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This is a tale of two brothers who both found a passion for mechanical engineering research, separated to pursue their own interests in graduate school, and
From the branching pattern of leaf veins to the variety of interconnected pathways that spread the coronavirus, nature thrives on networks — grids that link the
NIST’s Material Measurement Laboratory and Communications Technology Laboratory are developing a new spectroscopy for intermolecular interactions. The team is
The Polymer Analytics project was established with the goal of accelerating the discovery of new polymer physics through the development of datasets, methods
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
Using atomistic-level calculations (density functional theory and classical molecular dynamics), this project aims at increasing our knowledge of electronic and
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been
Dylan Walsh, Weizhong Zou, Ludwig Schneider, Reid Mello, Michael Deagen, Joshua Mysona, Tzyy-Shyang Lin, Juan de Pablo, Klavs Jensen, Debra Audus, Bradley Olsen
Polymeric materials are integral components of nearly every aspect of modern life. However, developing cheminformatic solutions for polymers has been difficult
Kamal Choudhary, Kevin Garrity, Francesca Tavazza, Ghanshyam Pilania
We develop a computational database, web-apps and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures
Recent advances in first principles calculations and machine learning techniques allow a systematic search for phonon-mediated superconductors. We develop a
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
GSAS_USE addresses the effects of systematic errors in Rietveld refinements. The errors are categorized into multiplicative, additive, and peak-shape types