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Journal ArticleDOI

Machine learning assisted design of high entropy alloys with desired property

TLDR
In this article, a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system was proposed.
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This article is published in Acta Materialia.The article was published on 2019-05-15. It has received 387 citations till now. The article focuses on the topics: High entropy alloys & Active learning (machine learning).

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Citations
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Journal ArticleDOI

Mechanical behavior of high-entropy alloys

TL;DR: In this article, the authors present a comprehensive, critical review of the mechanical behavior of high-entropy alloys and some closely related topics, including thermodynamics and kinetics.
Journal ArticleDOI

Data-Driven Materials Science: Status, Challenges, and Perspectives

TL;DR: In this article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data frastructures are discussed.
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Machine learning in additive manufacturing: State-of-the-art and perspectives

TL;DR: A comprehensive review on the state-of-the-art of ML applications in a variety of additive manufacturing domains can be found in this paper, where the authors provide a section summarizing the main findings from the literature and provide perspectives on some selected interesting applications.
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Multicomponent high-entropy Cantor alloys

TL;DR: A review of multicomponent high-entropy Cantor alloys can be found in this paper, where the authors describe the extensive range and complexity of multic-component phase space, including the prevalence of single (or relatively few) phases and the paucity of intrinsically new multic-component compounds.
Journal ArticleDOI

Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

TL;DR: In this paper, a genetic algorithm was used to select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs).
References
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Journal ArticleDOI

Efficient Global Optimization of Expensive Black-Box Functions

TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
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Microstructural development in equiatomic multicomponent alloys

TL;DR: In this paper, it was shown that the confusion principle does not apply, and other factors are more important in promoting glass formation of late transition metal rich multicomponent alloys.
Journal ArticleDOI

A critical review of high entropy alloys and related concepts

TL;DR: High entropy alloys (HEAs) are barely 12 years old as discussed by the authors, and the field has stimulated new ideas and inspired the exploration of the vast composition space offered by multi-principal element alloys.
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A fracture-resistant high-entropy alloy for cryogenic applications

TL;DR: This work examined a five-element high-entropy alloy, CrMnFeCoNi, which forms a single-phase face-centered cubic solid solution, and found it to have exceptional damage tolerance with tensile strengths above 1 GPa and fracture toughness values exceeding 200 MPa·m1/2.
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