We are a computational research group working at the interface between machine learning and atomistic simulations. We use the tools of data science and engineering as well as physics-based simulations like density functional theory and molecular dynamics to design and understand materials.
Designing for function.
We use computational tools to tackle design of materials in complex combinatorial search spaces, such as organic electronic materials, energy storage polymers and molecules, and heterogeneous (electro)catalysts. In addition to screening large numbers of possible candidates with forward models (given a material, predict its properties), machine learning tools allow us to address the inverse design question, that is, given the desired properties, imagine the material.
The combination of large experimental datasets and accurate theoretical simulations with statistical inference allows computers to answer fundamental scientific questions like never before. We ask questions about the nature of chemical reactivity, materials transformations, and ion transport.