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

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

A Review of Models for Heat Transfer in Steel and Concrete Members During Fire

October 22, 2021

Author(s)

Dilip K. Banerjee

Structural design for fire is conceptually similar to structural design conducted under ambient temperature conditions. Such design requires an establishment of

The effect of internal defects on tensile tests of additively manufactured metal samples: In-situ tensile testing during X-ray computed tomography

October 5, 2021

Author(s)

Felix Kim, Shawn P. Moylan, Thien Q. Phan, Edward Garboczi

Tensile mechanical tests were carried out simultaneously with high-resolution X-ray computed tomography (XCT) measurements on 17-4 stainless steel tensile dog

Nanopores in Atomically Thin 2D Nanosheets Limit Aqueous Single-Stranded DNA Transport

September 24, 2021

Author(s)

Alexander Smolyanitsky, Binquan Luan

Nanopores in 2D materials are highly desirable for DNA sequencing, yet achieving single-stranded DNA (ssDNA) transport through them is challenging. Using

Recommendation system to predict missing adsorption properties of nanoporous materials

September 7, 2021

Author(s)

Arni Sturluson, Ali Raza, Grant McConachie, Daniel Siderius, Xaioli Fern, Cory Simon

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores and thus could be used to store, capture, and sense many different gases

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

Interatomic Potential Data and Simulations

Developing a Materials Innovation Infrastructure

Publications

A Review of Models for Heat Transfer in Steel and Concrete Members During Fire

The effect of internal defects on tensile tests of additively manufactured metal samples: In-situ tensile testing during X-ray computed tomography

Nanopores in Atomically Thin 2D Nanosheets Limit Aqueous Single-Stranded DNA Transport

Recommendation system to predict missing adsorption properties of nanoporous materials

Software

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

NANOP

BBEST Bayesian Estimation of Incoherent Neutron Scattering Backgrounds

RMCPROFILE