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When the words “artificial intelligence” (AI) come to mind, your first thoughts may be of super-smart computers, or robots that perform tasks without needing
On the morning of March 22, 1915, residents of the small town of West Shelby, New York, awoke to a horrific scene. A woman clad only in a bloodied nightgown lay
More than a dozen chemical blends could serve as alternative refrigerants that won’t heat the atmosphere as much as today’s refrigerants do, or catch fire
NIST’s Material Measurement Laboratory and Communications Technology Laboratory are developing a new spectroscopy for intermolecular interactions. The team is
JARVIS-ML is a repository of machine learning (ML) model parameters, descriptors, and ML related input and target data. JARVIS-ML is a part of the NIST-JARVIS
This project provides resources to address some of the challenges to the wider use of classical atomistic simulations (e.g. molecular dynamics and Monte-Carlo)
Develop a materials innovation infrastructure for the design and discovery of inorganic materials to reduce the time and cost for deploying new materials into
Kevin F. Garrity, Sugata Chowdhury, Francesca M. Tavazza
MnBi2Te4 has recently been the subject of intensive study, due to the prediction of axion insulator, Weyl semimetal, and quantum anomalous Hall insulator phases
Dilip K. Banerjee, Kali Prasad, Krishnaswamy Hariharan, Uday Chakkingal
Industrial servo press has successfully demonstrated improved formability when deforming sheet metals. While the time dependent viscous behaviour of material is
Ganga Purja Pun, Vesselin Yamakov, James Hickman, Edward Glaessgen, Yuri Mishin
Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network
GSAS_USE addresses the effects of systematic errors in Rietveld refinements. The errors are categorized into multiplicative, additive, and peak-shape types
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to
We implemented a Bayesian-statistics approach for subtraction of incoherent scattering from neutron total-scattering data. In this approach, the estimated