LabCompass

Brian H. Lee

Materials Science · Purdue University West Lafayette

OpenAlex ORCID
Early career · publishing since 2019Rising activity
Machine Learning in Materials ScienceGold and Silver Nanoparticles Synthesis and ApplicationsSurfactants and Colloidal SystemsX-ray Diffraction in CrystallographyGraphene research and applications

Publications

21

Citations

147

Est. group size

~1

Recurring co-author estimate

Active years

7

Publishing since 2019

Research summary
AI-generated

Brian H. Lee's work spans computational and materials science topics, including machine learning applied to materials discovery, molecular and mesoscale modeling of energetic materials (like RDX), nanoparticle self-assembly, and 2D material simulations. Recent projects use methods such as graph neural networks, generative models, and active learning workflows to predict material properties and behavior. The work combines computational modeling with data-driven techniques across a range of material systems, from nanoparticles to crystalline explosives to 2D semiconductors.

Machine learning for materials discoveryCoarse-grained and molecular simulation of energetic materialsNanoparticle self-assembly and colloidal systems2D materials and transition metal dichalcogenidesGenerative and graph-based modeling methods

Publication output has grown over the last decade, rising from no recorded publications in 2017-2018 to a peak of 5 in 2024, averaging under 3 papers per year over the last 5 years.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 2.8/year recently
17182019: 2 publications192020: 2 publications202021: 3 publications212022: 3 publications222023: 2 publications232024: 5 publications5242025: 4 publications2526
Recent publications
Publishes in
  • arXiv (Cornell University)×6
  • Nanoscale×3
  • npj 2D Materials and Applications×2
  • ACS Nano×2
  • Journal of Applied Physics×2
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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