Research
We seek to precisely control the structure of organic materials from the bottom up (synthesis & molecular design) and from the top down (formulation & processing). In particular, we are interested in organic mixed ionic-electronic conducting (OMIEC) materials.
To accelerate the process of materials design and discovery, we build self-driving laboratories (SDLs) – automated experiments guided by machine learning (ML) algorithms. To this end, we also develop SDL technologies and materials informatics methods.
Conjugated Polymer Structure from Sequence
We synthesize conjugated polymers with precisely defined length and sequence. Similar to biomolecules, we can use the sequence of building blocks to build 3D structure from sequence.
Complex Formulation and Processing of OMIEC Thin Films
The properties of many commercial materials are precisely tuned by carefully optimizing their composition, formulation, and processing, often requiring multiple different components to achieve the ideal properties. We leverage the strengths of automation to precisely control thin film microstructure with complex formulation and processing steps.
Self-Driving Laboratories: Accelerating Materials Design
We develop the underlying methods and technologies that will enable broad use of SDLs. Areas of interest include: developing new automated experiments and materials informatics approaches, as well as using interpretable ML to further scientific understanding.