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Advanced manufacturing processes create novel materials and components with superior properties and performance, enhancing the precision, efficiency, and
We engage with leaders in the pharmaceutical, dental, and personal care research sectors that rely on water soluble polymers (polyelectrolytes) to develop new
Our Atomistic Line Graph Neural Network (ALIGNN) models are designed to predict atomistic properties with high accuracy. They can successfully predict single
The Polymer Analytics project was established with the goal of accelerating the discovery of new polymer physics through the development of datasets, methods
The integration of Machine Learning (ML) in network modeling and simulations is key to evaluating ML-based solutions and algorithms used to configure and
We are developing novel machine learning algorithms and incorporating them into closed-loop autonomous systems to accelerate knowledge capture in the lab and in
We are educating the next generation workforce with MGI skills and providing tools to rapidly deploy these skills. This includes knowledge and tools for machine
We use autonomous experimentation to elucidate the role of composition, processing, and microstructure on the aqueous corrosion of complex metal alloys. To do
With increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Communities
Computational materials design requires a variety of tools to model processing-structure-property relationships across a range of time and length scales. This
NIST is working with industry and academia to improve the trustworthiness and applicability of artificial intelligence and machine learning technologies to
Develop a materials innovation infrastructure for the design and discovery of inorganic materials to reduce the time and cost for deploying new materials into
This project provides and develops novel computational infrastructure (i.e. data archives, open-source codes, APIs) that support the design, execution, and
Using methods such as electronic structure (such as density functional theory, tight-binding, quantum Monte Carlo), force-field, machine-learning and quantum
An integrated computational materials engineering (ICME) approach is employed to link experimental and computational models across a variety of time and length
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
Many photocrosslinked polymers and composites start with a mixture of low molecular mass / viscosity resins containing one or more reactive functionalities. The
Current terminology used to describe materials data is heterogeneous, redundant, and often ambiguous. The lack of common, community-based terminology hinders
Synthetic biomaterials must pass rigorous clinical, toxicological, and regulatory tests before they are introduced into practice. In order to design new
One of the major goals of the Materials Genome Initiative (MGI) is to facilitate the exchange of materials data to speed material discovery and development. The
Directed Self Assembly (DSA) is one of the leading candidates for next generation lithography for the semiconductor and data storage industries. DSA combines
Photopolymerization, particularly in crosslinked systems, proceeds via a complex reaction-diffusion process. Initiation, propagation (including those leading to