Machine Learning / A.I.

JARVIS-ALIGNN, JARVIS-ALIGNN-FF

Our Atomistic Line Graph Neural Network (ALIGNN) models are designed to predict atomistic properties with high accuracy. They can successfully predict single

Polymer Analytics

The Polymer Analytics project was established with the goal of accelerating the discovery of new polymer physics through the development of datasets, methods

Machine Learning in Network Modeling and Simulation

The integration of Machine Learning (ML) in network modeling and simulations is key to evaluating ML-based solutions and algorithms used to configure and

$Autonomous materials metrology diffraction measurement performed at SLAC$

Autonomous Systems for Materials Research and Metrology: Accelerating Discovery and Democratizing Science

We are developing novel machine learning algorithms and incorporating them into closed-loop autonomous systems to accelerate knowledge capture in the lab and in

Machine Learning: Educating the Next Generation Materials Workforce

We are educating the next generation workforce with MGI skills and providing tools to rapidly deploy these skills. This includes knowledge and tools for machine

Autonomous Scanning Droplet Cell

We use autonomous experimentation to elucidate the role of composition, processing, and microstructure on the aqueous corrosion of complex metal alloys. To do

Trustworthy Intelligent Networks

NIST is working with industry and academia to improve the trustworthiness and applicability of artificial intelligence and machine learning technologies to

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

Advanced Materials Design: Electronic and Functional Applications

Using methods such as electronic structure (such as density functional theory, tight-binding, quantum Monte Carlo), force-field, machine-learning and quantum

High Performance Crystal Plasticity

Deducing Prior Material Deformation from Simple Mechanics

AI self-quality assurance using learning curves in feedback loops

Polymer Property Predictor and Database

AI/ML for Data Extraction and Uncertainty Predictions

Teaching Liquid State Theory to an Artificial Neural Network (ANN)

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

Using AI to Determine Structure-Property Relations in Materials

Illustration of cyberbrain surrounded by industry icons

Computation Platform for AI/ML

The enki system provides access to research on and use of artificial intelligence and machine learning.

Materials Data Curation System

Semi-Automatic Curation

JARVIS-FF

JARVIS-FF (Website: https://www.ctcms.nist.gov/~knc6/periodic.html ) is a repository of classical force-field/potential calculation data to help users to select

JARVIS-DFT

JARVIS-DFT (Website: https://www.ctcms.nist.gov/~knc6/JVASP.html ) is a materials property repository focused on density functional theory (DFT) predictions of

CHiMaD Phase Field Community

CHiMaD Phase Field Website Repository See the live website at pages.nist.gov/chimad-phase-field. In January 2015 a group of phase field

Machine Learning for Materials Research: Bootcamps and Workshops

Over the last several years machine learning has gained increased attention in the material science community. The ability to perform high throughput in silico