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Abstract High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.
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Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach
High-entropy alloys (HEAs), characterized as compositionallycomplex solid solutions with five or more metal elements, have emerged as a novelclass of catalytic materials with unique attributes. Because of the remarkablediversity of multielement sites or site ensembles stabilized by configurationalentropy, human exploration of the multidimensional design space of HEAspresents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-basedmodeling. Leveraging deep learning interatomic potentials for large-scalemolecular simulations and pretrained machine learning models of surfacereactivity, our approach effectively rationalizes the enhanced activity of apreviously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemicaloxygen reduction, as corroborated by experimental observations. We contend thatthis framework deepens our fundamental understanding of the surface reactivity ofhigh-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatilematerial platform for catalyzing sustainable chemical and energy transformations.
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- PAR ID:
- 10525722
- Publisher / Repository:
- pubs.acs.org/JPCC
- Date Published:
- Journal Name:
- The Journal of Physical Chemistry C
- Volume:
- 128
- Issue:
- 27
- ISSN:
- 1932-7447
- Page Range / eLocation ID:
- 11183 to 11189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract A wide range of deep learning-based machine learning (ML) techniques are extensively applied to the design of high-entropy alloys (HEAs), yielding numerous valuable insights. Kolmogorov–Arnold networks (KAN) is a recently developed architecture that aims to improve both the accuracy and interpretability of input features. In this work, we explore three different datasets for HEA design and demonstrate the application of KAN for both classification and regression models. In the first example, we use a KAN classification model to predict the probability of single-phase formation in high-entropy carbide ceramics based on various properties such as mixing enthalpy and valence electron concentration. In the second example, we employ a KAN regression model to predict the yield strength and ultimate tensile strength of HEAs based on their chemical composition and process conditions including annealing time, cold rolling percentage, and homogenization temperature. The third example involves a KAN classification model to determine whether a certain composition is an HEA or non-HEA, followed by a KAN regressor model to predict the bulk modulus of the identified HEA, aiming to identify HEAs with high bulk modulus. In all three examples, KAN either outperform or match the performance in terms of accuracy such asF1 score for classification and mean square error, and coefficient of determination (R2) for regression of the multilayer perceptron by demonstrating the efficacy of KAN in handling both classification and regression tasks. We provide a promising direction for future research to explore advanced ML techniques, which lead to more accurate predictions and better interpretability of complex materials, ultimately accelerating the discovery and optimization of HEAs with desirable properties.more » « less
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High-entropy alloys (HEAs) are a class of metal alloys consisting of four or more molar equal or near-equal elements. HEA nanomaterials have garnered significant interest due to their wide range of applications, such as electrocatalysis, welding, and brazing. Their unique multi-principle high-entropy effect allows for the tailoring of the alloy composition to facilitate specific electrochemical reactions. This study focuses on the synthesis of high-purity HEA nanoparticles using the method of femtosecond laser ablation synthesis in liquid. The use of ultrashort energy pulses in femtosecond lasers enables uniform ablation of materials at significantly lower power levels compared to longer pulse or continuous pulse lasers. We investigate how various femtosecond laser parameters affect the morphology, phase, and other characteristics of the synthesized nanoparticles. An innovative aspect of our solution is its ability to rapidly generate multi-component nanoparticles with a high fidelity as the input multi-component target material at a significant yielding rate. Our research thus focuses on a novel synthesis of high-entropy alloying CuCoMn1.75NiFe0.25 nanoparticles. We explore the characterization and unique properties of the nanoparticles and consider their electrocatalytic applications, including high power density aluminum air batteries, as well as their efficacy in the oxygen reduction reaction (ORR). Additionally, we report a unique nanowire fabrication phenomenon achieved through nanojoining. The findings from this study shed light on the potential of femtosecond laser ablation synthesis in liquid (FLASiL) as a promising technique for producing high-purity HEA nanoparticles.more » « less
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Abstract The vast high entropy alloy (HEA) composition space is promising for discovery of new material phases with unique properties. This study explores the potential to achieve rare‐earth‐free high magnetic anisotropy materials in single‐phase HEA thin films. Thin films of FeCoNiMnCu sputtered on thermally oxidized Si/SiO2substrates at room temperature are magnetically soft, with a coercivity on the order of 10 Oe. After post‐deposition rapid thermal annealing (RTA), the films exhibit a single face‐centered‐cubic phase, with an almost 40‐fold increase in coercivity. Inclusion of 50 at.% Pt in the film leads to ordering of a singleL10high entropy intermetallic phase after RTA, along with high magnetic anisotropy and 3 orders of magnitude coercivity increase. These results demonstrate a promising HEA approach to achieve high magnetic anisotropy materials using RTA.more » « less
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We report on a novel TaNbZrHfTi-based high entropy alloy (HEA) which demonstrates distinctive dual-phase superconductivity. The HEA was synthesized under high pressures and high temperatures starting from a ball milled mixture of elemental metals in a large-volume Paris–Edinburgh cell with P ≈ 6 GPa and T = 2300 K. The synthesized HEA is a phase mixture of BCC (NbTa)0.45(ZrHfTi)0.55 with Tc1 = 6 K and FCC (NbTa)0.04(ZrHfTi)0.96 with Tc2 = 3.75 K. The measured magnetic field parameters for the HEA are lower critical field, Hc1(0) = 31 mT, and a relatively high upper critical field, Hc2(0) = 4.92 T. This dual-phase system is further characterized by the presence of a second magnetization peak, or the fishtail effect, observed in the virgin magnetization curves. This phenomenon, which does not distort the field-dependent magnetization hysteresis loops, suggests intricate pinning mechanisms that could be potentially tuned for optimized performance. The manifestation of these unique features in HEA superconductivity reinforces phase-dependent superconductivity and opens new avenues in the exploration of novel superconducting materials.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1021/acs.jpcc.4c01630
