This repository contains code used and described in references ^{1 2}.

If you find this code useful in producing published works, please provide an appropriate citation. Note that the second citation is focused on adding features that make use of GPR models based on derivative information produced by the core code base. For now, the GPR code, along with more information, may be found under here. In a future release, we expect this to be fully integrated into the code base rather than a standalone module.

Code included here can be used to perform thermodynamic extrapolation and interpolation of observables calculated from molecular simulations. This allows for more efficient use of simulation data for calculating how observables change with simulation conditions, including temperature, density, pressure, chemical potential, or force field parameters. Users are highly encourage to work through the Jupyter Notebooks presenting examples for a variety of different observable functional forms. We only guarantee that this code is functional for the test cases we present here or for which it has previously been applied Additionally, the code may be in continuous development at any time. Use at your own risk and always check to make sure the produced results make sense. If bugs are found, please report them. If specific features would be helpful just let us know and we will be happy to work with you to come up with a solution.

• Fast calculation of derivatives

This package is actively used by the author. Please feel free to create a pull request for wanted features and suggestions!

thermoextrap may be installed with either (recommended)

conda install -c conda-forge thermoextrap

or

pip install thermoextrap

If you use pip, then you can include additional dependencies using

pip install thermoextrap[all]

If you install thermoextrap with conda, there are additional optional dependencies that take some care for installation. We recommend installing the following via pip, as the versions on the conda/conda-forge channels are often a bit old.

pip install tensorflow tensorflow-probability gpflow

To install from source do the following:

git clone git@github.com:usnistgov/thermoextrap.git
cd thermoextrap
pip install . [-e]

To (optionally) include the example data do the following:

git submodule update --init  --recursive

import thermoextrap

See the documentation for a look at thermoextrap in action.

To have a look at using thermoextrap with Gaussian process regression, look in the gpr and gpr_active_learning directories.

This is free software. See LICENSE.

This package extensively uses the cmomy package to handle central comoments.

Questions may be addressed to Bill Krekelberg at william.krekelberg@nist.gov or Jacob Monroe at jacob.monroe@uark.edu.

This package was created with Cookiecutter and the wpk-nist-gov/cookiecutter-pypackage Project template forked from audreyr/cookiecutter-pypackage.