pqam-rmsadtandoc2023 0.1.2

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

Lattice-Distortion

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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) 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

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/)