PyQAlloy: Python tools for ensuring the Quality of Alloys data

Introduction

PyQAlloy development is a part of ULTERA Project carried under the DOE ARPA-E ULTIMATE program that aims to develop a new generation of materials for turbine blades in gas turbines and related applications. The ULTERA Project, along is led by Phases Research Lab at Penn State. As a part of it, we developed a new large-scale database of high entropy alloys (HEAs) reported in the literature along with their experimental properties. As of March 2023, the database contains around 6,000 property data points of 2,500 HEAs coming from almost 500 publications. It is currently the largest database of HEAs in the world, and while it is not publicly available we welcome collaborators who would like to use it in their research or contribute to it.

ULTERA Database is not simply a dataset but features a robust set of data processing, curation, and aggregation tools we built for the last 3 years. These tools allowed us to remove around the 5-10% erroneous data we identified in datasets available in the literature. Most of our tools are not published yet, as the project is ongoing (they give us a competitive advantage), and because most of the tools require an elaborate computing infrastructure setup.

However, as some of them are less-infrastructure-demanding and are, at the same time highly applicable outside HEAs, we decided to release them as separate packages. This repository contains the first of such packages, PyQAlloy, which is a Python package for detecting data abnormalities in datasets of arbitrary alloys, ranging from complex, concentrated solutions, i.e. High Entropy Alloys (HEAs) / Multi Principle Element Alloys (MPEAs) / Concentrated Complex Alloys (CCAs) to more traditional alloys such as steels, nickel-based superalloys, etc.

Installation

At the moment, PyQAlloy is not available on PyPI, so you need to clone the repository and install it in editable mode. While not required, it is recommended to first set up a virtual environment using venv or Conda. This ensures that one of the required versions of Python (3.9+) is used and there are no dependency conflicts. If you have Conda installed on your system (see instructions at https://docs.conda.io/en/latest/miniconda.html), you can create a new environment with:

conda create -n pyqalloy python=3.9 jupyter
conda activate pyqalloy

Then, clone PyQAlloy from GitHub like

git clone https://github.com/PhasesResearchLab/PyQAlloy.git

Or by downloading a ZIP file (not recommended if you want to make changes). Please note this will, by default, download the latest development version of the software, which may not be stable. For a stable version, you can specify a version tag after the URL with --branch <tag_name> --single-branch.

Then, move to the PyQAlloy folder and install in editable (-e) mode.

cd PyQAlloy
pip install -e .

If you are using the ULTERA Project infrastructure, now you should fill in your details into the pyqalloy/credentials.json with name, dbKey, and dataServer fields, and you should be ready to go! :)

If you are not using the ULTERA infrastructure, you will need to set up your own MongoDB database and fill it with data that conforms to the ULTERA schema. This will be quite elaborate, but we have the tools to do it, and we can assist you.