Sort by
Refine Your Search
-
-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Machine learning; Density functional theory; force
-
Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 10 days ago
). This activity will be supported by ab initio calculations using density functional theory or many-body Green-function methods that take into account electronic correlations (coll. M. Helgren, B. Lenz and M
-
, HEP-Theory (hep-th) , High energy density matter , High Energy Experimental , High Energy Physics , high energy physics or mathematical physics , High Energy Theory , High Energy Theory Group , High
-
Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
-
NIST only participates in the February and August reviews. First principles calculations, usually based on density functional theory (DFT), are a crucial aspect of modern materials physics research
-
will work with future emerging topics in High Energy Particle Physics and Quantum Physics. The aim of the research is to develop the underlying theory, simulations and Artificial Intelligence (AI
-
to determine these materials’ chemical structure and its effect on their properties. This project will use theoretical modelling (density-functional theory and Monte Carlo calculations) to investigate
-
in structure characterization of homogeneous and heterogeneous catalysts using nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) to deduce structure-property
-
. Advisers name email phone Carelyn E. Campbell carelyn.campbell@nist.gov 301.975.4920 Description First principles electronic structure methods such as density functional theory (DFT) are crucial
-
behavior. You will combine density functional theory, thermodynamics, and automated Python-based workflows to generate physically grounded datasets describing oxidation states, defect formation, and surface