Theoretical and Computational Soft Matter
How can materials dynamically control or remodel their own internal structure to affect their behavior? How can the statistics of structural disorder be biased to produce non-trivial properties? How can one discover novel equilibrium and non-equilibrium assembly mechanisms in highly parameterized systems? Questions like these are a necessary step in the development of synthetic biology, where non-biological materials and nano-scale machines operate with the complexity and functionality found only in biology.
Towards this end, the Goodrich Group uses computational and theoretical tools to discover basic soft matter principles that could one day lead to new functional materials as well as deepen our understanding of complex biological matter. The goal is to first understand general or even universal mechanisms that are not overly sensitive to the details of a given experimental system, and then work with experimentalists to test these ideas in practice. The group deploys and develops a number of numerical techniques, from molecular dynamics and Monte Carlo to machine learning and automatic differentiation. Specifically, the researchers are at the forefront in the development of trainable physics models, which provide a new and powerful way to explore high-dimensional systems and discover complex, non-trivial phenomena.
On this site:
The Goodrich Group currently has a opening for a postdoc position.
Interested candidates can apply by sending a short email to email@example.com (with firstname.lastname@example.org in CC), with a CV that includes publications and contact information for at least 3 references. Applications received before March 14, 2021 will received full consideration. Women and those from underrepresented groups are especially encouraged to apply.
More information about the group an their research can be found on their website.
Goodrich CP, King EM, Schoenholz SS, Cubuk ED, Brenner MP. 2021. Designing self-assembling kinetics with differentiable statistical physics models. PNAS. 118(10), e2024083118. View
Since September 2020 Assistant Professor, IST Austria
2015-2020 Postdoctoral Scholar at Harvard University, Cambridge, MA USA
2009-2015 Ph.D. in physics at the University of Pennsylvania, Philadelphia, PA USA
2005-2009 B.S. in physics at Syracuse University, Syracuse, NY USA