Governance
AI project review and portfolio management
I support structured evaluation of AI initiatives, helping teams move from experimentation toward scoped, reviewable, and strategically aligned execution.
AI Governance • Scientific Computing • Materials Discovery
Hyun-Jung Kim
I work at the boundary of computational materials physics and enterprise AI adoption, translating rigorous research methods into practical systems, education, and governance.
At LG Display, I lead workstreams across AI project review, technical scouting, committee operations, and internal AI capability building. My research background spans first-principles simulation, condensed-matter theory, open-source scientific tooling, and long-horizon work on inverse design for molecular and materials discovery.
Current Work
Bringing research discipline into enterprise AI adoption.
The present focus is not a generic AI showcase. It is careful institutional work: building review structures, teaching usable skills, and identifying where advanced methods can create real technical advantage.
Education
Internal teaching for AI, automation, and LLM applications
I design and deliver internal training on AI and ML fundamentals, workflow automation, and LLM application development so adoption can happen with technical clarity rather than hype.
Discovery
Materials discovery with a long-horizon view
My research agenda continues to explore AI-enabled inverse design and quantum-ready optimization frameworks for OLED-relevant molecular and materials problems.
Trajectory
A career shaped by theory, simulation, and translation.
The throughline is consistent: rigorous modeling, transferable workflows, and a bias toward building systems other people can actually use.
2025–Present
AI Governance Team, LG Display
Workstream lead covering AI project review, technical scouting, committee operations, and early-stage exploration of quantum-computing use cases and external collaboration paths.
2022–2024
OLED Materials Research, LG Display
Applied quantum-chemical and DFT-driven analysis to molecular design problems, linking computation, mechanism analysis, and data-driven screening for industrial R&D.
2020–2022
Forschungszentrum Jülich
Visiting scientist and postdoctoral researcher at PGI-1 and IAS-1, supported by the Humboldt Research Fellowship, working on electronic structure, topology, and transferable modeling.
2015–2020
Korea Institute for Advanced Study
Postdoctoral researcher and research fellow in computational sciences and the Quantum Universe Center, developing theory-driven workflows for low-dimensional and topological materials.
2009–2015
Hanyang University
M.S. and Ph.D. in theoretical condensed matter physics, building the academic foundation in electronic structure, low-dimensional systems, and surface science.
Scholarship
Selected publications, tools, and research themes.
This public page is intentionally selective. The full publication list, talks, and detailed academic record remain in the downloadable CV.
Selected publications
- Machine Learning Strategy Towards Inverse Design of Blue TADF Emitter SID Symposium Digest of Technical Papers, 2024.
- Simultaneous enhancement in electrical conductivity and Seebeck coefficient by single- to double-valley transition in a Dirac-like band npj Computational Materials, 2022.
- Circular Dichroism of Emergent Chiral Stacking Orders in Quasi-One-Dimensional Charge Density Waves Physical Review Letters, 2022.
Open-source research tools
Research themes
Computational materials science, first-principles simulation, tight-binding workflows, inverse design, AI and ML for materials discovery, quantum optimization, and scientific software that remains interpretable and reusable.
DFT and quantum chemistry
AI/ML workflows
Inverse design
Topological materials
Open-source tooling
Selected venues across the record include Physical Review Letters, Physical Review B, Physical Review Materials, ACS journals, Scientific Reports, and npj Computational Materials.
Contact
Available for conversation where science, systems, and AI meet.
This site is designed as a calm public introduction. For the full record, use the CV. For direct contact, the links below are the best entry points.
Reach out
Working principle
Adopt new tools carefully. Keep the reasoning visible. Teach people well enough that the system keeps working after the first demo.