Degree Solar energy and microfluidics, Bachelor's Degree Solar energy and microfluidics at University of Science and Technology of ChinaCalifornia Institute of TechnologyHong Kong University of Science and Technology (HKUST)
Github Skills (13)
lammps10
keras10
machine-learning10
pymatgen10
deep-learning10
tensorflow10
science10
python10
testing10
materials-informatics9
material-components9
material39
material-design9
Programming languages (8)
Github contributions (5)
materialsvirtuallab/megnet
Dec 2018 - Aug 2022
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Role in this project:
ML Engineer
ML EngineerContributions:22 releases, 491 commits, 100 PRs in 3 years 8 months
Contributions summary:Chi's commits focused on modifying and enhancing the functionality of the MEGNet library, specifically in the context of molecular and crystal machine learning. Their primary contributions involved revising and expanding example codes, including the integration of a new QM9 example notebook. The user also made enhancements, such as adding the GaussianExpansion layer and providing a more modular and comprehensive model through the refactoring of codebase. These updates improved the library's capabilities and usability in materials science applications.
moleculesgraph-networksdeep-learningcrystalsmachine-learning
materialsproject/pymatgen
Aug 2017 - Oct 2021
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:61 commits, 16 PRs, 42 comments in 4 years 2 months
Contributions summary:Chi primarily focused on implementing new features related to LAMMPS data generation within the pymatgen library, specifically the `LMPData` class. This involved converting pymatgen structures into a format suitable for LAMMPS simulations, including handling cell parameters and atomic positions. The user also created and modified associated tests, including writing test cases that validate the functionality and ensure the integration with LAMMPS.
moleculespythonscienceelectronic-structurepowers
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