Deciphering Chemical Ordering in High Entropy Materials: A Machine Learning-Accelerated High-throughput Cluster Expansion Approach

TL;DR

This paper introduces a machine learning-accelerated cluster expansion method using a GNN model to efficiently predict chemical ordering in high entropy materials, reducing computational costs and improving accuracy.

Contribution

The work presents a novel hybrid approach combining GNNs with cluster expansion to significantly lower computational expenses and enhance predictive accuracy for complex multicomponent systems.

Findings

Achieved RMSE below 10 meV/atom in energy predictions.

Validated method on diboride and high-entropy alloy systems.

Enabled large-scale analysis of chemical ordering evolution.

Abstract

The Cluster Expansion (CE) Method encounters significant computational challenges in multicomponent systems due to the computational expense of generating training data through density functional theory (DFT) calculations. This work aims to refine the cluster and structure selection processes to mitigate these challenges. We introduce a novel method that significantly reduces the computational load associated with the calculation of fitting parameters. This method employs a Graph Neural Network (GNN) model, leveraging the M3GNet network, which is trained using a select subset of DFT calculations at each ionic step. The trained surrogate model excels in predicting the volume and energy of the final structure for a relaxation run. By employing this model, we sample thousands of structures and fit a CE model to the energies of these GNN-relaxed structures. This approach, utilizing a large training dataset, effectively reduces the risk of overfitting, yielding a CE model with a root-mean-square error (RMSE) below 10 meV/atom. We validate our method's effectiveness in two test cases: the (Cr,Hf,Mo,Ta,Ti,Zr)B$_2$ diboride system and the Refractory High-Entropy Alloy (HEA) AlHfNbTaTiZr system. Our findings demonstrate the significant advantages of integrating a GNN model, specifically the M3GNet network, with CE methods for the efficient predictive analysis of chemical ordering in High Entropy Materials. The accelerating capabilities of the hybrid ML-CE approach to investigate the evolution of Short Range Ordering (SRO) in a large number of stoichiometric systems. Finally, we show how it is possible to correlate the strength of chemical ordering to easily accessible alloy parameters.