Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys

He, Song; Wang, Yanming; Zhang, Zhengyang; Xiao, Fei; Zuo, Shungui; Zhou, Ying; Cai, Xiushan; Jin, Xianglin

doi:10.1016/j.matdes.2022.111513

Cited by 23 publications

(5 citation statements)

References 53 publications

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“…Five widely applied ML algorithms to predict material properties, ,, Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), RF, Gradient Boosting Decision Trees (GBDT), and Extra Trees (ET), were utilized for modeling in this paper. The original data were partitioned into training and test sets, and the effect of various splitting ratios (testing set ranging from 10 to 50% of the original data) on the predictive applicability of the model was compared.…”

Section: Methodsmentioning

confidence: 99%

“…Li et al reduced the prediction error of hardness of the AlCoCrCuFeNi system effectively by optimizing the genetic algorithm of ML. Currently, the applications of ML are predominantly emphasized on the hardness and phase structure prediction in bulk HEAs, − with limited research on the properties of HEN coatings, which are significantly impacted by the diverse parameters of the preparation technique.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings

Zhang,

Jia,

Zeng

et al. 2024

ACS Appl. Mater. Interfaces

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Limited by the inefficiency of the conventional trialand-error method and the boundless compositional design space of high-entropy alloys (HEAs), accelerating the discovery of superiorperforming high-entropy nitride (HEN) coatings remains a formidable challenge. Herein, the superhard HEN coatings were designed and prepared using the rapidly developing data-driven model machine learning (ML). A database containing hardness and different features of HEN coatings was established and categorized into four subsets covering the information on composition, composition−physical descriptors, composition− technique parameters, and composition−physical descriptors− technique parameters. Feature engineering was employed to reduce dimensionality and interpret the impact of features on the evolution of hardness. Both root mean squared error (RMSE) and decision coefficient (R 2 ) were applied to assess the predictive accuracy of ML models with different subsets, proportions of test set, and algorithms. The model with best predicted performance was used to explore superhard HEN coatings in a predefined virtual space. Among the generated 5-/6-/7-/8-component (excluding N) systems, the coating possessing highest hardness was individually selected for further preparation. Four newly prepared coatings achieved the superhard level with an average prediction error of 7.83%. The morphology, chemical composition, structure, and hardness of the newly prepared coatings were discussed. The nanocrystal−amorphous nanocomposite structure of the novel AlCrNbSiTiN coating with the highest hardness of 45.77 GPa was revealed. The results demonstrated that ML can effectively guide the design and composition optimization of superb-performance protective HEN coatings.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings

Zhang,

Jia,

Zeng

et al. 2024

ACS Appl. Mater. Interfaces

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“…Several machine learning applications are implemented as a workflow, which starts with data collection and ends with a model evaluation and simulations or software development. Examples of fields that introduce custom machine learning workflow solutions include, but are not limited to, malware detection and classification [1], software development with adversarial attack classification [2], task fault prediction in workflows developed with cloud services [3], pipeline optimization [4], the classification of forest stand species via remote sensing [5], the detection of mechanical discontinuities in materials and the prediction of martensitic transformation peak temperature of alloys [6,7], the optimization of metabolic pathways and ranking of miRNAs retarding insulin gene transcription in human islets [8,9], large-scale crop yield forecasting [10], classification and forecasting in chemical Eng 2024, 5 includes models and algorithms, as well as training, validation, and evaluation methods. Section 5 analyzes the model deployment step, Section 6 presents the state-of-the-art automation methods in machine learning workflows and related coding practices, and Section 8 provides conclusions.…”

Section: Introductionmentioning

confidence: 99%

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Kampezidou,

Tikayat Ray,

Bhat

et al. 2024

Eng

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This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.

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“…machine learning (ML) has increasingly found applications in materials science research, providing a novel approach to tackling various problems and challenges in the field. Currently, ML has successfully predicted and designed various materials like amorphous alloys, [16] high entropy alloys, [17,18] ceramics, [19] shape memory alloys, [20] catalytic materials, [21] perovskite photovoltaics, [22] and more. Furthermore, by establishing connections between material composition and performance, ML can effectively streamline the material design process.…”

Section: Introductionmentioning

confidence: 99%

Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization

Jin,

Luo,

Wang

et al. 2024

Materials Genome Engineering Advances

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This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm (GA) for the rapid screening of hardness in the AlCoCrCuFeMoNiTi high‐entropy alloys system. Through feature engineering and modeling, valence electron concentration, atomic size difference (δr), Pauling electronegativity difference (Δχ), geometric parameters (Λ), and the Cr content were identified as the five key features in the database. The GA was employed to search for alloys with superior hardness and guided synthesis. After three iterations, the HEA Al18Co21Cr23Fe23Mo15 exhibiting the highest predicted hardness (868.8 HV) was identified. The alloy was predominantly composed of BCC, ordered B2, and σ phases, with an experimental hardness of 899.8 ± 9.9 HV, which as approximately 5.38% greater than the maximum hardness observed in the original dataset. The design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.

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Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys

Cited by 23 publications

References 53 publications

Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings

Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization

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