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I am an Assistant Professor at KAIST Graduate School of AI, where I direct the Structured and Probabilistic Machine Learning (SPML) Lab.

My research focuses on structured, probabilistic machine learning for atomic- and electron-level modeling, with applications to drug discovery and material design. I enjoy bringing this perspective to scientific problems and diving deep into the underlying physics, chemistry, and biology.

Members

Yunhui Jang, Hyosoon Jang, Hyomin Kim, Seonghyun Park, Seongsu Kim, Kiyoung Seong, Minkyu Kim, Dongyeop Woo, Nayoung Kim, Minsu Kim (Postdoc), Taewon Kim, Hyunjin Seo, Yinhua Piao (Postdoc), Yoonho Kim, Honghui Kim (Postdoc)

Alumni: Haeji Ko, Juwon Hwang

Selected Highlights

  1. INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery
    Yunhui Jang, Seonghyun Park, Jaehyung Kim, and Sungsoo Ahn
    [arxiv / code / project]
  2. Riemannian MeanFlow (ICML 2026)
    Dongyeop Woo, Marta Skreta, Seonghyun Park, Kirill Neklyudov†, and Sungsoo Ahn†
    † co-corresponding authors
    [arxiv / code]
  3. Machine Learning Hamiltonians are Accurate Energy-Force Predictors (ICML 2026)
    Seongsu Kim, Chanhui Lee, Yoonho Kim, Seongjun Yun, Honghui Kim, Nayoung Kim, Changyoung Park, Sehui Han, Sungbin Lim†, and Sungsoo Ahn†
    † co-corresponding authors
    [arxiv / code]
  4. AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction
    Nayoung Kim, Honghui Kim, Sihyun Yu, Minkyu Kim, Seongsu Kim, and Sungsoo Ahn
    [arxiv / code]
  5. CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching (ICML 2026)
    Minkyu Kim, Nayoung Kim, Honghui Kim, and Sungsoo Ahn
    [arxiv / code]
  6. Learning Collective Variables from BioEmu with Time-Lagged Generation (ICLR 2026)
    Seonghyun Park, Kiyoung Seong, Soojung Yang, Rafael Gomez-Bombarelli, and Sungsoo Ahn
    [arxiv / code]
  7. Flexible MOF Generation with Torsion-Aware Flow Matching (NeurIPS 2025)
    Nayoung Kim, Seongsu Kim, and Sungsoo Ahn
    [arxiv / code]
  8. High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction (NeurIPS 2025)
    Seongsu Kim, Nayoung Kim, Dongwoo Kim, and Sungsoo Ahn
    Spotlight presentation
    [arxiv / code]
  9. MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks (ICLR 2025)
    Nayoung Kim, Seongsu Kim, Minsu Kim, Jinkyoo Park, and Sungsoo Ahn
    [arxiv / code]

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