Yimeng Min Ph.D.

Research Interests

My work sits at the intersection of artificial intelligence and its applications in the natural sciences. I draw on physics, geometry, and dynamical systems to build more efficient generative models, such as flow matching, and to understand why they work. I am also interested in AI systems that are physically grounded rather than purely data-driven. In AI for Science and Sustainability, I use AI-guided discovery to find new functional materials, validated by real experiments.

Some current research directions include:

  • AI for Science & Scientific Machine Learning
  • Computational Sustainability
  • Generative Models
  • Geometric Deep Learning

Openings

I am recruiting several Ph.D. students to start in 2027 in Computer Science and Engineering at Texas A&M. Postdocs are welcome to reach out, as are master’s and undergraduate students. How to join →

Selected Publications

  1. Miao Zhong*, Kevin Tran*, Yimeng Min*, Chuanhao Wang*, et al. “Accelerated Discovery of CO2 Electrocatalysts Using Active Machine Learning,” Nature, 2020.
    Details

    We show that machine learning can accelerate the search. Using computer models and theoretical data, algorithms can toss out the worst options and point the way toward more promising candidates. The new catalyst we found is the first one for CO2-to-ethylene conversion to have been designed in part through the use of AI.

  2. Yimeng Min, Carla P. Gomes. “Learning Unbiased Permutations via Flow Matching,” NeurIPS, 2026.
  3. Yimeng Min, Carla P. Gomes. “Structure as Search: Unsupervised Permutation Learning for Combinatorial Optimization,” New Perspectives in Graph Machine Learning workshop at NeurIPS, 2025.
  4. Yimeng Min*, Yiwei Bai*, Carla P. Gomes. “Unsupervised Learning for Solving the Travelling Salesman Problem,” NeurIPS, 2023.
  5. Yimeng Min*, Frederik Wenkel*, Michael Perlmutter, Guy Wolf. “Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?” NeurIPS, 2022.
  6. Yimeng Min*, Frederik Wenkel*, Guy Wolf. “Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks,” NeurIPS, 2020.

*Equal contribution

Full list of publications

© 2026 Yimeng Min