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High-throughput materials screening algorithm based on first-principles density functional theory and artificial

Meena Rittiruam^1,2,3, Jakapob Noppakhun^1,2,3, Sorawee Setasuban^1,2,4

¹High-Performance Computing Unit (CECC-HCU), Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Chulalongkorn University, Bangkok, 10330, Thailand.

Scientific Reports

|October 5, 2022

Summary

This study developed a fast, accurate method combining Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural networks (ANN) for predicting high-entropy alloy phases. The approach efficiently screens PtPd-based alloys, identifying elements favoring FCC or BCC structures.

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Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

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Published on: May 29, 2018

Area of Science:

Materials Science
Computational Materials Science
Alloy Design

Background:

High-entropy alloys (HEAs) offer unique properties but their complex phase behavior requires efficient prediction methods.
Computational screening of HEAs is crucial for discovering new materials with desired characteristics.
Traditional methods for phase prediction can be computationally intensive.

Purpose of the Study:

To develop and validate a high-throughput computational framework for predicting the phase stability of PtPd-based high-entropy alloys.
To combine first-principles calculations with machine learning for accelerated materials discovery.
To identify elemental preferences for specific crystallographic phases (FCC and BCC) in HEAs.

Main Methods:

Utilized Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) within first-principles density functional theory to generate formation energy and lattice parameter data.
Employed artificial neural networks (ANNs) to build predictive models based on selected features from the generated data.
Validated the KKR-CPA-ANN algorithm on a large dataset of 9139 HEA systems.

Main Results:

The KKR-CPA-ANN algorithm achieved high accuracy, with R-squared values near unity and mean relative errors below 5% for phase prediction.
Successfully predicted phase formation tendencies for various elements in PtPd-based HEAs, distinguishing between FCC and BCC phase preferences.
Identified specific elements that predominantly favor FCC, BCC, or exhibit dual-phase tendencies.

Conclusions:

The combined KKR-CPA and ANN approach significantly reduces computational cost for screening PtPd-based HEAs.
This method accurately predicts crystallographic structures (FCC, BCC) of HEAs, facilitating efficient materials design.
The findings provide valuable insights into the phase formation rules for PtPd-based high-entropy alloys.