Models of Molecular Systems: from Newtonian Mechanics to Machine Learning

2-0-7 units · Undergraduate

Seminar-style subject concentrating on modeling creatively while understanding the intrinsic limitations of modeling and alternative ways of envisioning the world. Addresses the purpose of models from different perspectives, with a focus on open-ended problems and creative solutions. Investigates ancient and contemporary approaches, starting with the limitations of Newtonian mechanics to treat molecular systems and solutions provided by statistical mechanics and quantum mechanics, including their use in computations and simulations, and Aristotle's approach. Also covers machine learning and its limitations. Foundational readings inform the analyses with applications including molecular science, color, motion, biology, and nature broadly. Work consists of weekly assignments, class participation, and a final project.