Thermodynamics and Kinetics Group

Microstructure Tools

• PyMKS  A python-based framework of the Materials Knowledge System (MKS) is a data science approach for solving multiscale materials science problems using  physics, machine learning, regression analysis, signal processing, and spatial statistics to create processing-structure-property relationship.
• 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 experiments to determine the macroscopic properties of the microstructure.

Phase-based Modeling tools

• Open Calphad: This is a multicomponent thermodynamic open-source software for performing a variety multicomponent multiphase calculations.

Atomistic Simulation and Potential Develop Tools

• PINN is a novel method combining physics-based knowledge and neural networks to determine descriptions of the bonding forces between atoms.
• pyfit-FF: This is a python package for training artificial neural network interatomic potentials (including PINN potentials)
• AtomMan   The Atomistic Manipulation Toolkit is a Python library for creating, representing, manipulating, and analyzing large-scale atomic systems of atom.
• IPRPy   This is a  collection of tools and resources supporting the design of scientific calculations for evaluating basic materials properties across multiple classical interatomic potentials.

High-Throughput DFT Tools

• JARVIS-Tools: This is a package of scripts used in generating and analyzing the datasets from JARVIS-DFT and JARVIS-FF data repositories. 

• JARVIS-Heterostructure: This is a set of tools for 2D materials in the JARVIS-DFT database. Some of the properties available are: work function, band-alignment, and heterostructure classification.

Infrastructure and Workflow Tools

• CoRR: Cloud of Reproducible Records is a  web platform for storing and viewing metadata and data associated with simulation records for reproducibility and beyond.
• PFHuB: Phase Field Hub is a web platform for facilitating collaboration within the phase-field community.
• JARVIS-ML: Machine learning prediction tools, trained on JARVIS-DFT data.
• Materials Design Toolkit: A python-based framework that provides the generic environment for materials design, particularly for integrating CALPHAD-based models. 

Interatomic Potential Repository: This repository 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.

JARVIS-FF: This force-fields (interatomic potentials) database is a collection of LAMMPS calculation-based data covering crystal structure, formation energy, phonon density of states, band structure, surface energy and defect formation energy. It is designed to help select the optimal force-field for the user’s application. 

JARVIS-DFT:  This is a DFT database of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.

Phase-Based Data Repository This is a  community phase-based property database that includes a variety of experimental and computational multicomponent thermodynamic and kinetic.  This database also includes NIST Diffusion Data Center ( a collection of diffusion references from 1960-1980)

AM-BenchMarking Data Repository This is a repository of NIST Metal AM Benchmark data associated with the NIST AM Benchmark Test series.

DFT Benchmarking Data

National Research Council Postdoctoral Fellowship Opportunities 

US citizens who are interested in postdoctoral opportunities in the group, should consider applying for one of the opportunities listed below.   NIST participates in the NRC competitions as a part of the February 1 and August 1 deadlines.   More information on the is program is available at the following link.

• Coupling electronic structure methods, artificial intelligence, and data-driven approaches for next-generation quantum materials (50.64.21.C1087)
• Development of a Digital Twin Framework for Metal Additive Manufacturing (50.64.21.C0951)
• Structure, property and performance of high impurity-tolerant metals and alloys. (50.64.21.C0935)
• Dynamic Atomistic Predictions of Crystalline, Crystal Defect and Liquid Metal Properties (50.64.21.C0643)
• Developing Novel Additive Manufacturing Processing Methods (50.64.21.C0656)
• Diffusion in Advanced Materials (50.64.21.B6790)
• Integrating Data and Computational Tools for Advanced Materials Design (50.64.21.B7663)
• Processing-Structure-Property Relationships for ICME of Advanced Engineering Alloys (50.64.21.B8309)
• Phase-Based Property Data Informatics (50.64.21.B8065)
• Microstructure Evolution Models for Optimizing Metals Additive Manufacturing (50.64.21.B8063)
• Corrosion Behavior of Additively-Manufactured Alloys (50.64.21.C0193)
• Materials Modeling, Characterization, and Design for Plasticity (50.64.21.B7890)
• The Center for Theoretical and Computational Materials Science (50.64.21.B1819)
• Microstructural Modeling of Additively Manufactured Materials (50.64.21.B8306)

New Materials for Sustainable Energy Applications

Characterization of 3D Photovoltaics

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

Metrics for Reactive Wetting in Complex Systems

Lead Free-Solders

Multiscale MD-FEM Methodology