Lightweight but caffeinated Python implementation of computational methods for statistical mechanical calculations of configurational states in crystalline materials.
smol is a minimal implementation of computational methods to calculate statistical mechanical and thermodynamic properties of crystalline material systems based on the cluster expansion method from alloy theory and related methods. Although smol is intentionally lightweight---in terms of dependencies and built-in functionality---it has a modular design that closely follows underlying mathematical formalism and provides useful abstractions to easily extend existing methods or implement and test new ones.
smol currently includes the following functionality:
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Defining cluster expansion functions for a given disordered structure using a variety of available site basis functions with and without explicit redundancy.
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Option to include explicit electrostatics in expansions using the Ewald summation method.
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Computing correlation vectors for a set of training structures with a variety of functionality to inspect the resulting feature matrix.
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Defining fitted cluster expansions for subsequent property prediction.
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Fast evaluation of correlation vectors and differences in correlation vectors from local updates in order to quickly compute properties and changes in properties for specified supercell sizes.
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Flexible toolset to sample cluster expansions using Monte Carlo with canonical, semigrand canonical, and charge neutral semigrand canonical ensembles using a Metropolis or a Wang-Landau sampler.
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Special quasi-random structure generation based on either correlation vectors or cluster interaction vectors.
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Solving for periodic ground-states of any given cluster expansion with or without electrostatics over a given supercell.
smol is built on top of pymatgen so any pre/post structure analysis can be done seamlessly using the various functionality supported there.
From pypi:
pip install smol
From source:
Clone the repository. The latest tag in the main branch is the stable version of the
code. The main branch has the newest tested features, but may have more
lingering bugs. From the top level directory
pip install .
Although smol is not tested on Windows platforms, it should still run on Windows since
there aren't any platform specific dependencies. The only known installation issue
is building pymatgen dependencies. If simply running pip install smol fails, try
installing pymatgen with conda first:
conda install -c conda-forge pymatgen
pip install smol
You can also simply use the environment.yml file in the repository to install smol:
conda env create -f environment.yml
source activate smol-env
Refer to the documentation for details on using smol. Going through the example notebooks will also help you get started. You can run the example notebooks interactively in binder.
If you use smol in your research, please give the repo a star ⭐ and refer to the citing page in the documentation for formal citation information.
We welcome all your contributions with open arms! Please fork and pull request any contributions. See the developing page in the documentation for how to contribute.
The most recent changes are detailed in the change log.
Statistical Mechanics on Lattices (smol) Copyright (c) 2022, The Regents
of the University of California, through Lawrence Berkeley National
Laboratory (subject to receipt of any required approvals from the U.S.
Dept. of Energy) and the University of California, Berkeley. All rights reserved.
If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office at
IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights. As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit others to do so.