Python Isotherm Prediction (PyIsoP)
PyIsoP uses a fast and accurate, semi-analytical algorithm to calculate the adsorption of single-site molecules in nanoporous materials using energy grids. The energy grid algorithm in PyIsoP is parallelized using Dask (python) such that PyIsoP will work just as well on your laptop (1 CPU, multi-threading) and on a High Performance Cluster (hundreds of CPU, multi-processing) with no or little additional coding on your part. The method itself is about 100 times faster compared to grand canonical Monte Carlo (GCMC) simulations, combined with the fast calculation of energy grids PyIsoP is ideal for obtaining quick estimates of adsorption and even interactive high-throughput screening of large databases. Although originally developed for predicting hydrogen adsorption, the underlying algorithm can be readily applied to other molecules which can also be modeled by a single-site (spherical probe) such as methane and noble gases. Since the energy landscape of a material is usually independent of temperature [1], including thermal swing into our calculations is also quick and easy. The energy grids can also be used to visualize and analyze complex pores in a materials by choosing the right isoenergy contours from the energy landscape. Please refer to our documentation page on ReadTheDocs for theory, examples and the API reference.
How does it work...?
Although PyIsoP offers many functionalities, the overall approach can be summarized as shown
Coming Soon !
We are currently working on adding an automated, energy-based, molecular siting module and extending the isotherm prediction approach to ethane and higher alkanes. Stay tuned for new features, tests, bug-fixes and examples.
| [1] | Feynman-Hibbs correction induces a temperature dependency on the energy grid, however this maybe assumed to be weak. For polyatomic probes, the existence of different orientations at any given site also imparts a temperature dependence on the energy grid. |
- ** Acknowledgements:
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Andrew Rosen, Snurr Research Group, Northwestern Univerisity. Project based on the Computational Molecular Science Python Cookiecutter version 1.0.
This work is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences through the Nanoporous Materials Genome Center under award DE-FG02-17ER16362.
Created by: Arun Gopalan
