Welcoming new faces to MatSE
is an assistant professor and a 2020 Institute for Computational and Data Sciences Faculty Fellow. Prior to joining Penn State, he worked as a research scientist at Siemens Corporate Technology. There, his research initiatives focused on computational geometry, knowledge representation, and exploiting the structure-function relationship in manufacturing contexts.
Reinhart’s research is interdisciplinary by nature and uses a data-driven approach to facilitate the design, manufacture, and maintenance of advanced materials, whose sought-after functions and properties will be derived from their yet-unknown internal structure. This relationship between structure and function is challenging to understand and even harder to predict because it is nonlinear, high dimensional, and results from physical phenomena at many scales. Traditional materials design has relied on human intuition to interpret patterns in known structure-function pairs and infer new materials with similar and hopefully improved properties, Reinhart’s group aims to use a combination of high-performance physics simulation and data science approaches to arrive at efficient representations of materials that will enable true inverse design of micro-structure.
“This means I use conventional, physics-based simulations to understand the way materials behave at the micro-, meso-, and macro-scales, but augment our predictive capability using machine learning to find new and improved materials for a variety of applications,” said Reinhart. “These can include new metals for additive manufacturing, semiconductors for optical computing, or even new building materials for homes or transportation infrastructure.”
Reinhart received his B.S. in chemical engineering from the University of Minnesota Twin Cities. He attended Princeton University on a National Defense Science and Engineering Graduate Fellowship, where he worked on strategies for predicting, understanding, and controlling colloidal crystallization using large-scale computer simulations and machine learning methods, obtaining his Ph.D. in 2019.
Learn more about the Reinhart Group