Our group at a glance

We are a computational physics / materials science group. We study the structure and properties of different phases of matter and how they can be used in applications, such as novel devices for solar energy conversion, energy storage and heterogeneous catalysis. We employ computational models and tools that span multiple scales from the atomistic to the continuum.  Recently, we have expanded our scope to using machine learning methods in search of new or better materials, and for gaining a deeper understanding of complex solids such as glasses, by exploring large data bases of structures.

Some topics of active research in our group include: properties of two-dimensional weakly-bonded heterostructures; real-time simulation of reaction dynamics on surfaces; simulation of fluids or gases in complex geometries like arteries or nano-porous materials; and the interaction of organic or bio-molecules like DNA with nano-structured materials (find out more in "Research"). 

Google scholar profile -- http://scholar.google.com/citations?user=s9cO1fUAAAAJ

Highlight of recent work

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PCA of Si neural network
Visualization of how a neural network representation interprets the various bulk phases of silicon, identified by the degree of tetrahedral (t-) order. For details see: E.D. Cubuk et al. Journal of Chemical Physics, vol. 147, 024104 (2017).

Research Video on multiscale arterial blood flow