Multicomponent alloys design and mechanical response: From high entropy alloys to complex concentrated alloys

Materials Science and Engineering R Reports, Journal Year: 2024, Volume and Issue: 161, P. 100853 - 100853

Published: Sept. 11, 2024

Language: Английский

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Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(7), P. 076014 - 076014

Published: June 10, 2024

Abstract High-entropy alloys (HEAs) are increasingly renowned for their distinct microstructural compositions and exceptional properties. These HEAs employed surface modification as coatings exhibit phenomenal mechanical characteristics including wear corrosion resistance which extensively utilized in various industrial applications. However, assessing the behaviour of HEA through conventional methods remains challenging time-consuming due to complexity structures. In this study, a novel methodology has been proposed predicting using Machine Learning (ML) algorithms such Support Vector (SVM), Linear Regression (LR), Gaussian Process (GPR), Least Absolute Shrinkage Selection Operator (LASSO), Bagging (BR), Gradient Boosting Tree (GBRT), Robust regressions (RR). The analysis integrates 75 combinations with processing parameters test results from peer-reviewed journals model training validation. Among ML models utilized, GBRT was found be more effective rate Coefficient Friction (COF) highest correlation coefficient R 2 value 0.95 ∼ 0.97 minimal errors. optimum is used predict unknown properties conducted experiments validate results, making crucial resource engineers materials sector.

Language: Английский

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Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3368 - 3368

Published: Nov. 29, 2024

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep

Language: Английский

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BaTiO3-based high-entropy ceramics for enhanced capacitive energy storage performance

Ceramics International, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Batch active learning for microstructure-property relations in energetic materials

Mechanics of Materials, Journal Year: 2025, Volume and Issue: unknown, P. 105308 - 105308

Published: March 1, 2025

Language: Английский

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High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys

Materials Genome Engineering Advances, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Abstract The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data‐driven material based on machine learning (ML) has emerged as critical tool accelerating materials design. However, absence of robust datasets impedes exploitation in novel RMPEAs. High‐throughput (HTP) calculations have enabled creation such datasets. This study addresses these challenges by developing framework predicting elastic properties RMPEAs, integrating HTP with ML. A big dataset RMPEAs including 4536 compositions was constructed using new proposed method. stacking ensemble regression algorithm combining multilayer perceptron (MLP) gradient boosting decision tree (GBDT) developed, which achieved 92.9% accuracy Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed ML model's robustness. integration provides cost‐effective, efficient, precise alloy strategy, advancing development.

Language: Английский

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Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models

Applied Physics Letters, Journal Year: 2025, Volume and Issue: 126(11)

Published: March 1, 2025

The inverse design of solid-state materials with targeted properties represents a significant challenge in science, particularly for piezoelectric semiconductors where both structural symmetry and electronic must be carefully controlled. Here, we employ the simplified line-input crystal-encoding system representation combined MatterGPT framework discovering potential semiconductors. By training on curated dataset 1556 from Materials Project database, our model learns to generate crystal structures through an autoregressive sampling process. Starting approximately 5000 generated structures, implemented comprehensive screening workflow incorporating validity, thermodynamic stability, property verification. This approach identified several promising candidates 4100 reconstructed each representing compounds unrecorded existing databases. Among these, most notable material demonstrated stress coefficient 25.9 C/m2 e[1,6] direction. Additionally, these demonstrate suitable bandgaps ranging 1.63 3.61 eV, suggesting applications high-sensitivity sensors high-temperature electronics. Our work demonstrates effectiveness combining structure language encoding generative models accelerating discovery functional properties.

Language: Английский

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Prediction of mechanical properties of high entropy alloys based on machine learning

Physica Scripta, Journal Year: 2025, Volume and Issue: 100(4), P. 046013 - 046013

Published: March 5, 2025

Abstract In recent years, the ideal- properties (young’s modulus, yield strength, toughness) and advanced application potential of high-entropy alloys (HEAs) have attracted numerous researchers. However, due to their unique structure multiple structural combinations, it is challenging explore impact various factors on mechanical performance solely through experiments. This study considers concentrations five alloy atoms working temperature as input parameters. Molecular dynamics (MD) simulations machine learning (ML) algorithms are employed predict tensile FeNiCrCoCu HEAs, including Young’s modulus ( E ) toughness uT ). A dataset 1000 HEAs generated MD simulations, feature selection conducted using principal component analysis Spearman correlation analysis. XGBoost, RF, DT, LGBoost, AdaBoost utilized comparing two methods prediction outcomes. During ML model training, 10-fold cross-validation grid search obtain best models Root mean squard error RMSE ), coefficient determination R 2 absolute MAE relative RAE used evaluation metrics. Results indicate that for outperforms analysis, XGBoost demonstrates superior predictive compared other models. Predictions more accurate than those , with exceeding 0.9 four out work may provide a new method studying ML. future, this can be applied research areas compositions, providing theoretical support It then further critical fields such biomedical aerospace industries.

Language: Английский

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AI-driven optimisation of metal alloys for space applications

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: April 12, 2025

Language: Английский

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Microstructural Characterization and Hardness Prediction of Alcucrfeni High Entropy Alloys Using Transformer-Based Physics-Informed Neural Networks (T-Pinn)

Published: Jan. 1, 2025

Language: Английский

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