Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
-
NIST only participates in the February and August reviews. There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https
-
. The objective of this PhD project is to use high pressure to obtain new polymorphic forms of poorly soluble APIs, combining experimental investigations and atomistic simulations. Two experimental approaches will
-
challenging. We seek to address this measurement problem by developing a coherent strategy for integrating inputs from several critical experimental techniques to perform fully atomistic structural refinements
-
approach must be combined with mechanistic models that describe the specific microstructure elements. A variety of inputs from both experimental work and simulations (i.e., first principle, atomistic, and/or
-
Offer Description Development of atomistic ab-initio simulations and machine learning models for the study of phonon transport, phase transitions, and structural optimization of phase change materials
-
), uncertainty quantification, and atomistic simulations within the FNR-funded UMLFF project. MLFFs have transformed atomistic simulations, offering quantum-chemical accuracy for large systems. However, they
-
atomistic tight-binding and multi-bands k.p models for the electronic structure of the materials. Using TB_Sim, CEA has made significant progress in the understanding of various aspects of the physics of spin
-
for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
-
states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic