Research
The goal of the Center for Computer Aided Synthesis (C-CAS) is to provide academic and nonacademic researchers tools to significantly enhance the effectiveness of synthesis planning and optimization, thus allowing them to focus on "what should be made and why" and less on "how to make it". We will build on recent methodological advances at the interface of machine learning and chemistry as well as our collaboration with industrial partners to create datasets that bring predictability to how to perform every individual step in a synthetic route.
- Molecular Characterization
- Low data / Transfer Learning (TL) / few-shot
- Course Development
- Reinforcement Learning
- Computational Chemistry and Theory
- Mechanism
- Complex Molecule Synthesis & Retrosynthesis
- DCN Collaborations
- Broadening Participation
- Generative Learning
- Reaction Discovery & Development Optimization
- Automation High-Throughput
- Asymmetric Catalysis
- Science Communication & Social Media
- Explainable Models
- Representation Learning
- Iterative Experimental Design
- Innovation: Internships & Entrepreneurship
- Diversity, Equity, and Inclusion Training
- Uncertainty Quantification (UQ)