The search for high entropy alloys: a high-throughput $\textit{ab-initio}$ approach

Abstract: While the ongoing search to discover new high-entropy systems is slowly expanding beyond metals, a rational and effective method for predicting "in silico" the solid solution forming ability of multi-component systems remains yet to be developed. In this article, we propose a novel high-throughput approach, called "LTVC", for estimating the transition temperature of a solid solution: ab-initio energies are incorporated into a mean field statistical mechanical model where an order parameter follows the evolution of disorder. The LTVC method is corroborated by Monte Carlo simulations and the results from the current most reliable data for binary, ternary, quaternary and quinary systems (96.6%; 90.7%; 100% and 100%, of correct solid solution predictions, respectively). By scanning through the many thousands of systems available in the AFLOW consortium repository, it is possible to predict a plethora of previously unknown potential quaternary and quinary solid solutions for future experimental validation.

High-Throughput Discovery of High-Entropy Alloys: An Ab-Initio Approach

The paper "The search for high entropy alloys: a high-throughput ab-initio approach" by Yoav Lederer et al., explores the exploration of high-entropy alloys (HEAs) employing advanced computational techniques. Although high-entropy systems are gaining significant interest, robust prediction of which multi-component alloys form single-phase solid solutions remains elusive. The authors propose an innovative method leveraging ab-initio computations combined with statistical mechanical modeling to tackle this challenge.

Methodological Innovation

The authors introduce a high-throughput ab-initio method, termed "LTV-Curtarolo", which incorporates ab-initio energies into a mean field statistical mechanical model. This method estimates the transition temperature of multi-component systems into solid solution phases. The methodology comprises several key steps:

1. Cluster Expansion (CE): The set of inequivalent atomic configurations is generated using a group-theoretical approach and subsequently subjected to ab-initio calculations to estimate energies for these configurations. The energies are then fed into the CE model within the Alloy Theoretic Automated Toolkit (ATAT) to predict zero-temperature energies of potential configurations.
2. Statistical Modeling: The configurations are utilized in a mean field statistical mechanical model called the Generalized Quasi-Chemical Approximation (GQCA). This model is particularly suitable for describing spatially homogeneous solid solutions where long-range order is negligible.
3. Order Parameter Analysis: To predict phase transitions, order parameters are introduced to track the evolution of disorder with temperature. The transition temperatures are determined by the maximal change in these order parameters as the system transitions from disorder to order.

Results and Analysis

The accuracy of the LTV-Curtarolo method is validated through comprehensive comparisons with experimental data, existing phase diagram calculations, and Monte Carlo simulations. Specifically:

• Binary Alloys: The method achieves a correct prediction rate of 87.2% when compared with experimental data for 117 binary alloys. Transition temperatures are slightly overestimated, demonstrating the sensitivity and reliability of the approach.
• Ternary Alloys: Comparing with CALPHAD results, the methodology boasts a congruence of 77.0% across 441 ternary alloys, affirming the method's robustness even as complexity increases.
• High-Throughput Exploration of Quaternary and Quinary Systems: The study extends its predictive capability to over 1100 quaternary and quinary alloys, identifying a significant number of potential single-phase HEAs. Notably, cases found experimentally to form solid solutions are corroborated with 100% success rate.

Implications and Future Directions

The findings suggest that short-range order effects are crucial in accurately predicting solid solutions, challenging prior assumptions that neglected these effects. The LTV-Curtarolo method introduces a computationally viable framework for massively parallel exploration of alloy space, considerably increasing the throughput of solid solution discovery.

Future developments should focus on integrating vibrational entropy consideration and expanding the methodology to non-metallic high-entropy systems. This research paves the way for discovering novel HEAs with potentially groundbreaking applications in materials design.

The study highlights the limitations of conventional empirical descriptors in identifying solid solutions, further advocating for reliance on comprehensive ab-initio approaches as opposed to simplified heuristic models. The implications for theoretical materials science are profound, potentially catalyzing the discovery of materials with unique and advantageous properties for industrial applications.