Prof. Brett A McGuire

Class of 1943 Career Development Assistant Professor of Chemistry

Research Summary

Research in the McGuire Group at MIT uses the tools of physical chemistry, molecular spectroscopy, and observational astrophysics to understand how the chemical ingredients for life evolve with and help shape the formation of stars and planets.

In the laboratory, we use high-resolution rotational spectroscopy to enable the detection of new molecules in the interstellar medium. We exploit automation, signal processing, and statistical tools to produce, identify, and measure new potential species in an unbiased fashion. We then endeavor to determine how the results of these experiments inform our understanding of the underlying fundamental chemical processes.

Observationally, we use high-resolution radio astronomical observations to study the rotational spectra of molecules in interstellar sources. We seek to know what molecules are present and to understand how they got there and where they are going, both in terms of the chemical and physical evolution of the source. The fundamental experiment is the detection of new molecular species. Each new detection imparts momentum of the field of astrochemistry, sometimes subtly pushing, other times forcefully driving research to follow the new discoveries.

Recent Work

  • Video

    2024 MIT R&D Conference: Track 1 - Space - Automating the Identification of Chemical Mixture Components with Machine Learning

    November 19, 2024Conference Video Duration: 38:4
    Automating the Identification of Chemical Mixture Components with Machine Learning
    Brett McGuire
    Class of 1943 Career Development Assistant Professor, MIT Department of Chemistry
    Identifying the precise chemical makeup of complex mixtures is of interest in fields ranging from atmospheric chemistry to pharmaceutical development and quality control to my own field of astrochemistry.  A variety of analytical tools such as spectroscopy, mass spectrometry, nuclear magnetic resonance, and chromatography provide chemical "fingerprinting," which can, in theory, be used to identify these chemical components, but the sheer density of spectral features of different molecules that are often present in such readings can make unambiguous assignment to individual species challenging. Yet, the components are commonly chemically related due to the shared chemical evolution of the mixture. Therefore, along with investigating the analytical signals, analysis of the structural and chemical relevance of a molecule is an important consideration when determining which species are present in a given mixture. My group works primarily in applications of rotational spectroscopy, and thus, in this talk, I will present a method that combines machine-learning molecular embedding models with a graph-based ranking system to determine the likelihood of a molecule being present in a pure rotational spectrum based on the other known species, chemical priors, and spectroscopic information.  I'll present details on the process as well as demonstrate its utility on both laboratory mixtures and astrochemical observations from space.  Our work demonstrates that the chemical inventory can be identified with extremely high accuracy in a much more efficient manner than manual analysis.