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Thermodynamics and Kinetics Group

The Thermodynamics and Kinetics Group develops measurement methods, models, data, standards, and science for the thermodynamics, kinetics, phase transformations, microstructure evolution, and properties of materials (e.g., metals, semiconductors, inorganics) of technological interest. The Group uses this expertise to define processing, structure and performance relations and accelerate the design of advanced materials.

Materials design approach

Group Competence

CALPHAD-Based Methods

Atomistic Simulations and Potential Development 

Density Functional Theory

First Principles Phase Stability Calculations

Phase Field Modeling

Phase Transformations, Diffusion, Solidification, Surface energy driven Processes

DTA/DSC/Dilatometry/X-ray Diffraction/Microscopy/Metals Processing

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

Projects and Programs



Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in

Short-Range Charge Density Wave Order in TaS2

Jaydeep D. Joshi, Heather M. Hill, Sugata Chowdhury, Christos D. Malliakas, Francesca M. Tavazza, Utpal Chatterjee, Angela R. Hight Walker, Patrick M. Vora
2H-TaS2 undergoes a charge density wave (CDW) transition at T_CDW ~ 75 K, however key questions regarding the onset of CDW order remain under debate. In this

PFHub: The Phase-Field Community Hub

Daniel Wheeler, Trevor Keller, Jonathan E. Guyer, James A. Warren, Stephen DeWitt, Andrea Jokisaari, Daniel Schwen, Larry Aagesen, Olle Heinonen, Michael Tonks, Peter Voorhees
An online portal provides a valuable space for scientific communities to summarize a shared challenge, collect attempts at a solution, and present a

Simulation of TTT curves for additively manufactured Inconel 625

Carelyn E. Campbell, Greta Lindwall, Eric Lass, Fan Zhang, Mark R. Stoudt, Andrew J. Allen, Lyle E. Levine
The ability to use common computational thermodynamic and kinetic tools to study the microstructure evolution in Inconel 625 (IN625) manufactured using the

Diffusion in the Ti-Al-V system

Greta Lindwall, Kil-Won Moon, Zhangqi Chen, Michael J. Mengason, Maureen E. Williams, Justin Gorham, Ji-Cheng Zhao, Carelyn E. Campbell
Diffusion in the Ti-Al-V system is studied and a CΑLPHAD diffusion mobility description is developed. Diffusion couple experiments are used to obtain


2018 Henry Marion Howe Medal

The Authors received the ASM Henry Marion Howe Medal for 2018 for their paper entitled: “ Formation of the Ni3Nb δ-Phase in Stress

News and Updates

Coining Less Expensive Currency

Nickels are ubiquitous in American life, tumbling around in pockets, rolling under car seats, and emerging from the back of dryers to be used over and over for

Press Coverage

Past Projects