Shuxia Tao is Assistant Professor of Computational Materials Physics at the Center for Computational Energy Research, Applied Physics, TU/e. Her research interests lie in the development and application of atomistic and multiscale computational methods in the area of novel solar energy conversion and storage technologies. A key feature of all novel solar energy technologies is their highly interdisciplinary nature, at the intersection of chemistry, physics, and materials science. Computational Materials Science is a powerful way to study the interplay of the chemistry and physics of materials, providing new insights into the relation of the atomistic details of materials with their device performances. More details of Shuxia's research can be found at her group site: www.shuxiatao.com.
With a physical chemistry background from Nankai University in China, Shuxia Tao started her PhD in 2007 at Department of Chemical Engineering and Chemistry, TU/e. There she learnt Computational Materials Science and earned her PhD in 2011 with a thesis on hydrogen storage in metal hydrides for battery applications. After a short career break to care for her children, from 2013 to 2016, she worked as a post-doctoral researcher at NWO physics institute NIKHEF for computational materials design for photodetectors. With two prestigious personal grants, CSER tenure track grant in 2016 and NWO START-UP grant in 2019, she established her research group Computational Materials Physics at Department of Applied Physics, TU/e. Her research focuses on the devolepment of atomistic and multiscale computational methods and their application in energy applications.
Absolute energy level positions in tin- and lead-based halide perovskitesNature Communications (2019)
Cation and anion immobilization through chemical bonding enhancement with fluorides for stable halide perovskite solar cellsNature Energy (2019)
Interstitial occupancy by extrinsic alkali cations in Perovskites and its impact on ion migrationAdvanced Materials (2018)
Accurate and efficient band gap predictions of metal halide perovskites using the DFT-1/2 methodScientific Reports (2017)
- Computational materials science
- Editorial Board Memmber, Scientific Reports