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PROJECTS/PROGRAMS

Machine Learning / A.I.

NIST JARVIS

JARVIS-ALIGNN, JARVIS-ALIGNN-FF

Ongoing
ALIGNN uses a line graph neural networks to include bond distances and angular information graph to incorporate finer details of atomic structure, leading to high accuracy models. While the nodes of an atomistic graph correspond to atoms and its edges correspond to bonds, the nodes of an atomistic

Polymer Analytics

Ongoing
This project focuses on a variety of activities to achieve the aforementioned goal of accelerating the discovery of new polymer physics. Polymer databases In collaboration with partners, we build FAIR (findable, accessible, interoperable, reproducible) data resources that enable machine learning

Data-Driven Network Optimization for Future Wireless Systems

Ongoing
Research Highlights AI Innovations in Spectrum Sharing for Wireless Networks With the increasing demand for wireless communication, efficient spectrum management has become essential. NIST is exploring the application of AI/ML techniques to optimize this area, particularly through solutions like
A photo of attendees attending MLMR Bootcamp 2018.

Machine Learning: Educating the Next Generation Materials Workforce

Ongoing
Annual Bootcamp: Machine Learning for Materials Research (MLMR) The fifth annual MLMR met in the summer of 2020 with 180 attendees from 12 countries, 30% of whom were from industry. Over the 5 years of the bootcamp, we have had attendees from a total of 19 countries. We also run tutorials at MRS
schematic of the scanning droplet cell apparatus

Autonomous Scanning Droplet Cell

Ongoing
Corrosion impacts a broad spectrum of application areas including infrastructure, transportation, and the military. The annual price tag for corrosion mitigation and remediation is 3.4 % of the US GDP. The team is particularly interested in discovering new metallic glasses (metals without long range
Trustworthy Intelligent Networks Project

Trustworthy Intelligent Networks

Ongoing
Our current research efforts include: The application of AI/ML techniques to detect abuse of the Domain Name System (DNS). The development of measurement techniques to characterize the robustness of AI/ML approaches to botnet detection. The application of AI/ML techniques to detect anomalies in the

Developing a Materials Innovation Infrastructure

Ongoing
Phase Field Community Hub ( PFHub) and Benchmarks The Phase Field Community Hub provides a framework that supports phase field practitioners and code developers participating in an effort to improve quality assurance for phase field codes. The main thrust of this effort is the generation of a set of

Advanced Materials Design: Electronic and Functional Applications

Ongoing
Accelerating Materials Discovery using Machine Learning and AI Using machine learning and AI techniques along with high-throughput DFT calculations materials with specific properties are identified to accelerate the the discovery process for a variety of applications. Some of the specific materials

High Performance Crystal Plasticity

Ongoing
“Crystal plasticity” is a computationally intensive way of computing the behavior of materials undergoing large permanent deformations. Computation is very inhomogeneous: A large effort is expended everywhere, but only a small portion of the computational domain is doing anything interesting. We

Deducing Prior Material Deformation from Simple Mechanics

Ongoing
Process-structure-property linkages suggest an opportunity to deduce processing from behavior. Simple imaging experiments provide rich sources of data. Can we deduce prior deformation? Scheme: Thin film plasticity Deform to some reference strain Unload Deform to test strain, image Deduce

AI self-quality assurance using learning curves in feedback loops

Ongoing
One application of artificial intelligence (AI) in materials is the acceleration of materials innovation, which is the mission of the Materials Genome Initiative. However, to decrease the cost and time-to-market, we must continuously assess the quality of models with new facts. AI quality assurance

Polymer Property Predictor and Database

Ongoing
We aim to generate the data necessary for polymer informatics by developing information extraction pipelines to automatically extract polymer properties from the literature. We use natural language processing software (ChemDataExtractor) to extract both names of chemical entities and properties

AI/ML for Data Extraction and Uncertainty Predictions

Ongoing
The Material Measurement Lab at NIST employs artificial intelligence for the prediction and discovery of materials characteristics. Our applications of artificial intelligence (AI) accelerate materials research as well as help the community learn about AI's capabilities and gain confidence in

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

Ongoing
Scientific questions: Can an ANN allow us to predict the structure of fluids that are impossible to predict numerically via liquid-state theory? Can we learn something about liquid-state theory itself by the nature of the trained ANN? What features do the hidden layers capture?
JARVIS-ML overview

JARVIS-ML

Ongoing
JARVIS-ML introduced Classical Force-field Inspired Descriptors (CFID) as a universal framework to represent a material’s chemistry-structure-charge related data. With the help of CFID and JARVIS-DFT data, several high-accuracy classifications and regression ML models were developed, with

Using AI to Determine Structure-Property Relations in Materials

Ongoing
The Material Measurement Lab at NIST employs artificial intelligence for the prediction and discovery of materials characteristics. Our applications of artificial intelligence (AI) accelerate materials research as well as help the community learn about AI's capabilities and gain confidence in
Illustration of cyberbrain surrounded by industry icons

Computation Platform for AI/ML

Ongoing
In collaboration with NIST’s Information Technology Laboratory and Office of Information Systems Management, the Office of Data and Informatics is supporting the deployment and development of the long-term operational model for the enki computation platform for NIST staff members who research and

Materials Data Curation System

Ongoing
The NIST Materials Data Curation System (MDCS) provides a means for capturing, sharing, and transforming materials data into a structured format that is XML based amenable to transformation to other formats. The data are organized using user-selected templates encoded in XML Schema. These templates

Semi-Automatic Curation

Ongoing
Scientific literature is undeniably an important source of scientific data for research but the review and curation of data from literature is both tedious and time consuming. Investigators must sort through many articles to review and extract relevant information. For many areas of research

JARVIS-FF

Ongoing
Many classical force-fields are developed for a particular set of properties (such as energies) and they may not have been tested for properties or configurations outside the training (such as elastic constants, defect formation energies or energies for metastable phases). JARVIS-FF provides an

JARVIS-DFT

Ongoing
JARVIS-DFT hosts materials property data for ~40000 bulk and ~1000 low-dimensional crystalline materials and the database is continuously expanding. Some of the properties in the database are: formation energies, bandgaps, elastic, piezoelectric, dielectric constants, and magnetic moments

Machine Learning for Materials Research: Bootcamps and Workshops

Ongoing
The 2016 bootcamp consisted of three days of lectures covering data processing, supervised learning and unsupervised learning as well as hands-on exercises using MATLAB covering a range of data analysis topics touching on each of the lecture . Example topics include: Identifying important