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.
On this site:
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
Since September 2021 Assistant Professor, IST Austria
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
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