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Material Measurement Laboratory / Materials Science and Engineering Division

Thermodynamics and Kinetics Group

Projects/Programs

Displaying 1 - 23 of 23
PhaseSeparation-predictions in Mo(S,Te)2 on Bulk

Advanced Materials Design: Electronic and Functional Applications

Ongoing
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

Advanced Materials Design: Structural Applications

Ongoing
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

Atomistic Potentials and the Future of Nanomaterials Metrology

Ongoing
Atomistic simulations are increasingly being used as a tool to understand and predict properties of materials in systems, such as nanomaterials, where direct measurement is time-consuming or extremely difficult. The success of atomistic simulations depends critically on the fidelity of a specific

Atomistic tools for structure-property investigations

Ongoing
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

Characterization of 3D Photovoltaics

Completed
First Generation (thick layers of crystalline silicon) and Second Generation (thin films of CdTe, CIGS, and related materials) PV devices are both well established in the marketplace, with well defined techniques for fabrication and characterization. Third Generation PV encompasses a wide diversity
IPython Automation

Complete Automation and Distribution of Parallel Simulation Tasks

Ongoing
In computational materials science, many problems require the execution of numerous parallel simulation tasks on High Performance Computing (HPC) resources. Often a single published data point is the result of several parallel tasks executed in a specific sequence. Despite the continual improvement

Deducing Prior Material Deformation from Simple Mechanics

Ongoing
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

Developing a Materials Innovation Infrastructure

Completed
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

DFT Repositories and Informatics

Ongoing
Historically, the printed article has served as the medium de rigueur for the dissemination of scientific information. This works well when the context and results of an experiment or theory fit on a few pages; however, it is insufficient as a publication medium for many computational studies. For

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

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-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

Lead-Free Surface Finishes for Electronic Components: Tin Whisker Growth

Completed
Tin is widely used as a coating in the electronics industry because it provides excellent solderability, ductility, electrical conductivity, and corrosion resistance. Unfortunately, tin whiskers often grow spontaneously from pure tin electrodeposits and short-circuit finely pitched electrical
MGI goals

Materials Informatics

Ongoing
To develop the need data infrastructure and informatics tools, NIST is focusing on three areas: data curation, data infrastructure, and data access. Data curation efforts include efforts to develop a phase-based materials ontology to ensure that the data is represented in a semantically

Metrics for Reactive Wetting in Complex Systems

Completed
From fundamental physical considerations, we have derived a set of partial differential equations describing wetting and spreading. These equations are derived using a variational thermodynamic principle applied to a two-component alloy system with three (vapor, liquid and solid) phases. The method

Microstructure-Property Tools for Structure-Property Design

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
Hybrid_method_small

Multiscale MD-FEM Methodology

Completed
MSED, as a part of the MGI effort within NIST, is developing a multiscale modeling schema, statically coupling finite element modeling (FEM) to atomistic Molecular Dynamics (MD) 1. This methodology allows a far more realistic representation of physical phenomena than that obtained by applying each
Integrated multi-physics simulation and validation

Multiscale Modeling and Validation of Semiconductor Materials and Devices

Ongoing
The limitations of scaling traditional CMOS (complementary metal-oxide semiconductors) designs have necessitated that the semiconductor industry consider new materials and design concepts. For wide bandgap semiconductor devices, optimization of materials and fabrication processes is needed to

Scientific Workflow

Ongoing
Digitally capturing the "scientific workflow" will be a key component to modernizing scientific data management. Defined in this context, a scientific workflow is the encapsulation of all processes and accompanying relevant data necessary to reproduce and validate an experiment. Thus, a workflow

AI self-quality assurance using learning curves in feedback loops

Ongoing
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

Thermodynamic & Kinetic Data for Sustainable Energy

Completed
Knowledge of the thermodynamic, phase equilibria and diffusion properties of potential novel materials can greatly accelerate their development. However, the data needed for new, multi-component materials are often not available. The CALPHAD (Computer Coupling of Phase Diagrams and thermochemistry)
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