1579 lines
109 KiB
Text
1579 lines
109 KiB
Text
<!DOCTYPE html>
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<html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# ">
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<article
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about="/programs-projects/structural-metrology-advanced-manufacturing-processes" class="nist-teaser"
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<h3 class="nist-teaser__title"><a href="/programs-projects/structural-metrology-advanced-manufacturing-processes"><span>Structural Metrology of Advanced Manufacturing Processes</span>
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</a></h3>
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<div class="nist-teaser__type">
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<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
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</div>
|
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</header>
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<div class="nist-teaser__content">
|
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<div class="text-with-summary"> Understanding material structures in advanced manufacturing is crucial because it enables precise control over material properties, leading to improved performance, efficiency, and cost-effectiveness in production processes. However, this understanding poses many challenges, such as the
|
||
|
||
</div>
|
||
|
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</div>
|
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</div>
|
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</article>
|
||
</div>
|
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<div>
|
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<article
|
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about="/programs-projects/polyelectrolyte-solutions-and-gels" class="nist-teaser"
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>
|
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<div class="nist-teaser__image">
|
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<div class="entity-reference"> <a href="/programs-projects/polyelectrolyte-solutions-and-gels" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/09/20/PSGfeatured.png?itok=xFDOnx2y" width="100" height="88" alt="Phase Behavior Schematic" typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
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</div>
|
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<div class="nist-teaser__content-wrapper">
|
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<header>
|
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<h3 class="nist-teaser__title"><a href="/programs-projects/polyelectrolyte-solutions-and-gels"><span>Polyelectrolyte Solutions and Gels</span>
|
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</a></h3>
|
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<div class="nist-teaser__type">
|
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<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
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</div>
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<div class="nist-teaser__content">
|
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</div>
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</article>
|
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</div>
|
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<div>
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<article
|
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about="/programs-projects/jarvis-alignn-jarvis-alignn-ff" class="nist-teaser"
|
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>
|
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<div class="nist-teaser__image">
|
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<div class="entity-reference"> <a href="/programs-projects/jarvis-alignn-jarvis-alignn-ff" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2022/10/18/JARVIS%20Identifier.png?itok=TR_H067n" width="100" height="26" alt="NIST JARVIS" typeof="foaf:Image" />
|
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|
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|
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|
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|
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|
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<div class="nist-teaser__content-wrapper">
|
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<header>
|
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<h3 class="nist-teaser__title"><a href="/programs-projects/jarvis-alignn-jarvis-alignn-ff"><span>JARVIS-ALIGNN, JARVIS-ALIGNN-FF</span>
|
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</a></h3>
|
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<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> ALIGNN uses a line graph neural networks to include bond distances and angular information graph to incorporate finer details of atomic structure, leading to high accuracy models. While the nodes of an atomistic graph correspond to atoms and its edges correspond to bonds, the nodes of an atomistic
|
||
|
||
</div>
|
||
|
||
</div>
|
||
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|
||
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|
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|
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<div>
|
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<article
|
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about="/programs-projects/polymer-analytics" class="nist-teaser"
|
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>
|
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|
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|
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<h3 class="nist-teaser__title"><a href="/programs-projects/polymer-analytics"><span>Polymer Analytics</span>
|
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</a></h3>
|
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<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> This project focuses on a variety of activities to achieve the aforementioned goal of accelerating the discovery of new polymer physics. Polymer databases In collaboration with partners, we build FAIR (findable, accessible, interoperable, reproducible) data resources that enable machine learning
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
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<article
|
||
about="/programs-projects/data-driven-network-optimization-future-wireless-systems" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/data-driven-network-optimization-future-wireless-systems"><span>Data-Driven Network Optimization for Future Wireless Systems</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Research Highlights AI Innovations in Spectrum Sharing for Wireless Networks With the increasing demand for wireless communication, efficient spectrum management has become essential. NIST is exploring the application of AI/ML techniques to optimize this area, particularly through solutions like
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/autonomous-systems-materials-research-and-metrology-accelerating-discovery-and" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/autonomous-systems-materials-research-and-metrology-accelerating-discovery-and" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2020/09/02/autonomous-materials-metrology-diffraction-SLAC.png?itok=L5KzjK6s" width="100" height="68" alt="Autonomous materials metrology diffraction measurement performed at SLAC" typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/autonomous-systems-materials-research-and-metrology-accelerating-discovery-and"><span>Autonomous Systems for Materials Research and Metrology: Accelerating Discovery and Democratizing Science</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Materials scientists often need to search high dimensional spaces for knowledge. For example, a scientist may be interested in learning how a material property (e.g. hardness) changes over many (for example, 10) values of a synthesis or processing parameter (e.g., annealing temperature). Such an
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/autonomous-scanning-droplet-cell" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/autonomous-scanning-droplet-cell" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2023/05/09/SDC-figure.png?itok=7xKXME-i" width="100" height="31" alt="schematic of the scanning droplet cell apparatus" typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/autonomous-scanning-droplet-cell"><span>Autonomous Scanning Droplet Cell</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Corrosion impacts a broad spectrum of application areas including infrastructure, transportation, and the military. The annual price tag for corrosion mitigation and remediation is 3.4 % of the US GDP. The team is particularly interested in discovering new metallic glasses (metals without long range
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/facilitating-adoption-fair-digital-objects-materials-science-and-engineering" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/facilitating-adoption-fair-digital-objects-materials-science-and-engineering" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2020/08/17/DO_0.png?itok=Evqw6NBt" width="100" height="75" alt="A digital object is composed of Data, Metadata, Protocols & Operations, and Persistent Identifiers." typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/facilitating-adoption-fair-digital-objects-materials-science-and-engineering"><span>Facilitating the Adoption of FAIR Digital Objects in Materials Science and Engineering</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> The FAIR Data Principles define a set of aspirational guidelines for those whose goal is to improve the reusability of their data. However, the FAIR Data Principles do not define an explicit set of requirements to achieve FAIRness, which may vary within and across domains. Various projects have
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/microstructure-property-tools-structure-property-design" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/microstructure-property-tools-structure-property-design"><span>Microstructure-Property Tools for Structure-Property Design</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Microstructure-level Structure-Property Tools OOF: Finite Element Analysis of Microstructures enables materials scientists calculate macroscopic properties from images of real or simulated microstructures. It reads an image, assigns material properties to features in the image, and conducts virtual
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/trustworthy-intelligent-networks" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/trustworthy-intelligent-networks" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2020/04/05/proj-tin-graphic-300.png?itok=XQGIXbmZ" width="100" height="95" alt="Trustworthy Intelligent Networks Project" typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/trustworthy-intelligent-networks"><span>Trustworthy Intelligent Networks</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Our current research efforts include: The application of AI/ML techniques to detect abuse of the Domain Name System (DNS). The development of measurement techniques to characterize the robustness of AI/ML approaches to botnet detection. The application of AI/ML techniques to detect anomalies in the
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/developing-materials-innovation-infrastructure" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/developing-materials-innovation-infrastructure"><span>Developing a Materials Innovation Infrastructure</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--green boolean">Completed</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Phase Field Community Hub ( PFHub) and Benchmarks The Phase Field Community Hub provides a framework that supports phase field practitioners and code developers participating in an effort to improve quality assurance for phase field codes. The main thrust of this effort is the generation of a set of
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/atomistic-tools-structure-property-investigations" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/atomistic-tools-structure-property-investigations"><span>Atomistic tools for structure-property investigations</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Interatomic Potential Repository The Interatomic Potentials Repository (IPR) provides a source for interatomic potentials (force fields), related files, and evaluation tools to help researchers obtain interatomic models and judge their quality and applicability. The files provided are of known
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/advanced-materials-design-electronic-and-functional-applications" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/advanced-materials-design-electronic-and-functional-applications"><span>Advanced Materials Design: Electronic and Functional Applications</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Accelerating Materials Discovery using Machine Learning and AI Using machine learning and AI techniques along with high-throughput DFT calculations materials with specific properties are identified to accelerate the the discovery process for a variety of applications. Some of the specific materials
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/advanced-materials-design-structural-applications" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/advanced-materials-design-structural-applications"><span>Advanced Materials Design: Structural Applications</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Designing New High Temperature Co Superalloys In collaboration with the NIST CHiMaD center, an ICME approach in being used to develop new Co superalloys that are strengthened using an ordered FCC (L1 2) phase (similar to the related Ni-based superalloys). The design goals for these alloys include
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/high-performance-crystal-plasticity" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/high-performance-crystal-plasticity"><span>High Performance Crystal Plasticity</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> “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
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/deducing-prior-material-deformation-simple-mechanics" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/deducing-prior-material-deformation-simple-mechanics"><span>Deducing Prior Material Deformation from Simple Mechanics</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> 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
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/ai-self-quality-assurance-using-learning-curves-feedback-loops" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/ai-self-quality-assurance-using-learning-curves-feedback-loops"><span>AI self-quality assurance using learning curves in feedback loops</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> One application of artificial intelligence (AI) in materials is the acceleration of materials innovation, which is the mission of the Materials Genome Initiative. However, to decrease the cost and time-to-market, we must continuously assess the quality of models with new facts. AI quality assurance
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/polymer-property-predictor-and-database" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/polymer-property-predictor-and-database"><span>Polymer Property Predictor and Database</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> We aim to generate the data necessary for polymer informatics by developing information extraction pipelines to automatically extract polymer properties from the literature. We use natural language processing software (ChemDataExtractor) to extract both names of chemical entities and properties
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/aiml-data-extraction-and-uncertainty-predictions" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/aiml-data-extraction-and-uncertainty-predictions"><span>AI/ML for Data Extraction and Uncertainty Predictions</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> 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
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/teaching-liquid-state-theory-artificial-neural-network-ann" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/teaching-liquid-state-theory-artificial-neural-network-ann"><span>Teaching Liquid State Theory to an Artificial Neural Network (ANN)</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Scientific questions:
|
||
|
||
Can an ANN allow us to predict the structure of fluids that are impossible to predict numerically via liquid-state theory?
|
||
Can we learn something about liquid-state theory itself by the nature of the trained ANN?
|
||
What features do the hidden layers capture?
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/jarvis-ml" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/jarvis-ml" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2020/07/29/JARVIS-ML-7-2020.png?itok=x2WHG8z-" width="100" height="73" alt="JARVIS-ML overview " typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/jarvis-ml"><span>JARVIS-ML</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> 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
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/using-ai-determine-structure-property-relations-materials" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/using-ai-determine-structure-property-relations-materials"><span>Using AI to Determine Structure-Property Relations in Materials</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> 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
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/computation-platform-aiml" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__image">
|
||
<div class="entity-reference"> <a href="/programs-projects/computation-platform-aiml" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2019/02/08/ai-industries.png?itok=n4nc3J2i" width="100" height="100" alt="Illustration of cyberbrain surrounded by industry icons" typeof="foaf:Image" />
|
||
|
||
</a>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/computation-platform-aiml"><span>Computation Platform for AI/ML</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> In collaboration with NIST’s Information Technology Laboratory and Office of Information Systems Management, the Office of Data and Informatics is supporting the deployment and development of the long-term operational model for the enki computation platform for NIST staff members who research and
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/multiparametric-analysis-network-growth-process-combined-experimental-and" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/multiparametric-analysis-network-growth-process-combined-experimental-and"><span>Multiparametric Analysis of Network Growth Process via Combined Experimental and Computational Approaches</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> We have developed techniques that use simultaneous measurements of evolution of multiple polymerization properties combined with computational approaches. In-situ measurements including stress evolution, chemical evolution, and temperature changes during polymerization and computation approaches can
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/strategy-extensible-evolving-terminology-mgi-efforts" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/strategy-extensible-evolving-terminology-mgi-efforts"><span>Strategy for extensible, evolving terminology for MGI efforts</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Many Indo-European languages utilize a limited set of highly reused, non-synonymous, short semantically relevant words called roots that can be combined to facilitate the building of new compounded terms such as peanut butter and watch dog. This approach, which is more prominent in certain languages
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/fundamentals-macromolecular-self-assembly-particles-gels-and-polyelectrolyte" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/fundamentals-macromolecular-self-assembly-particles-gels-and-polyelectrolyte"><span>Fundamentals of Macromolecular Self Assembly: particles, gels, and polyelectrolyte complex coacervates</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-badge display-inline-block margin-y-1 nist-badge--blue boolean">Ongoing</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> This project considers the hierarchical structure formed in aqueous solution from rationally‑designed block polymer sequence and chemistry. Regular nanoscale structures spontaneously form in aqueous solution by tailoring the antagonistic interactions among the solution and copolymer moieties. These
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/programs-projects/data-models-expression-uncertainty-materials-data" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/programs-projects/data-models-expression-uncertainty-materials-data"><span>Data Models for Expression of Uncertainty in Materials Data</span>
|
||
</a></h3>
|
||
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<div class="text-with-summary"> The two main parts of this work are
|
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|
||
definition of appropriate data structures for different types of uncertainty analyses, and
|
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implementation of these data structures into analysis and visualization software for demonstration, testing, and use.
|
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The different types of uncertainty analyses to be
|
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<h3 class="nist-teaser__title"><a href="/programs-projects/directed-self-assembly-block-copolymers-nanopatterning"><span>Directed Self Assembly of Block Copolymers for Nanopatterning</span>
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<div class="nist-teaser__content">
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<div class="text-with-summary"> One of the key issues for DSA is the three dimensional structure of the block copolymer. For the example of line-space patterns made using lamella-forming block copolymers, it is possible to have the DSA form tilted lamella or complex mixed morphologies that are a combination of vertical and
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<div class="text-with-summary"> We have developed a computational method to simulate the complex interactions of the stochastic processes during polymerization. Specifically, the firing rates of these stochastic events are determined based on monomer information ( e.g. functionalities, rate constants, diffusivities, interaction
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<div class="text-with-summary"> WebFF is an open and extensible force-field (FF) repository, designed to support the Materials Genome Initiative (MGI) for organic and related soft materials. The repository is built using the NIST Materials Data Curation System (MDCS) which supports ontology based database descriptions using XML
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