Modeling & computational material science

News and Updates

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

A man in brown camouflage clothing, including baseball cap, gloves and ear protection, uses a long pole to clean the inside of an HVAC unit outside a military tent.

NIST Study Identifies Chemical Blends as Possible Alternative Refrigerants

May 29, 2019

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

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

Interatomic Potential Data and Simulations

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)

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

Publications

Topological surface states of MnBi2Te4 at finite temperatures and at domain walls

February 24, 2021

Author(s)

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

On the interplay of friction and stress relaxation to improve stretch-flangeability of dual phase (DP600) steel

January 6, 2021

Author(s)

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

Resolving the Geometry/Charge Puzzle of the c(2 × 2)-Cl Cu(100) Electrode

December 27, 2020

Author(s)

Kathleen A. Schwarz, Mitchell C. Groenenboom, Thomas P. Moffat, Ravishankar Sundararaman, John T. Vinson

Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method

November 19, 2020

Author(s)

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

Modeling & computational material science

News and Updates

NIST AI System Discovers New Material

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

NIST Study Identifies Chemical Blends as Possible Alternative Refrigerants

Projects and Programs

Electric-acoustic spectroscopy for intermolecular interactions in solution

JARVIS-ML

Interatomic Potential Data and Simulations

Developing a Materials Innovation Infrastructure

Publications

Topological surface states of MnBi2Te4 at finite temperatures and at domain walls

On the interplay of friction and stress relaxation to improve stretch-flangeability of dual phase (DP600) steel

Resolving the Geometry/Charge Puzzle of the c(2 × 2)-Cl Cu(100) Electrode

Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method

Software

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

NANOP

BBEST Bayesian Estimation of Incoherent Neutron Scattering Backgrounds

RMCPROFILE