Materials Genome Initiative (MGI)

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

A digital object is composed of Data, Metadata, Protocols & Operations, and Persistent Identifiers.

Facilitating the Adoption of the FAIR Digital Object Framework in Material Science

With increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Communities

Microstructure-Property Tools for Structure-Property Design

Computational materials design requires a variety of tools to model processing-structure-property relationships across a range of time and length scales. This

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

Atomistic tools for structure-property investigations

This project provides and develops novel computational infrastructure (i.e. data archives, open-source codes, APIs) that support the design, execution, and

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

Advanced Materials Design: Structural Applications

An integrated computational materials engineering (ICME) approach is employed to link experimental and computational models across a variety of time and length

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.

Multiparametric Analysis of Network Growth Process via Combined Experimental and Computational Approaches

Many photocrosslinked polymers and composites start with a mixture of low molecular mass / viscosity resins containing one or more reactive functionalities. The

Strategy for extensible, evolving terminology for MGI efforts

Current terminology used to describe materials data is heterogeneous, redundant, and often ambiguous. The lack of common, community-based terminology hinders

Fundamentals of Macromolecular Self Assembly: particles, gels, and polyelectrolyte complex coacervates

Synthetic biomaterials must pass rigorous clinical, toxicological, and regulatory tests before they are introduced into practice. In order to design new

Data Models for Expression of Uncertainty in Materials Data

One of the major goals of the Materials Genome Initiative (MGI) is to facilitate the exchange of materials data to speed material discovery and development. The

Directed Self Assembly of Block Copolymers for Nanopatterning

Directed Self Assembly (DSA) is one of the leading candidates for next generation lithography for the semiconductor and data storage industries. DSA combines

Stochastic Network Growth Simulation for Photopolymerization

Photopolymerization, particularly in crosslinked systems, proceeds via a complex reaction-diffusion process. Initiation, propagation (including those leading to

WebFF: Force-field repository for organic and soft materials

The vision of the Materials Genome Initiative (MGI) is a new R&D paradigm enabling accelerated materials development at lower cost via an increased reliance on

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