UChicago PME Materials Special Seminar- Tristan Bereau

The Unreasonable Effectiveness of Multiscale Modeling in Soft-Materials Discovery: Advanced statistical methods are rapidly impacting many scientific fields, offering new perspectives on long-standing problems. In materials science, data-driven methods are already bearing fruit in various disciplines, such as hard condensed matter or inorganic chemistry, while comparatively less has happened in soft matter. I will describe how we use multiscale simulations together with data-driven methods in soft matter. We aim at establishing structure-property relationships for complex thermodynamic processes across the chemical space of small molecules. Akin to screening experiments, we devise a high-throughput coarse-grained simulation framework. Coarse-graining is an appealing screening strategy for two main reasons: it significantly reduces the size of chemical space and it can suggest a low-dimensional representation of the structure-property relationship. I will report on a biological application of our methodology that led to the discovery of an in vivo active compound. Finally, I will outline complementary contributions, including FAIR simulation workflows and neural samplers from deep generative machine learning methods.