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Artificial intelligence

Projects/Programs

Displaying 26 - 50 of 72

Embodied AI and Data Generation for Robotics

Ongoing
Objective To facilitate the adoption of AI-based robotic approaches in practical manufacturing scenarios by creating test methods that target AI-enabled robotic systems, evaluating the performance of AI-enabled robotic systems, and creating manufacturing-relevant and AI-centric datasets. Technical
circuit

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
GIAB Logo

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
Microscope image of a crossbar array

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

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
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

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 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
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
A group of students in a laboratory with computers and equipment

A Low-Cost Robot Science Kit for Education

Ongoing
Despite its low cost, Legolas has been demonstrated for machine learning-driven hypothesis design, discovery, and validation. For the last four years, Legolas has been used in hands-on courses at the University of Maryland to teach next-generation workforce skills, including ML, control systems
MLF_EquationofState1

Machine Learning Fluid Equations of State

Ongoing
Understanding the thermodynamic properties of fluids and fluid mixtures is of central importance in many fields of science and engineering ranging from medicine to consumer products. The nature of the particles in a fluid can vary greatly depending on the type of interactions present, e.g., dipole

Machine Learning for Internet of Things (IoT)

Ongoing
Resource Allocation in IIoT Systems As with any large an complex system, Industrial Internet of Things (IIoT) deployments require the system operator to efficiently allocate the available bandwidth, computing, and energy resources. This is challenging because IIoT systems, especially large ones, can

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
Illustration with logo style printing: “organic ingredients”, “100% natural”, “100% organic”; “natural ingredients”, and “authentic” centered in bold letters.

Machine Learning to Predict Food Provenance

Ongoing
Adulteration of food and food products is a pernicious problem which is difficult to solve as supply chains and international trade routes become increasingly complex; yet agriculture contributed over $1 trillion to the US GDP in 2017, [1] illustrating the importance of protecting this and related
Multi_Colloidal_Crystals2

Machine Learning to Predict Multicomponent Colloidal Crystals

Ongoing
There is a direct link between a material’s macroscopic properties and its microscopic structure, which makes rational bottom-up self-assembly a powerful tool for engineering properties of materials. In general, colloids are facile material building blocks whose shape, charge, and surface

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
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