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Cheng Group

Computational Materials Science

The building blocks of matter are electrons and atomic nuclei, whose behavior follows the laws of quantum mechanics. By solving the Schrödinger equation, one can predict the properties of any material, including existing or novel compounds yet to be synthesized. However, there is a catch. As the number of electrons and nuclei increases, the complexity involved in solving the equation soon becomes intractable even with the fastest supercomputers. In fact, atomistic simulations based on quantum mechanics are still unaffordable for systems with more than a few hundred atoms, or for a time period longer than a nanosecond.

The Cheng group is particularly interested in developing methods to extend the scope of atomistic simulations, in order to understand and predict materials properties that are hard to access. The group deploys and designs a combination of techniques encompassing machine learning, enhanced sampling, path-integral molecular dynamics, and free energy estimation. The systems of study include energy materials, aqueous systems, and matter under extreme conditions.


Current Projects

Machine-learning potentials for functional materials | Transport phenomena at the microscale | Efficient statistical learning of materials properties | Developing advanced methods for statistical mechanics and atomistic simulations


Dong H, Shi Y, Ying P, Xu K, Liang T, Wang Y, Zeng Z, Wu X, Zhou W, Xiong S, Chen S, Fan Z. 2024. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials. Journal of Applied Physics. 135(16), 161101. View

Zeng Z, Shen X, Cheng R, Perez O, Ouyang N, Fan Z, Lemoine P, Raveau B, Guilmeau E, Chen Y. 2024. Pushing thermal conductivity to its lower limit in crystals with simple structures. Nature Communications. 15, 3007. View

Cheng R, Zeng Z, Wang C, Ouyang N, Chen Y. 2024. Impact of strain-insensitive low-frequency phonon modes on lattice thermal transport in AxXB6-type perovskites. Physical Review B. 109(5), 054305. View

Reinhardt A, Chew PY, Cheng B. 2023. A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals. Journal of Chemical Physics. 159(18), 184110. View

Ouyang N, Zeng Z, Wang C, Wang Q, Chen Y. 2023. Role of high-order lattice anharmonicity in the phonon thermal transport of silver halide AgX (X=Cl,Br, I). Physical Review B. 108(17), 174302. View

View All Publications

ReX-Link: Bingqing Cheng

Google Scholar


Since 2024 Visiting Professor, Institute of Science and Technology Austria (ISTA), Assistant Professor, University of California, Berkeley, USA
2021 – 2024 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2020 – 2021 Departmental Early Career Fellow, University of Cambridge, UK
2019 Junior Research Fellow, Trinity College, University of Cambridge, UK
2014 – 2019 Ph.D. in Materials Science, EPFL, Switzerland

Selected Distinctions

2023 ERC Starting Grant
2022 JCP Best Paper by Emerging Investigator Award
2021 Volker Heine Young Investigator Award
2019 Trinity College Junior Research Fellowship
2019 Distinction Prize 8% for PhD thesis, the Doctoral School of EPFL
2018 Early Postdoc.Mobility Fellowship (Swiss National Science Foundation)
2014 Award for Outstanding Research Postgraduate Student, University of Hong Kong

Additional Information

Open Bingqing Cheng’s website
Physics & Beyond at ISTA

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