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PROJECTS/PROGRAMS

Structural Metrology of Advanced Manufacturing Processes

Ongoing
Understanding material structures in advanced manufacturing is crucial because it enables precise control over material properties, leading to improved performance, efficiency, and cost-effectiveness in production processes. However, this understanding poses many challenges, such as the
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
ML in Network Simulation

Machine Learning in Network Modeling and Simulation

Ongoing
The ability to access, manipulate, and process data has allowed network researchers to focus on many network optimization problems that were previously intractable due to complexity and scale. Solutions that make use of Machine Learning (ML) techniques are becoming increasingly popular. However, the
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

Microstructure-Property Tools for Structure-Property Design

Ongoing
Microstructure-level Structure-Property Tools OOF: Finite Element Analysis of Microstructures enables materials scientists calculate macroscopic properties from images of real or simulated microstructures. It reads an image, assigns material properties to features in the image, and conducts virtual
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

Atomistic tools for structure-property investigations

Ongoing
Interatomic Potential Repository The Interatomic Potentials Repository (IPR) provides a source for interatomic potentials (force fields), related files, and evaluation tools to help researchers obtain interatomic models and judge their quality and applicability. The files provided are of known

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

Advanced Materials Design: Structural Applications

Ongoing
Designing New High Temperature Co Superalloys In collaboration with the NIST CHiMaD center, an ICME approach in being used to develop new Co superalloys that are strengthened using an ordered FCC (L1 2) phase (similar to the related Ni-based superalloys). The design goals for these alloys include

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

Strategy for extensible, evolving terminology for MGI efforts

Ongoing
Many Indo-European languages utilize a limited set of highly reused, non-synonymous, short semantically relevant words called roots that can be combined to facilitate the building of new compounded terms such as peanut butter and watch dog. This approach, which is more prominent in certain languages

Data Models for Expression of Uncertainty in Materials Data

Ongoing
The two main parts of this work are definition of appropriate data structures for different types of uncertainty analyses, and implementation of these data structures into analysis and visualization software for demonstration, testing, and use. The different types of uncertainty analyses to be

Directed Self Assembly of Block Copolymers for Nanopatterning

Ongoing
One of the key issues for DSA is the three dimensional structure of the block copolymer. For the example of line-space patterns made using lamella-forming block copolymers, it is possible to have the DSA form tilted lamella or complex mixed morphologies that are a combination of vertical and

Stochastic Network Growth Simulation for Photopolymerization

Ongoing
We have developed a computational method to simulate the complex interactions of the stochastic processes during polymerization. Specifically, the firing rates of these stochastic events are determined based on monomer information ( e.g. functionalities, rate constants, diffusivities, interaction