Jaehyung (Jae) Lee

I am a second-year PhD student in Materials Science & Engineering at Johns Hopkins University, where I build machine learning and high-performance computing tools that accelerate the discovery and design of new materials as a member of the Choudhary Research Group.

My research sits at the intersection of first-principles simulation (DFT), machine-learning interatomic potentials, and agentic AI. I work on a range of problems: predicting electronic properties across millions of crystal structures, screening and designing battery cathode materials, learning catalytic adsorption energies, and building AtomGPT-powered agents that let researchers run materials-discovery workflows in natural language. I also care about making these methods fast and reproducible at scale, from single GPUs to multi-GPU systems.

Before Hopkins, I earned an M.S. in Chemical Engineering from Columbia University and a B.S. in Chemical Engineering from Penn State, with prior research in machine-learning potentials for battery materials and DFT studies of catalytic metal oxides.

Feel free to reach out to me at jlee859@jh.edu or grab my CV.

Recent News

Jul 09, 2026	New co-authored preprint on arXiv: “Hybrid DiffractGPT-Rietveld Refinement Framework for Automated X-ray Diffraction Analysis” (arXiv:2607.08890).
Jul 08, 2026	New co-authored preprint on arXiv: “Hallucination Detector: A Hybrid LLM and Semantic Scholar Tool-Calling System for Detecting Hallucination in Scientific Literature on AtomGPT.org” (arXiv:2607.09774).
Jul 07, 2026	New preprint on arXiv: “BatteryMat: A Hierarchical Machine-Learning and DFT Framework for Average-Voltage Screening of Lithium-Ion Cathode Materials” (arXiv:2607.06645).
Jul 06, 2026	New co-authored preprint on arXiv: “SlaKoNet-VQD: A Universal Slater-Koster Tight-Binding Hamiltonian for Variational Quantum Band-Structure Calculations on Near-Term Hardware” (arXiv:2607.09761).
Jun 24, 2026	Presented a poster on “SlaKoNet DB: A Cross-Domain Tight-Binding Database of Electronic Structure” at the Electronic Structure Workshop (ESW), University of Wisconsin–Madison.

Selected Publications

JPCL

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org

Jaehyung Lee, Justin Ely, Kent Zhang, and 3 more authors

The Journal of Physical Chemistry Letters, 2026

DOI Bib GitHub

@article{lee2026agapi,
  title = {AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org},
  author = {Lee, Jaehyung and Ely, Justin and Zhang, Kent and Ajith, Akshaya and Campbell, Charles Rhys and Choudhary, Kamal},
  journal = {The Journal of Physical Chemistry Letters},
  year = {2026},
  publisher = {American Chemical Society},
  doi = {10.1021/acs.jpclett.6c00837},
}

Preprint

BatteryMat: A Hierarchical Machine-Learning and DFT Framework for Average-Voltage Screening of Lithium-Ion Cathode Materials

Jaehyung Lee, Charles Rhys Campbell, Kent Zhang, and 1 more author

arXiv preprint arXiv:2607.06645, 2026

arXiv Bib GitHub

@article{lee2026batterymat,
  title = {BatteryMat: A Hierarchical Machine-Learning and DFT Framework for Average-Voltage Screening of Lithium-Ion Cathode Materials},
  author = {Lee, Jaehyung and Campbell, Charles Rhys and Zhang, Kent and Choudhary, Kamal},
  journal = {arXiv preprint arXiv:2607.06645},
  year = {2026},
}

Preprint

SlaKoNet-VQD: A Universal Slater-Koster Tight-Binding Hamiltonian for Variational Quantum Band-Structure Calculations on Near-Term Hardware

Akshaya Ajith, Jaehyung Lee, Charles Rhys Campbell, and 1 more author

arXiv preprint arXiv:2607.09761, 2026

arXiv Bib

@article{ajith2026slakonetvqd,
  title = {SlaKoNet-VQD: A Universal Slater-Koster Tight-Binding Hamiltonian for Variational Quantum Band-Structure Calculations on Near-Term Hardware},
  author = {Ajith, Akshaya and Lee, Jaehyung and Campbell, Charles Rhys and Choudhary, Kamal},
  journal = {arXiv preprint arXiv:2607.09761},
  year = {2026},
}

MLST

Lessons Learned from the 2025 Agentic AI for Science Hackathon

Jaehyung Lee, Harichandaan Neralla, Charles R. Campbell, and 24 more authors

Machine Learning: Science and Technology, 2026

DOI Bib GitHub

@article{lee2026hackathon,
  title = {Lessons Learned from the 2025 Agentic AI for Science Hackathon},
  author = {Lee, Jaehyung and Neralla, Harichandaan and Campbell, Charles R. and Zhang, Kent and Ajith, Akshaya and Ely, Justin and Jiang, Hua and Zhou, Zhijian and Ramlukan, Nikhil and Li, Xiaolong and Chiang, Huat Thart and Kulkarni, Arijit and Thirumalaiswamy, Amruthesh and Pahi, Sampanna and Rahman, Md Habibur and Pratoori, Raghunandan and Huang, Erwei and Bhatti, Asif and Prabhakaran, Prem and Kumar, Manish and Romero, Aldo H. and Sun, Yu and Stiles, Christopher and Maheshwari, Chinmay and Liu, Peirong and Shi, Laixi and Choudhary, Kamal},
  journal = {Machine Learning: Science and Technology},
  year = {2026},
  publisher = {IOP Publishing},
  doi = {10.1088/2632-2153/ae7f6a},
}