Seongsu Kim

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Pricinple Investigator: Sungsoo Ahn
Language: Korean, English
Email: seongsu.kim@kaist.ac.kr

Hi, I’m Seongsu. Thank you for stopping by! 👋 The spelling of my name in Korean is “성수” and it is pronounced as [sʌŋ-su:].

I am currently an incoming Ph.D. student in the AI department at KAIST. I am also a member of Structured and Probabilistic Machine Learning (SPML) Lab. I completed my M.S. in Artificial Intelligence at POSTECH.

My research interests include integrating AI into scientific research and using AI to uncover scientific facts. Also, I am interested in physical and chemical concepts like solid state physics, simulation dynamics and molecular science, and also mathematical concepts like group theory, geometry and geometrical deep learning.

Recently, I am focusing on (1) generative modeling for materials and (2) ab-initio method based machine learning for quantum physics and chemistry related with density functional theroy (DFT) or variational Monte Carlo (VMC).

I believe that AI will effectively reduce the complexity of calculations in physics and chemistry, especially in areas like multi-objective optimization problems, challenges in establishing ansatzes, modeling interactions and governing equations, and handling intractable computations. I am studying and researching these possibilities.

I am always open to discussion, so please feel free to reach out anytime!

News

Sep 18, 2025 📋 Two papers were accepted to NeurIPS 2025: one Spotlight (first author) and one Poster (second author).
Jun 20, 2025 🏫 I will be starting my Ph.D. program at KAIST under the supervision of Sungsoo Ahn
May 25, 2025 🐋 Two paper, “High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction” and “Flexible MOF Generation with Torsion-Aware Flow Matching” are updated.
May 20, 2025 📕 Honorably, I was selected as Notable Reviewer at 2025 ICLR!
Jan 23, 2025 💎 One paper, “MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks”, has been accepted to 2025 ICLR.
Oct 31, 2024 💎 One paper, “MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks”, has been accepted to AIDrugX workshop at 2024 NeurIPS.
May 16, 2024 🚀 One paper, “Gaussian Plane-Wave Neural Operator For Electron Density Estimation”, has been accepted to 2024 ICML.

Publications

  1. NeurIPSSpotlight
    High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction
    Seongsu Kim, Nayoung Kim, Dongwoo Kim, and Sungsoo Ahn
    Neural Information Processing Systems (NeurIPS) 2025
  2. NeurIPSPoster
    Flexible MOF Generation with Torsion-Aware Flow Matching
    Nayoung Kim, Seongsu Kim, and Sungsoo Ahn
    Neural Information Processing Systems (NeurIPS) 2025
  3. ICLRPoster
    MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
    Nayoung Kim, Seongsu Kim, Minsu Kim, Jinkyoo Park, and Sungsoo Ahn
    International Conference on Learning Representations (ICLR) 2025
  4. ICMLPoster
    Gaussian Plane-Wave Neural Operator for Electron Density Estimation
    Seongsu Kim, and Sungsoo Ahn
    International Conference on Machine Learning (ICML) 2024