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Completed TRL 3 (started at 2, targeting 3)
Modern computational methods, most notably molecular dynamics, have been used extensively to understand the microscopic behavior of atomic systems. However, despite the fact that these methods are far more computationally tractable than higher levels of theory, physically relevant spatial and temporal scales on the order of millions of atoms over the span of microseconds or longer are inaccessible. These regimes are particularly relevant in settings such as gas separation membranes, thermal transport in self-healing materials, or flexible solid state electrolytes for Lithium ion batteries.
In this work, we propose to develop a framework with which larger spatial and temporal scales can be accessed with molecular dynamics. We will use cutting-edge machine learning techniques, along with a rigorous mathematical formalism, to develop the computational tools needed in order to reduce the dimensionality of all-atom simulations. This dimensionality reduction, termed coarse graining, will provide the link between nano- and micro-scale systems that will allow NASA to design better materials with multiple functionalities, as well as perform large-scale screening of materials based on desired performance metrics.
Develop the computational tools needed in order to reduce the dimensionality of all-atom simulations. This dimensionality reduction, termed coarse graining, will provide the link between nano- and micro-scale systems that will allow NASA to design better materials with multiple functionalities, as well as perform large-scale screening of materials based on desired performance metrics.
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