The article discusses research conducted by the National Institute of Standards and Technology (NIST) focused on developing advanced computational methods for predicting the structure and properties of various materials. The research team, led by Dr. Kristin Persson, has developed first-principles-based methods to predict atomic arrangements and properties in advanced materials using high-performance computing resources.
Key findings from the research include:
1. High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses
2. Computational search for magnetic and non-magnetic 2D topological materials using unified spin-orbit spillage screening
3. Combined cluster and atomic displacement expansion for solid solutions and magnetism
4. Prediction of Weyl semimetal and antiferromagnetic topological insulator phases in Bi2MnSe4
5. High-throughput first-principles search for new ferroelectrics
6. Local structural distortions and failure of the surface-stress “core-shell” model in brookite titania nanorods
7. Density functional theory meta GGA study of water adsorption in MIL-53(Cr)
8. Structural basis of CO2 adsorption in a flexible metal-organic framework material
9. Accurate band alignment at the amorphous Al2O3/p-Ge(100) interface determined by hard x-ray photoelectron spectroscopy and density functional theory
10. Local atomic geometry and Ti 1s near-edge spectra in PbTiO3 and SrTiO3
These findings contribute to the Materials Genome Initiative and JARVIS (Joint Automated Repository for Various Integrated Simulations) projects, advancing the understanding and prediction of material properties for various applications.
Keywords: Materials, Genome, Density, Functional, Theory
