Traditional avenues of advanced materials development are slow and costly. Broadly based efforts, such as the Materials Genome Initiative, are attempting to expedite discovery by applying modern computational methods to identification and characterization of novel material systems. In this context, the NIST/TRC Group is building capabilities in computational materials science to couple with a substantial effort to develop a comprehensive database of thermophysical and transport properties of alloys and composites.
Programmatic interests focus on determination of structural, thermophysical, and transport properties of systems and materials. We also aim to enable materials design from the function-structure relationship standpoint, as applied to metal alloys, carbon-based composites, and solid-state-biomolecule hybrid structures.
Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive empirical models that take advantage of large collections of experimental data compiled by the NIST/TRC Group.
Our theory and simulation effort is at the cutting edge of the intersection of thermodynamics, physical chemistry, and mesoscale physics. We employ a set of rigorous top-down methods, ranging from analytical theory calculations to large-scale atomistic and density functional theory simulations. A special focus is on thermal transport and interfacial phenomena for a variety of applications, including nanotechnology, energy, and sensing. In addition, we seek to improve the existing atomistic simulation methodologies via standardized, system-specific refinement of interatomic interactions.
Deng Z, Smolyanitsky A, et al: Nature Materials 11(12): 2012
Smolyanitsky A, Killgore JP: Physical Review B 86(12): 125432, 2012
Alloys; Thermodynamics; Simulation; Carbon; Composites; Theory; Metals; Thermophysical properties;