Background
The recent decade has been a "golden age" for development of novel methods and tools in materials informatics. Among many other advances, the web-based and distributed databases with various properties of molecules and compounds of interest for chemical and biological areas of research have been expanding at a rapid pace.
Alongside these advances, design of novel and optimization of the existing computational methods, based on computational high-throughput screening and machine learning, have been on the rise.
These advances have opened multitude of novel venues in the research areas encompassing computational materials design and optimization within the paradigms of computational large-scale high-throughput screening and Big Data-based machine-learning.
Systems
Our group embarks upon the aforementioned themes of research, with the particular focus put on the computational design and optimization of liquid electrolytes for energy storage devices.
We study the physics and chemistry of materials in electrolytes and electrode-electrolyte interfaces using atomistic computational methods and high-performance computing technology.
Tools
Our main tools are density functional theory (DFT) and molecular dynamics (MD), but we also use a range of machine learning (ML) methods to take advantage of large databases.
We collaborate heavily with experimental groups to ensure our materials make it out from the computer to the laboratory.