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 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!

Use one of the following to install thermoextrap:

conda install -c conda-forge thermoextrap

or

pip install thermoextrap

To utilize the full potential of thermoextrap, additional dependencies are needed. This can be done via pip by using:

pip install thermoextrap[all]

If using conda, then you'll have to manually install some dependencies. For example, you can run:

conda install bottleneck dask "pymbar>=4.0"

At this time, it is recommended to install the Gaussian Process Regression (GPR) dependencies via pip, as the conda-forge recipes are slightly out of date:

pip install tensorflow tensorflow-probability "gpflow>=2.6.0"

thermoextrap makes extensive use of the cmomy library. If using thermoextrapin parallel, you should either first compile cached numba code with

python -m cmomy.compile

Or run your command with the environment variable CMOMY_NUMBA_CACHE set to false

CMOMY_NUMBA_CACHE=false python ....

The repo is setup to use uv to create a development environment. Use the following:

uv sync

This environment will include all additional dependencies mentioned above.

Alternatively, you can install the (locked) development dependencies using:

uv export --group dev > requirements-dev.txt
uv pip install -r requirements/lock/dev.txt

It is not recommended to install the development dependencies with conda.

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.

See changelog.

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 using Cookiecutter with the usnistgov/cookiecutter-nist-python template.