Machine learning assisted design of high entropy alloys with desired property

Wen, Cheng; Zhang, Yan; Wang, Cynthia; Xue, Dezhen; Bai, Yang; Antonov, Stoichko; Dai, Lan-Hong; Lookman, Turab; Su, Yanjing

doi:10.1016/j.actamat.2019.03.010

Cited by 563 publications

(244 citation statements)

References 53 publications

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“…[576] The results in this research have revealed the successful implementation of MLDS method in assisting inverse design of 8 new high-performance copper alloys with target UTS of 600 to 950 MPa and EC over 50 pct of International Annealed Copper Standards. A second example in this area, Wen et al [577] have developed a property oriented optimization strategy to search for large hardness as a desired property in Al-Co-Cr-Cu-Fe-Ni high entropy alloys (HEA). [578] In this research they first trained a machine learning model to predict hardness of HEA using chemical composition and chemistry of elements as descriptors of the alloy from experimental data.…”

Section: Machine Learning-assisted Alloy Microstructure and Propmentioning

confidence: 99%

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Raabe

Sun

Silva

et al. 2020

Metall Mater Trans A

134

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This is a viewpoint paper on recent progress in the understanding of the microstructure–property relations of advanced high-strength steels (AHSS). These alloys constitute a class of high-strength, formable steels that are designed mainly as sheet products for the transportation sector. AHSS have often very complex and hierarchical microstructures consisting of ferrite, austenite, bainite, or martensite matrix or of duplex or even multiphase mixtures of these constituents, sometimes enriched with precipitates. This complexity makes it challenging to establish reliable and mechanism-based microstructure–property relationships. A number of excellent studies already exist about the different types of AHSS (such as dual-phase steels, complex phase steels, transformation-induced plasticity steels, twinning-induced plasticity steels, bainitic steels, quenching and partitioning steels, press hardening steels, etc.) and several overviews appeared in which their engineering features related to mechanical properties and forming were discussed. This article reviews recent progress in the understanding of microstructures and alloy design in this field, placing particular attention on the deformation and strain hardening mechanisms of Mn-containing steels that utilize complex dislocation substructures, nanoscale precipitation patterns, deformation-driven transformation, and twinning effects. Recent developments on microalloyed nanoprecipitation hardened and press hardening steels are also reviewed. Besides providing a critical discussion of their microstructures and properties, vital features such as their resistance to hydrogen embrittlement and damage formation are also evaluated. We also present latest progress in advanced characterization and modeling techniques applied to AHSS. Finally, emerging topics such as machine learning, through-process simulation, and additive manufacturing of AHSS are discussed. The aim of this viewpoint is to identify similarities in the deformation and damage mechanisms among these various types of advanced steels and to use these observations for their further development and maturation.

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Section: Machine Learning-assisted Alloy Microstructure and Propmentioning

confidence: 99%

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Raabe

Sun

Silva

et al. 2020

Metall Mater Trans A

134

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“…BO enables significant acceleration of materials design and discovery, as demonstrated by its successful application across a wide variety of materials covering alloys [217], polymers [208,218], inorganic compounds [219][220][221], and drug-like molecules [222][223][224]. A recent article by Lookman et al [225] reviews application of BO in materials science and highlights existing challenges.…”

Section: The Design Recommended By Acquisition Function Is Evaluated mentioning

confidence: 99%

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

et al. 2020

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Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

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“…While these approaches can leverage experimental data accumulated over decades, extrapolating (and even interpolating) these models outside the range of the input data is risky due to the absence of physical constraints. There have been attempts to incorporate atomistic-level features, e.g., atomic radius/volume, electronegativities, cohesive energy, and local electronegativity mismatch, for predicting high-temperature alloy properties [14][15][16][17] , but features related to phenomena/mechanisms occurring in larger length scales (i.e., micro-and meso-scale) may have more impact on alloys.…”

Section: Introductionmentioning

confidence: 99%

Coupling physics in machine learning to predict properties of high-temperatures alloys

et al. 2020

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High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

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Machine learning assisted design of high entropy alloys with desired property

Cited by 563 publications

References 53 publications

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

Coupling physics in machine learning to predict properties of high-temperatures alloys

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