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Artificial intelligence

Projects/Programs

Keyword

Organizational Unit

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...

AI for Live Mouse Cancer Progression Detection

Ongoing

Overview NIST has partnered with the National Cancer Institute (NCI) at the National Institutes of Health (NIH) to study cellular characteristics and cellular movements in live mice that indicate the presence of tumors. NIST researchers have developed neural networks designed to automatically detect...

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...

Measurement Science for Automated Vehicles

Ongoing

Objective Develop measurement science for evaluating AI-based decision-making systems in automated vehicles. Technical Idea This project produces measurement methods, reference baselines, test tools, and scenario datasets that solve the measurement gap described above. Current evaluation methods...

Measurement Science for Robotics and Autonomous Systems Program

Ongoing

Objective Develop and deploy measurement science that advances robotic system performance, collaboration, agility, autonomy, safety, and ease of implementation to enhance U.S. innovation and industrial competitiveness. Technical Idea The fundamental idea is to provide the measurement science needed...

Measurements and Standards for AI-Ready Biological Data: Unlocking Protein Function

Ongoing

To address this need, the NIST Cellular Engineering Group is collaborating with the Align Foundation and several other laboratories to develop a framework of measurements and standards for AI-ready data. This framework is based on four principles designed to maximize the impact of data collection...

Microstructure-Property Tools for Structure-Property Design

Ongoing

Materials Digital Twins for AM Current methodologies for designing digital twins in additive manufacturing (AM) exhibit two main approaches: one involves AI models. that establish a direct correlation between input and output properties, overlooking materials structure and characterization, while...

Nanoparticle Diameter Estimation Using AI-based Models from Event Camera-based Optical Imaging

Ongoing

Overview 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...

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 LabCAS: Data Driven Science Architecture and Resource

Ongoing

Biological data lie at the heart of innovation and artificial intelligence (AI) advances in the emerging biotechnology sector and serve as key strategic resources for achieving major breakthroughs in biomanufacturing, as summarized by the National Security Commission on Emerging Biotechnology's...

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...

Overlay Metrology Using Physics and AI-Based Scanning Electron Microscopy

Ongoing

Overview The project aims to build a complete, physics-backed metrology pipeline for deployment in commercial semiconductor chip fabrication facilities and research labs to optimize scanning electron microscopy (SEM)-based overlay, critical dimensions (CD), and defect inspection. Beyond production...

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