Victor
Fung

General Information

Email:
victorfung@gatech.edu
Phone:
9513845242
Location - Building:
Coda
Location - Room:
E1354B
Roles:
Professor (any rank)
Primary Unit:
College of Computing
Website URL
https://www.fung-group.org/

Details

Degrees with subject and Postdoc Experience:
Degree Type
Ph.D
Subject
Chemistry
Year
2019
Institution
University of California, Riverside
Location
Riverside
Statement of Research Interests:

Finding new materials to serve as the next generation catalysts, batteries, solar cells, superconductors or electronic devices can have a potentially transformative impact on our lives and society. Our group at Georgia Tech seeks to harness the power of computing and machine learning to accelerate this discovery process, with the eventual goal of fully realizing materials by inverse design. We accomplish this by developing novel methods and tools which incorporate chemical information to model phenomena at the atomic scale, as well as design new materials from the ground up, atom-by-atom. We also work to establish automated, data-driven and domain-informed ecosystems for materials and chemical discovery which can be deployed on the latest supercomputers. 

Statement of Teaching Interests:

I teach computational problem solving, an undergraduate course. I also teach AI for materials science and chemistry, a graduate level course.

Selection of recent research, scholarly, and creative activities:
@article{liu2025roft,
  title={RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models},
  author={Liu, Shikun and Zou, Deyu and Shoghi, Nima and Fung, Victor and Liu, Kai and Li, Pan},
  journal={arXiv preprint arXiv:2509.00614},
  year={2025}
}

@article{jia2025pre,
  title={Pre-training graph neural networks with structural fingerprints for materials discovery},
  author={Jia, Shuyi and Govil, Shitij and Ramprasad, Manav and Fung, Victor},
  journal={arXiv preprint arXiv:2503.01227},
  year={2025}
}

@article{jia2025electronic,
  title={Electronic Structure Guided Inverse Design Using Generative Models},
  author={Jia, Shuyi and Ganesh, Panchapakesan and Fung, Victor},
  journal={arXiv preprint arXiv:2504.06249},
  year={2025}
}

@article{kong2025mattertune,
  title={Mattertune: An integrated, user-friendly platform for fine-tuning atomistic foundation models to accelerate materials simulation and discovery},
  author={Kong, Lingyu and Shoghi, Nima and Hu, Guoxiang and Li, Pan and Fung, Victor},
  journal={arXiv preprint arXiv:2504.10655},
  year={2025}
}

@article{anam2025comprehensive,
  title={A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons},
  author={Anam, Md Zaibul and Aghoghovbia, Ogheneyoma and Al-Fahdi, Mohammed and Kong, Lingyu and Fung, Victor and Hu, Ming},
  journal={Advanced Intelligent Discovery},
  pages={e202500075},
  year={2025}
}

@article{kong2025scalable,
  title={Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning},
  author={Kong, Lingyu and Shim, Jaeheon and Hu, Guoxiang and Fung, Victor},
  journal={arXiv preprint arXiv:2509.21694},
  year={2025}
}