David Zhe Gao
Background and activities
My research is focused on the multi-scale modeling of important fundamental physical mechanisms including adsorption, mobility, growth, energy dissipation, and defect creation in a variety of systems. Understanding the mechanisms of these processes is critical in designing novel materials in a wide variety of fields including lubrication, catalysis, and microelectronics.
My background in both industry and academic research are key in allowing me to bridge the gap between fundamental physics and the development of real world technologies. Through my company Nanolayers Research Computing, I aim to incorporate theoretical physics and chemistry methods alongside evolutionary algorithms, machine learning, and data science techniques. In order to effectively leverage data science techniques, I have become very involved within the EU’s H2020 framework in developing data and metadata standards.
Scientific, academic and artistic work
- (2020) Conductivity control via minimally invasive anti-Frenkel defects in a functional oxide. Nature Materials. vol. 19.
- (2019) Mechanisms of oxygen vacancy aggregation in SiO2 and HfO2. Frontiers in Physics. vol. 7.
- (2019) DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications. vol. 247.