Projects/Programs

Displaying 1 - 25 of 37

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

Small array of transistors designed for single device integration.

Integrated CMOS Testbeds for Nanoelectronics and Machine Learning

With the rapid rise of artificial intelligence and its insatiable demand for low-energy, high performance computing, research is accelerating into novel

Neuromorphic Device Measurements

Neuromorphic computing is a radical new approach to information processing for artificial intelligence where, instead of using digital electronics, inspiration

Fig. 1. Die containing 285 superparamagnetic tunnel junctions.

Spintronics for Neuromorphic Computing

One of the most promising new approaches to next generation information processing is spintronics, where information is carried with electronic spin rather than

Fig. 1. Time re-Envisioned: Not a biproduct of computation but the domain in which information is encoded. Leads to different mathematical functions becoming energy efficient primitives.

Temporal Computing

The human brain does some types of information processing, like speech recognition, image recognition, or video processing, much more efficiently than can be

Genome in a Bottle

Consortium hosted by NIST and JIMB dedicated to authoritative characterization of benchmark human genomes. Sign up for General GIAB and Analysis Team email

NVIDIA® DGX™ Station, an AI Supercomputer

3.5 GHz Spectrum Sharing

Application of deep learning algorithms to 3.5 GHz spectrograms to characterize incumbent federal radar emissions.

AI-based measurements For Imaging

NIST streamlines deriving measurements from terabyte-sized images and improves image-based measurement accuracy via web-based applications addressing image

Artificial Intelligence for Chemical Manufacturing

NIST uses AI for high-throughput data evaluation, which is critical to the chemical manufacturing enterprise.

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) Microscopy

Broadband coherent anti-Stokes scattering (BCARS) microscopy is an imaging modality that probes the chemical content and abundance of samples without the

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

Classification Methods for the NIST Biorepository

We aim to identify chemometric fingerprints using artificial intelligence methods that reflect the temporal and geographic history of seabirds, and by extension

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.

Consistency Analysis and Uncertainty in ‘omic Data

As ‘omics moves into industrial and commercial applications, there is a need for quality and process control to ensure safety and efficacy of products and

Data Driven Decision Support for Additive Manufacturing

“Digital disruption is a change in industry triggered by advances in information and communications technology. During the next few years, the technologies

AI/ML for Data Extraction and Uncertainty Predictions

Deducing Prior Material Deformation from Simple Mechanics

Deep Learning for MRI Reconstruction and Analysis

Healthcare is big part of the national economy. Medical Imaging has emerged over the past several decades as a critical diagnosis and therapeutic tool. However

Embodied AI and Data Generation for Manufacturing Robotics

Approaches categorized as “artificial intelligence” (AI) are enabling significant advances in robotics. These include symbolic logic, Bayesian statistics, and

High Performance Crystal Plasticity

High-Throughput Experimental Materials Collaboratory

Materials are technology enablers. Many technologies (e.g., solar fuels, gas steam/turbines, catalysis, additive manufacturing, and corrosion mitigation) await

Identification, Suitability Testing, and Property Determination from Untargeted Measurements

Many polymeric fluids, including biotherapeutics and fuels, can have unusual physical behavior in solutions, and many applications require detailed knowledge of

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

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

Machine Learning Fluid Equations of State

By using machine learning approaches, we seek to elucidate underlying universal characteristics of fluids and fluid mixtures that enable property prediction

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