High entropy alloy electrocatalysts: a critical assessment of fabrication and performance

2020 ◽  
Vol 8 (30) ◽  
pp. 14844-14862 ◽  
Author(s):  
Gracita M. Tomboc ◽  
Taehyun Kwon ◽  
Jinwhan Joo ◽  
Kwangyeol Lee
Keyword(s):  
Machine Learning ◽  
Present Status ◽  
Critical Assessment ◽  
Optimum Composition ◽  
High Entropy Alloy ◽  
Computational Studies ◽  
Combinatorial Screening ◽  
High Entropy ◽  
Composition And Structure ◽  
And Performance

Critical assessment of the present status of HEA NPs as catalysts, including an in-depth discussion of computational studies, combinatorial screening, or machine-learning studies to find the optimum composition and structure of HEA electrocatalysts.

scholarly journals Exploring the Relationship between the CO Oxidation Reaction and the Elemental Composition in Electrocatalysts with Machine Learning - a Case Study of PtRuPdRhAu High Entropy Alloys

2021 ◽  
Author(s):  
Vladislav Mints ◽  
Jack Pedersen ◽  
Alexander Bagger ◽  
Jonathan Quinson ◽  
Andy Anker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Co Oxidation ◽  
Oxidation Reaction ◽  
Density Functional ◽  
Hydrogen Oxidation ◽  
Bayesian Optimization ◽  
High Entropy Alloy ◽  
Onset Potential ◽  
High Entropy ◽  
Co Oxidation Reaction

In recent years, the development of complex multi-metallic nanomaterials like high entropy alloy (HEA) catalysts has gained popularity. Composed of 5 or more metals, the compositions of HEAs exhibit extreme diversity. This is both a promising avenue to identify new catalysts and a severe constraint on their preparation and study. To address the challenges related to the preparation, study and optimization of HEAs, machine learning solutions are attractive. In this paper, the composition of PtRuPdRhAu hydrogen oxidation catalysts is optimized for the CO oxidation reaction. This is achieved by constructing a dataset using Bayesian optimization as guidance. For this quinary nanomaterial, the best performing composition was found within the first 35 experiments. However, the dataset was expanded until a total of 68 samples were investigated. This final dataset was used to construct a random forest regression model and a linear model. These machine learned models were used to assess the relationships between the concentrations of the consituent elements and the CO oxidation reaction onset potential. The onset potentials were found to correlate with the composition dependent adsorption energy of *OH obtained from density functional theory. This study demonstrates, how machine learning can be employed in an experimental setting to investigate the vast compositional space of HEAs.

scholarly journals Prediction on Mechanical Properties of Non-Equiatomic High-Entropy Alloy by Atomistic Simulation and Machine Learning

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 922
Author(s):  
Liang Zhang ◽  
Kun Qian ◽  
Björn W. Schuller ◽  
Yasushi Shibuta
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Md Simulation ◽  
Mechanical Performance ◽  
Binary Classification ◽  
Computational Simulation ◽  
Engineering Application ◽  
High Entropy Alloy ◽  
High Entropy ◽  
Learning Machine

High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years, due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on equiatomic or near equiatomic HEAs. However, there is probably far more treasure in those non-equiatomic HEAs with carefully designed composition. In this study, the molecular dynamics (MD) simulation combined with machine learning (ML) methods was used to predict the mechanical properties of non-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensile test of 900 HEA single-crystal samples by MD simulation. Eight ML models were investigated and compared for the binary classification learning tasks, ranging from shallow models to deep models. It was found that the kernel-based extreme learning machine (KELM) model outperformed others for the prediction of yield stress and Young’s modulus. The accuracy of the KELM model was further verified by the large-sized polycrystal HEA samples. The results show that computational simulation combined with ML methods is an efficient way to predict the mechanical performance of HEAs, which provides new ideas for accelerating the development of novel alloy materials for engineering applications.

scholarly journals Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

2021 ◽  
Author(s):  
Nirmal Kumar Katiyar ◽  
Gaurav Goel ◽  
Saurav Goel
Keyword(s):  
Machine Learning ◽  
High Entropy Alloy ◽  
High Entropy Alloys ◽  
Learning Approaches ◽  
Metallic Elements ◽  
Materials Synthesis ◽  
High Entropy ◽  
Structural Applications ◽  
Electromagnetic Shield ◽  
The Right

AbstractThe high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications.

Thermal Stability and Performance of NbSiTaTiZr High-Entropy Alloy Barrier for Copper Metallization

2011 ◽  
Vol 158 (11) ◽  
pp. H1161 ◽  
Author(s):  
Ming-Hung Tsai ◽  
Chun-Wen Wang ◽  
Che-Wei Tsai ◽  
Wan-Jui Shen ◽  
Jien-Wei Yeh ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Copper Metallization ◽  
High Entropy Alloy ◽  
High Entropy ◽  
And Performance
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