Modeling & computational material science

News and Updates

Race Logic: Novel Circuitry Solves a Myriad of Computationally Intensive Problems With a Minimum of Energy

May 11, 2021

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

An illustration for AI shows a silhouette of a human head containing icons for computer networks.

NIST AI System Discovers New Material

November 24, 2020

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

Divide and Conquer: New Algorithm Examines Crime-Scene Bullets Segment by Segment

March 26, 2020

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

Projects and Programs

Electric-acoustic spectroscopy for intermolecular interactions in solution

NIST’s Material Measurement Laboratory and Communications Technology Laboratory are developing a new spectroscopy for intermolecular interactions. The team is

JARVIS-ML

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

Developing a Materials Innovation Infrastructure

Develop a materials innovation infrastructure for the design and discovery of inorganic materials to reduce the time and cost for deploying new materials into

Integrated Density Functional Theory (DFT) Data and Tools

Using atomistic-level calculations (density functional theory and classical molecular dynamics), this project aims at increasing our knowledge of electronic and

Publications

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-Ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

June 6, 2021

Author(s)

Robert T. Bell, Peter A. Beaucage, Marc J. Murphy, Aine Connolly, David Ginley, Ulrich Wiesner, R. B. Van Dover, Michael O. Thompson

Most material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been

High-throughput bend-strengths of ultra-small polysilicon MEMS components

May 17, 2021

Author(s)

Robert Cook, Brad Boyce, Lawrence Friedman, Frank DelRio

The strength distribution of polysilicon bend specimens, approximately 10 um in size, is measured using a high-throughput microelectromechanical system

The impact of alloying on defect-free nanoparticles exhibiting softer but tougher behavior

May 4, 2021

Author(s)

Anuj Bisht, Raj Kiran Koju, Yuanshen Qi, James Hickman, Yuri Mishin, Eugen Rabkin

Coarsening of Solid Particles in Liquid Pb-Sn Alloys: Reinterpretation of Data in Light of the TIDC Theory of Coarsening

April 1, 2021

Author(s)

James Hickman, Yuri Mishin, Vidvuds Ozolins, Alan Ardell

Modeling & computational material science

News and Updates

Race Logic: Novel Circuitry Solves a Myriad of Computationally Intensive Problems With a Minimum of Energy

NIST AI System Discovers New Material

Divide and Conquer: New Algorithm Examines Crime-Scene Bullets Segment by Segment

Projects and Programs

Electric-acoustic spectroscopy for intermolecular interactions in solution

JARVIS-ML

Developing a Materials Innovation Infrastructure

Integrated Density Functional Theory (DFT) Data and Tools

Publications

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-Ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

High-throughput bend-strengths of ultra-small polysilicon MEMS components

The impact of alloying on defect-free nanoparticles exhibiting softer but tougher behavior

Coarsening of Solid Particles in Liquid Pb-Sn Alloys: Reinterpretation of Data in Light of the TIDC Theory of Coarsening

Software

NANOP

BBEST Bayesian Estimation of Incoherent Neutron Scattering Backgrounds

RMCPROFILE

GSAS_USE (Bayesian Statistics Approach to Accounting for Unknown Systematic Errors)