pqam-rmsadtandoc2023 0.1.4

PyQAlloy-compatible Model for RMSAD prediction based on Tandoc 2023 (https://doi.org/10.1038/s41524-023-00993-x)

Lattice-Distortion

Release:

Compatible with:

Tests:

License:

This Fork

This small repository is a lightweight fork version of the original one by Tandoc. It is only slightly modified to fit automated pipelines of the ULTERA Database (ultera.org) infrastrcutre, which expects model.py script with predict(comp: pymatgen.Composition) and predict(comp: str) function returning an ordered array of output numbers or a labeled dictionary of them.

Output Order: [rmsad_Tandoc2023]

Output Meaning (all based on 10.1038/s41524-023-00993-x):

• rmsad_Tandoc2023 - Root Mean Squared Atomic Displacement in the units of Angstrom

Install and Use

There are no dependencies beyond a fairly recent pymatgen (>=2022.1.9). You need to simply

pip install pqam-rmsadtandoc2023

and you should be good to go! Then you can run it on your compositions in Python like

from pqam-rmsadtandoc2023 import predict

print(predict('Mo25 Nb25 Hf50'))
print(predict('MoNbHf'))
print(predict('Mo33.3 Nb33.3 Hf33.3'))

or from command line like

python -c "import pqam_rmsadtandoc2023 as m;print(m.predict('Mo25 Nb25 Hf50'));print(m.predict('MoNbHf'));print(m.predict('Mo33.3 Nb33.3 Hf33.3'))"

Original README by Tandoc

This repository contains relevant code and data for "Mining lattice distortion, strength, and intrinsic ductility of refractory high-entropy alloys using physics-informed >statistical learning" by Christopher Tandoc, Yong-Jie Hu, Liang Qi, and Peter K. Liaw to be published in npj Computational Materials

RMSAD_tool.py is a linux command line script written in python that takes a chemical composition in the form of a text string and prints the lattice distortion in angstroms.

example usgage: ./RMSAD_tool.py Ti0.5V0.5

-This script uses pymatgen (https://pymatgen.org/) to process the input string and is thus a requirement for the script to work. Depending on the version of pymatgen you have >installed, lines 3 and 380 may need to be modified (https://matsci.org/t/python-problem-with-pymatgen/35720) -numpy (https://numpy.org/) is also a dependency -This tool is currently only able to make predictions for compositions containing Ti,Zr,Hf,V,Nb,Ta,Mo,W,Re,Ru and will return an error if any other elements are present in >the input -B2 and elemental feature data are defined in dictionaries at the beginning of the code

training.ipynb and training_data.csv contains code and data to reproduce the rmsad model training that was performed in the paper -jupyter notebook is needed to open training.ipynb, dependencies are numpy, pymatgen, matplotlib (https://matplotlib.org/), pandas (https://pandas.pydata.org/), and >sklearn(https://scikit-learn.org/stable/)

Miscellaneous

Last maintenance check: January 22nd, 2024