thermoextrap: Thermodynamic Extrapolation/Interpolation Library

This repository contains code used and described in:

Monroe, J. I.; Hatch, H. W.; Mahynski, N. A.; Shell, M. S.; Shen, V. K. Extrapolation and Interpolation Strategies for Efficiently Estimating Structural Observables as a Function of Temperature and Density. J. Chem. Phys. 2020, 153 (14), 144101. https://doi.org/10.1063/5.0014282.

Monroe, J. I.; Krekelberg, W. P.; McDannald, A.; Shen, V. K. Leveraging Uncertainty Estimates and Derivative Information in Gaussian Process Regression for Expedited Data Collection in Molecular Simulations. In preparation.

Overview

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 docs/notebooks/gpr. 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 Notebook tutorial (Ideal_Gas_Example.ipynb) 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.

Features

• Fast calculation of derivatives

Status

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

Quick start

thermoextrap may be installed with either (recommended)

conda install -c wpk-nist 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

Example usage

import thermoextrap

Documentation

See the documentation for a look at thermo-extrap in action.

To have a look at using thermo-extrap with Gaussian process regression, look in the [gpr][docs/notebooks/gpr] directory.

License

This is free software. See LICENSE.

Related wor

Related work

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

Contact

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

Credits

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

History

0.0.1 (2021-01-04)

• First release on PyPI.

This software was developed by employees of the National Institute of Standards and Technology (NIST), an agency of the Federal Government. Pursuant to title 17 United States Code Section 105, works of NIST employees are not subject to copyright protection in the United States and are considered to be in the public domain. Permission to freely use, copy, modify, and distribute this software and its documentation without fee is hereby granted, provided that this notice and disclaimer of warranty appears in all copies.

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