zhubonan / ASAP

ASAP is a package that quickly checks if there's enough signal in a dataset of crystal or molecular structures that is worthy of furthur analysis.

ASAP

Automatic Selection And Prediction tools for materials and molecules

Basic usage

Type asap and use the sub-commands for various tasks.

e.g. in the folder ./tests/

to generate SOAP descriptors:

asap gen_desc --fxyz small_molecules-1000.xyz soap

for columb matrix:

asap gen_desc -f small_molecules-1000.xyz --no-periodic cm

for pca map:

asap map -f small_molecules-SOAP.xyz -dm [SOAP-n4-l3-c1.9-g0.23] -c dft_formation_energy_per_atom_in_eV pca

Installation & requirements

python 3

Installation:

python3 setup.py install --user

Requirements:

• numpy scipy scikit-learn json ase dscribe umap-learn PyYAML click

Add-Ons:

• (for finding symmetries of crystals) spglib
• (for annotation without overlaps) adjustText
• The FCHL19 representation requires code from the development brach of the QML package. Instructions on how to install the QML package can be found on https://www.qmlcode.org/installation.html.

One can use the following comments for installing the packages:

pip3 install --upgrade pip

python3 -m pip install --user somepackage .or. pip3 install --user somepackage

How to add your own atomic or global descriptors

• To add a new atomic descriptor, add a new Atomic_Descriptor class in the asaplib/descriptors/atomic_descriptors.py. As long as it has a __init__() and a create() method, it should be competitable with the ASAP code. The create() method takes an ASE Atoms object as input (see: ASE)

• To add a new global descriptor, add a new Global_Descriptor class in the asaplib/descriptors/global_descriptors.py. As long as it has a __init__() and a create() method, it is fine. The create() method also takes the Atoms object as input.

Additional tools

In the directory ./scripts/ and ./tools/ you can find a selection of other python tools:

• frame_select.py: select a subset of the xyz frames based on random or farthest point sampling selection.

• kernel_density_estimate.py: does principle component analysis on the kernel matrix, estimates kernel density, and makes plots

• clustering.py: does clustering tasks based on the kernel matrix, does plotting as well.