scispace - formally typeset
Journal ArticleDOI

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

Reads0
Chats0
TLDR
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).
About
This article is published in Acta Materialia.The article was published on 2020-02-15. It has received 188 citations till now. The article focuses on the topics: Materials informatics & Active learning (machine learning).

read more

Citations
More filters
Journal Article

Big Data of Materials Science -- Critical Role of the Descriptor

TL;DR: A trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful when the scientific connection between the descriptor and the actuating mechanisms is unclear.
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

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

High-entropy energy materials: challenges and new opportunities

TL;DR: A comprehensive review of high-entropy materials in the energy field, including alloys, oxides and other entropy-stabilized compounds and composites, in various energy storage and conversion systems.
References
More filters
Proceedings Article

A study of cross-validation and bootstrap for accuracy estimation and model selection

TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
Journal ArticleDOI

No free lunch theorems for optimization

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Journal ArticleDOI

Machine learning for molecular and materials science.

TL;DR: A future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence is envisaged.
Journal ArticleDOI

Solid‐Solution Phase Formation Rules for Multi‐component Alloys

TL;DR: In this article, the factors of the atomic size difference Delta and the enthalpy of mixing ΔH mιx of the multi-component alloys were summarized from the literatures.
Journal ArticleDOI

On representing chemical environments

TL;DR: It is demonstrated that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighborhood density function.
Related Papers (5)