Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning

Pan, Shangke; Wang, Yongjie; Yu, Jinxin; Yang, Mujin; Zhang, Yanqing; Wei, Haiting; Chen, Yuechao; Wu, Junwei; Han, Jiajia; Wang, Cuiping; Liu, Xingjun

doi:10.1016/j.matdes.2021.109929

Cited by 37 publications

(14 citation statements)

References 69 publications

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“…Generally, supervised learning is preferred and thus used in classification or regression problems, encompassing support vector machine algorithms, Naive Bayes classifiers, decision trees, neighbor K-nearest algorithms, and artificial neural networks (ANNs) [145,146] . Among the various types of learning algorithms, artificial neural networks are widely used in material manufacturing processes due to their ability to overcome the limitations imposed by nonlinearities and the multiple parameters involved in computing material design problems [147][148][149][150] .…”

Section: Machine Learning Toward Materials-property Relationmentioning

confidence: 99%

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.

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Section: Machine Learning Toward Materials-property Relationmentioning

confidence: 99%

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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“…The most recent application of a ML-based framework has led to the successful design of a series of novel W-free high-strength Co-V-Ta-based alloys. The designed alloys all exhibit a unique combination of low mass densities (8.67-8.86 g/cm 3 ) and high γ′ solvus temperatures (up to 1044 °C) and strength, which is more intriguing compared with that of traditional and novel γ′-reinforced Co-based superalloys (as shown in Figure 10) [17] .…”

Section: Optimization Of Novel Co-based Superalloysmentioning

confidence: 99%

“…Figure 10. Temperature dependence of yield strengths of designed Co-V-Ta-based alloys (CVT, CVTT and CVTA) [17] . For comparison, the yield strengths of the traditional Co-and commercial Ni-based superalloys and newly developed Co-based alloys are plotted in (A) and (B).…”

Section: Development Of Ti Alloys For Biomedical Applicationsmentioning

confidence: 99%

“…To date, two active and highly related research areas in the application of ML in materials science and engineering are property predictions and materials discovery and design [10][11][12][13][15][16][17][18][19][20][21][22][23][24][25][26][27][28] . As such, ML algorithms involved in property prediction naturally fall in the category of supervised learning, in view of the nature of the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-guided design and development of metallic structural materials

Yu¹,

Xi²,

Pan³

et al. 2021

JMI

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In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor.

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“…Li-Si-(Mn,Fe,Co)-O bileşimlerine sahip katot malzemeleri, Li-iyon bataryalardaki uygulamaları nedeniyle araştırmalar tarafından büyük ilgi görmektedir (Askanazi et al 2021;Pan et al 2021;Bartel et al 2020;Park and Wolverton 2020;Cubuk et al 2019). Shandiz ve Gauvin (2016), Malzeme Projesi veri setini ile ML yöntemleri olan lineer, kuadratik ve daralan diskriminant analizi, sinir ağları, destek vektör makineleri, k-en yakın komşular, rastgele ormanlar ve aşırı derecede rastgele ağaçları modelleri oluşturmada kullanmışlardır.…”

Section: Introductionunclassified

Implementation of Machine Learning Approaches for Crystal Structure Estimation in Lithium-ion Battery Cathode Materials

SÜPÜRTÜLÜ

DİNDAR²,

AKYÜZ³

2022

Afyon Kocatepe University Journal of Sciences and Engineering

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It has commonly been assumed that the physical and chemical characteristics of lithium-ion silicate cathodes are influenced significantly by the crystal structure system. Because of this, crystal structure estimation has played a vital role in bringing about forecasting many other features of cathodes in battery applications. Using a variety of classification techniques in machine learning which three primary crystal structure (monoclinic, orthorhombic, and triclinic) of silicate-based cathode materials with compound systems of Li-Si-(Mn,Fe,Co)-O has been estimated. The computations are based on the Materials Project's density functional theory computations. In this study, it has been explained that based on property evaluation in statistical models, the considerable correlation between the crystal system and other physical characteristics of the cathodes was validated. Furthermore, the parameters of several categorization techniques have been tuned in order to achieve maximum prediction accuracy. This case has shown that the XGBoost and Support Vector Classifier algorithms carried out in this study the highest forecasting accuracy in this study along with many other classification methods in cross-validation tests.

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Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning

Cited by 37 publications

References 69 publications

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Machine learning-guided design and development of metallic structural materials

Implementation of Machine Learning Approaches for Crystal Structure Estimation in Lithium-ion Battery Cathode Materials

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