THE DOYLE GROUP
Contributing new approaches to chemical synthesis and catalysis by embracing diverse skills and perspectives, and by building a research culture that enables everyone to develop to their highest potential.
The Doyle lab fosters an inclusive and equitable environment in which all members have the opportunity to reach their highest potential as scientists and as people. We believe that a diversity of skills, perspectives, and experiences is critical to the success of our program and constitutes our greatest strength as a group. We welcome and value researchers of any race, color, religion, national origin, gender identity, sexual orientation, physical ability, socioeconomic status, age and more. We strive to develop and maintain a culture of belonging where everyone can flourish by facilitating tailored mentorship, interdisciplinary learning, and a collaborative atmosphere. We are committed to being leaders within the scientific community in promoting these values of inclusivity and equity.
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The Doyle lab conducts research at the interface of organic, organometallic, physical organic, and computational chemistry. Our goal is to address unsolved problems in organic synthesis through the development of catalysts, catalytic reactions, and synthetic methods. We apply mechanistic and computer-assisted techniques to the analysis of these reactions in order to uncover general principles that can guide the design of improved catalysts and the discovery of new reactions. Learn more about our research programs here:
The Doyle Lab is a founding member of two multi-institutional centers that aim to collaboratively address challenges in the field of catalysis. BioInspired Light Escalated Chemistry or BioLEC is an Energy Frontier Research Center aimed to employ advances in photochemistry to enable photoinduced cross-coupling reactions; Center for Computer Assisted Synthesis or C-CAS affords a platform to collaboratively tackle challenges in complex molecule synthesis such as reaction optimization and predicting reactivity and selectivity using data-driven methods.