Multiscale modeling for materials design: from density functional theory (DFT) to coarse grained molecular dynamics (CGMD)
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The accelerated discovery of advanced materials is essential for addressing global challenges in energy, health, and sustainability. However, traditional design paradigms, which rely solely on trial-and-error experimentation, struggle to navigate the vast and complex chemical design space efficiently. This dissertation presents a multiscale computational framework that integrates density functional theory (DFT), molecular dynamics (MD), coarse-grained (CG) simulations, and artificial intelligence (AI) to enable more rapid and rational materials design. Specifically, the dissertation covers the following topics: (1) employing DFT to design cobalt-based single-atom catalysts for lithium–sulfur (Li–S) batteries; (2) using first-principles calculations to elucidate ionic interactions in fast Li-ion-conducting molecular crystals and neuromorphic devices; (3) developing accurate interatomic potentials for binary alloys by combining DFT datasets with AI-based force field fitting; (4) applying replica exchange MD to investigate the conformational landscape of silk–elastin-like proteins (SELPs); and (5) integrating all-atom MD and CG simulations to guide the discovery of antiviral small molecules. Together, these studies demonstrate the power of combining physics-based simulations with data-driven approaches to accelerate the design of functional materials across multiple length and time scales.