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.
Team
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
Publications
Cheng B. 2024. Response matching for generating materials and molecules. Journal of Chemical Theory and Computation. View
Cheng B. 2024. Cartesian atomic cluster expansion for machine learning interatomic potentials. npj Computational Materials. 10, 157. View
Wang X, Cheng B. 2024. Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. Journal of Chemical Physics. 161(3), 034111. View
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
ReX-Link: Bingqing Cheng
Career
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
