Recent progress in the machine learning-assisted rational design of alloys

Fu, Huadong; Zhang, Hongtao; Wang, Changsheng; Wei, Yong; Xie, Jun

doi:10.1007/s12613-022-2458-8

Cited by 28 publications

(10 citation statements)

References 58 publications

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“…Machine learning modeling aims to establish a function between input and output and makes it as close to the real function relationship as possible by optimizing the model parameters [ 50 , 51 ]. Due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material science, including the establishment of phase diagrams [ 52 ], properties prediction [ 53 ], the discovery and design of high-performance materials [ 38 , 54 ], and the exploration of strengthening and toughening mechanism [ 38 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…When the sample data are scarce due to the experimental challenges or high costs, it is difficult to establish a machine learning model with high prediction accuracy and great generalization ability using an existing small dataset. Hence, the active learning method that uses the designed experimental iterative feedback optimization method to improve the machine learning model predictions and reduce the number of required experiments has attracted attention [ 50 , 58 ]. Results of Lookman et al indicate that active learning is forgiving of poor model quality [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

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Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Gao

Zhong

Liu

et al. 2023

Science and Technology of Advanced Materials

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Scandium is the best alloying element to improve the mechanical properties of industrial Al-Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc additions in different commercial Al-Si-Mg casting alloys with well-defined compositions. However, no attempt to optimize the contents of Si, Mg, and Sc has been made due to the great challenge of simultaneous screening in high-dimensional composition space with limited experimental data. In this paper, a novel alloy design strategy was proposed and successfully applied to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys over high-dimensional composition space. Firstly, high-throughput CALculation of PHAse Diagrams (CALPHAD) solidification simulations of ocean of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide composition range were performed to establish the quantitative relation ‘composition-process-microstructure’. Secondly, the relation ‘microstructure-mechanical properties’ of Al-Si-Mg-Sc hypoeutectic casting alloys was acquired using the active learning technique supported by key experiments designed by CALPHAD and Bayesian optimization samplings. After a benchmark in A356-xSc alloys, such a strategy was utilized to design the high-performance hypoeutectic Al-xSi-yMg alloys with optimal Sc additions that were later experimentally validated. Finally, the present strategy was successfully extended to screen the optimal contents of Si, Mg, and Sc over high-dimensional hypoeutectic Al-xSi-yMg-zSc composition space. It is anticipated that the proposed strategy integrating active learning with high-throughput CALPHAD simulations and key experiments should be generally applicable to the efficient design of high-performance multi-component materials over high-dimensional composition space.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Gao

Zhong

Liu

et al. 2023

Science and Technology of Advanced Materials

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“…Matweb, established in the United States, is a freely searchable database of material properties, serving metals, plastics, ceramics, and composites industries [7] . The Materials Science International database includes alloy material composition and phase diagram data of more than 4600 systems.…”

Section: Steel Materials Databasesmentioning

confidence: 99%

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng,

Wang,

et al. 2023

Materials Genome Engineering Advances

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With the development of new information technology, big data technology and artificial intelligence (AI) have accelerated material research and development and industrial manufacturing, which have become the key technology driving a new wave of global technological revolution and industrial transformation. This review introduces the data resources and databases related to steel materials. It then examines the fundamental strategies and applications of machine learning (ML) in the design and discovery of steel materials, including ML models based on experimental data, industrial manufacturing data, and simulation data, respectively. Given the advancements in big data, AI/ML, and new communication technologies, an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution. Consequently, the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed. Furthermore, the applications of ML in service performance prediction of steel products are addressed. Finally, the future development trends for data‐driven and AI approaches throughout the entire life cycle of steel materials are prospected. Overall, this work presents an in‐depth examination of the integration of data‐driven and AI technologies in the steel industry, highlighting their potential and future directions.

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“…Additionally, physical and chemical features, the interactions and reactions of added elements, and how elements affect mechanical properties cannot be interpreted by employing chemical compositions as input variables and by establishing a phenomenological model. 41 However, the previously mentioned problem can be addressed by employing alloy features that transform from chemical compositions to input variables. The applicable alloy features machine learning method has been successfully used to design new alloy.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, machine learning method is a potential approach to establish one model for predicting low cycle fatigue life in different types of austenitic stainless steels at various elevated temperatures. Additionally, physical and chemical features, the interactions and reactions of added elements, and how elements affect mechanical properties cannot be interpreted by employing chemical compositions as input variables and by establishing a phenomenological model 41 . However, the previously mentioned problem can be addressed by employing alloy features that transform from chemical compositions to input variables.…”

Section: Introductionmentioning

confidence: 99%

Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

Wei

et al. 2022

Fatigue Fract Eng Mat Struct

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An alloy features-based and chemical compositions-based machine learning method was used to examine the low cycle fatigue life of austenitic stainless steels at different elevated temperatures employing one model. Furthermore, eight algorithms were used to examine the impact of algorithms on the precision of constructed models. As input, physicochemical features of elements were transformed from chemical compositions. After being conducted by the feature screening process, electronegativity deviation (E2.sd), ionization energy deviation (E6.sd), testing conditions, and tensile strength were chosen as input.The results show that algorithms affect accuracy and the models with the highest accuracy are SVR and ANN for alloy features and chemical compositionsbased method, respectively. Chemical composites-based model demonstrates relatively lower precision than the alloy feature model. Almost all testing data distribute within two-factor band lines predicted by alloying feature-based model. The validation testing results indicate that 83% data plots distribute within two-factor band lines.

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Recent progress in the machine learning-assisted rational design of alloys

Cited by 28 publications

References 58 publications

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

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