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Information Technology

Artificial intelligence

Projects/Programs

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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

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

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

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

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

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

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

Neuromorphic Device Measurements

Ongoing

One type of device that is emerging as an attractive artificial synapse is the resistive switch, or memristor. These devices, which usually consist of a thin layer of oxide between two electrodes, have conductivity that depends on their history of applied voltage, and thus have highly nonlinear

NIST Automated Vehicles Program

Ongoing

Measurement science and standards are needed to support the safe and predictable operation of future automated vehicles (AVs), which have great potential to significantly impact our daily lives and improve the competitiveness of our economy. A FY22 NIST Strategic and Emerging Research Initiatives

NIST Living Measurement Systems Foundry

Ongoing

To enable the production of high-throughput, high-quality data that meets the rapidly evolving needs of current and future stakeholders, NIST has established an automation facility for the growth, manipulation, sample preparation, and measurement of engineered microbes. The core of the facility

Physics and Hardware for Intelligence

Ongoing

Our work in this area can be separated into two categories: conceptual and experimental. Please read our publications linked below for more information. Experimental: Our latest generation of synaptic circuits are described in a 2024 paper published in APL Machine Learning. These circuits are our

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