Sort by
Refine Your Search
-
to predict materials properties is essential to improve materials design methods. This research will focus on the development and integration of first principle calculations; atomistic simulations; and/or
-
supports US Semiconductor Manufacturing in overcoming various qualitative and quantitative measurement challenges especially over large areas, as is needed for effective manufacturing process control
-
://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
-
thermodynamic descriptions to model diffusion processes in a variety of disordered and ordered metallic systems. The next challenge is to model the diffusion mobilities in complex materials where a Calphad-type
-
-process densification. Complementary computational model simulation capabilities are also available. [1] J. Ilavsky, F. Zhang, R.N. Andrews, I. Kuzmenko, P.R. Jemian, L.E. Levine & A.J. Allen; J. Appl
-
of integration, encompassing AM equipment, data, and simulation tools. To facilitate this, this opportunity focuses on building the necessary data and computation infrastructure to enable the Integration
-
structure-property relationships for polymers has been largely limited due to the inability to systematically control polymer sequence especially under real-world conditions where process history
-
images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
-
processes over such an extended time range is a formidable task for conventional molecular dynamics. We have developed a mathematical technique for simulation of phonon transport in nanomaterials based
-
industry, NIST recently established the NISTCHO Reference Material (RM 8675), a living cell line intended to benchmark biomanufacturing processes. However, CHO cell lines are known for their genomic