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Artificial intelligence

Projects/Programs

Keyword

Organizational Unit

Data Driven Decision Support for Additive Manufacturing

Completed

Objective To develop and deploy the metrics, models, and best practices for using product definition, advanced analytics, and machine learning methods in additive manufacturing design and process planning to reduce lead times and support first-part-correct goals. Technical Idea Future advancements...

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...

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...

Deep Learning for MRI Reconstruction and Analysis

Ongoing

The project is proceeding in three directions. Creating a new MRI reference artifact designed to assess geometric distortion using NIST’s MRI scanner. The artifact will be small enough to fit within the scanner with sufficient clearance to allow for variation in positioning within the scanner....

an infographic showing four proposed standards connecting as four joined puzzle pieces

Development of Standards to Support a Modular and Autonomous Laboratory Ecosystem

Ongoing

Autonomous experimentation (AE), or self-driving laboratories, combines AI and automation while leveraging human intuition and creativity to guide experiment campaigns. AE can reduce the time and resources needed to discover new materials by orders of magnitude and reduce the time-to-market for...

Edge AI

Ongoing

This project: Develops edge learning and edge-enabled collaborative learning algorithms. Develops measurement methods for testing the performance and robustness of edge-learned, and more generally collaboratively learned, AI/ML models. Collaborates with other projects (e.g., 5G/6G Core Network...

Emerging Hardware for Artificial Intelligence

Ongoing

Here is a brief description of our work with links to recent papers from our investigations, broadly classified as experimental and modeling. A brief overview of Josephson junction-based bio-inspired computing can be found in our review article. Experimental We have facilities to develop our devices...

Explainable Artificial Intelligence Based Modeling Applied to OMICS Problems

Ongoing

The goal of the project is to produce billions of certified values from multiple measurement methods with well-characterized uncertainty in the “’omics” field (genomics, proteomics, transcriptomics). These values are prepared by a joint effort between human experts and trained AI based models for...

viral vector illustration with RNA seen within the capsid

Gene Delivery Particle Analysis with High-Throughput Light Scattering Microscopy

Ongoing

This project aims to develop light-scattering microscopy methods for rapid, multi-attribute characterization of gene therapy particles at the single particle level. Emerging gene therapy treatments use gene delivery particles, such as viral vectors and non-viral carriers, to cure intractable...

Generative Artificial Intelligence Evaluation Program (GenAI)

Ongoing

Please visit the GenAI evaluation server page for an overview of the GenAI program and descriptions of specific tasks, schedules, and evaluation plans, as well as the registration, download, and hosts submission platform for participants. Direct links to specific GenAI challenges are provided below...

Genome in a Bottle

Ongoing

Consortium goals: The Genome in a Bottle Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice and innovations...

Hardware Accelerators for Neural Networks

Ongoing

One promising candidate for building a hardware accelerator comes from the field of spintronics, where information is carried by electronic spin rather than charge. Magnetic tunnel junctions are particularly suited because of their multifunctionality and compatibility with standard integrated...

Hermes: Modular Open-Source Tools for Autonomous Materials Science Experimentation

Ongoing

Autonomous Experimentation is one of the latest, most powerful paradigms of research that takes advantage of Machine Learning tools. However, most machine learning code bases are designed for generic data. Contrastingly, in Materials Science, and other sciences, the data has significant physical...

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...

Identification, Suitability Testing, and Property Determination from Untargeted Measurements

Ongoing

In many applications, detailed knowledge of a fluid mixture’s physical behavior is needed. This can include physical properties such as viscosity and qualitative properties such as solubility. For instance, a dissolved polymer may suddenly precipitate out of solution when exposed to saline, and if...

Indoor Localization and Tracking

Completed

Please see the page that describes NIST work in this area.

Actionable Intelligence and Industrial AI

Industrial Artificial Intelligence Management and Metrology (IAIMM)

Ongoing

IAIMM has identified IAI systems for decision making, planning, and control in manufacturing as a prime candidate for better Standard Operating Procedures (SOPs) centered on both use and evaluation. The specific use case of multi-stage manufacturing presents a broad scope of application to...

Small array of transistors designed for single device integration.

Integrated CMOS Testbeds for Nanoelectronics and Machine Learning

Ongoing

The increasingly complex device requirements for next-generation computing architectures such as neuromorphic computing or nanoelectronic machine learning accelerators present challenges for researchers across the spectrum of institutions, from small businesses and universities to government...

Integrated structural biology for predicting and quantifying nucleic acid structure and dynamics

Ongoing

Comparative assessment and improvement of atomic potentials. NMR is a primary structure determination method for short RNAs (100 nucleotides or less). Many types of NMR experiments can be performed, yielding data with varying levels of precision in defining structure: nuclear Overhauser effect...

GadInk filled neutron transmission grating

Interferometry: INFER - Neutron Interferometric Microscopy of Small Forces and Hierarchical Structures

Ongoing

Multi-scale Images and Hierarchical Structures One of the keys identified by the 2015 DOE report, “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science” are methods that can study hierarchical systems. Such systems include the propagation of...

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...

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-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...

LANTERN - Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power

Ongoing

LANTERN: genotype-phenotype LANdscape in TERpretable Non-parametric model The bioeconomy is increasingly data-driven, relying on large-scale measurements to rapidly engineer novel functions. To facilitate rational engineering, where complex designs are constructed from well understood components...

Figure 1. Document and linguistic relations extracted by QuerCUS from NIST cybersecurity publications

Linguistic and AI Support for Collective Standards Development

Ongoing

Standards are increasingly part of complex digital ecosystems which may span dozens of documents, several different domains of science and engineering, and hundreds of contributors from different countries and backgrounds. It can be challenging to ensure that standards are consistent, meet...

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