About me

Yangzesheng (Andrew) Sun

Ph. D. Student

Department of Chemistry &
Department of Computer Science and Engineering,
University of Minnesota

Contact: sun00032 [at] umn [dot] edu

Github    Linkedin    Twitter    Youtube    [CV]


I am a PhD student in Computational Chemistry and MSc student in Computer Science at University of Minnesota Twin Cities, advised by J. Ilja Siepmann. My research interests include physics-informed machine learning, physically-based simulations, and high-performance computing. My PhD research is supported by the Nanoporous Materials Genome Center. I received Bachelor's degree in Chemistry with Honors from Wuhan University in 2017.

Apart from my PhD research I am also interested in computer graphics, game development, and graphics design. I am an active game modding developer for Cities: Skylines, one of the most popular city-building games in the past decade. My primary modding project, Cities: Skylines Urban Road (CSUR), has more than 35,000 users (cumulative) on the Steam Workshop.

I am looking for full-time positions in simulation and machine learning for game development, content creation, and digital twin modeling starting Spring 2022 or later. I would greatly appreciate it if you can contact me about related opportunities.


Click here to learn about my PhD research.


Click here to view my software projects.


Click here to view my design, illustration, and modeling works.


Peer-reviewed conferences (proceedings & workshops)

  • Sun, Y.-Z.-S.; Josephson, T. R.; Siepmann, J. I. Interpretable Learning of Complex Multicomponent Adsorption Equilibria from Self-attention, NeurIPS 2020 Machine Learning for Molecules Workshop, 2020. [Paper] [Poster]
  • Sun, Y.-Z.-S.; DeJaco, R. F.; Siepmann, J. I. Predicting hydrogen storage in nanoporous materials using meta-learning, NeurIPS 2019 Machine Learning and the Physical Sciences Workshop, Vancouver, Canada, 2019. [Paper] [Poster]

Scientific journals

  • Sun, Y.-Z.-S.; DeJaco, R. F.; Li, Z.; Tang, D.; Glante, S.; Sholl, D. S.; Colina, C. M.; Snurr, R. Q.; Thommes, M.; Hartmann, M.; Siepmann, J. I. Fingerprinting nanoporous materials for hydrogen storage using meta-learning, Science Advances, under review.
  • Rahbari, A.; Josephson, T. R.; Sun, Y.-Z.-S.; Moultos, O.A.; Dubbeldam, D.; Siepmann, J. I.; Vlugt, T. J. H. Multiple linear regression and thermodynamic fluctuations are equivalent for computing thermodynamic derivatives from molecular simulation, Fluid Phase Equilibria 2020, 112785. [Paper]
  • Eggimann, B. L.; Sun, Y.-Z.-S.; DeJaco, R. F.; Singh, R.; Ahsan, M.; Josephson, T. R.; Siepmann, J. I. Assessing the quality of molecular simulations for vapor–liquid equilibria: an analysis of the TraPPE database, Journal of Chemical & Engineering Data 2020, 65, 1330–1344. [Paper]
  • Sun, Y.-Z.-S.; DeJaco, R. F; Siepmann, J. I. Deep neural network learning of complex binary sorption equilibria from molecular simulation data, Chemical Science 2019, 10, 4377–4388. [Paper] [Journal Front Cover]
  • Peng, S.; Bie, B.; Sun, Y.-Z.-S.; Liu, M.; Cong, H.; Zhou, W.; Xia, Y.; Tang, H.; Deng, H.; Zhou, X. Metal-organic frameworks for precise inclusion of single-stranded DNA and transfection in immune cells, Nature Communications 2018, 9, 1293.
  • Dong, Z.; Sun, Y.-Z.-S.; Chu, J.; Zhang, X.; Deng, H. Multivariate metal-organic frameworks for dialing-in the binding and programming the release of drug molecules, Journal of the American Chemical Society 2017, 139, 14209–14216.