About us
The Computational Materials Physics Group of Shuxia Tao works on the understanding of the process-structure-property-performance relationship of solid-state materials for energy applications. We develop and use multiscale methods, combining quantum methods e.g. Density Functional Theory with classical methods such as Molecular Dynamics and Monte Carlo, to study the complex interplay of chemistry and physics of materials at the nanoscale. Currently, our main focus is perovskite solar cells. We are a part of Materials Simulation & Modelling at the Department of Applied Physics at Eindhoven University of Technology (TU/e) and a member of Center for Computational Energy Research.
Highlights
Temperature dependent chirality in halide perovskites
Using MD simulations with machine-learned potentials, Mike uncovers the temperature dependence of chirality in chiral perovskites.
Defects in perovskites: 2D vs 3D
In ACS Energy Letters, Haibo studied the thermodynamics of defects in 2D perovskites and found defects are more difficult to form but potentially more harmful.
Machine learning for defects
Mike and Victor show how machine learning helps creating force fields with ab-initio accuracy to describe defect dynamics in perovskites.
Theory of phase separation
Zehua developed a unified thermodynamic theory for the segregation of mixed halide perovskites, published on Nature Communications.
Stabilizing perovskite LEDs
On Nature Communications, Sofia shows PAAI long-chain molecule improves operation lifetime of perovskite LEDs.
Understanding crystallization
On Advanced Materials, Haibo studies the impact of crystallization kinetics on the quality and the morphology of perovskite films.
The absolute energy levels
The absolute energy levels of 18 halide perovskites is published on Nature Communications.
Fluoride protects perovskite PV
Our article NaF improving the stability of perovskite solar cells is published on Nature Energy.
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