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Hydrothermal Synthesis and Characterization of Mixed Fluoride Based Nanophosphors Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Reviewing the Novel Machine Learning Tools for Materials Design

Authors : Amir Mosavi, Timon Rabczuk, Annamária R. Varkonyi-Koczy

Published in: Recent Advances in Technology Research and Education

Publisher: Springer International Publishing

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Abstract

Computational materials design is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. Today the latest advancements in machine learning, deep learning, internet of things (IoT), big data, and intelligent optimization have highly revolutionized the computational methodologies used for materials design innovation. Such novelties in computation enable the development of problem-specific solvers with vast potential applications in industry and business. This paper reviews the state of the art of technological advancements that machine learning tools, in particular, have brought for materials design innovation. Further via presenting a case study the potential of such novel computational tools are discussed for the virtual design and simulation of innovative materials in modeling the fundamental properties and behavior of a wide range of multi-scale materials design problems.

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Metadata
Title
Reviewing the Novel Machine Learning Tools for Materials Design
Authors
Amir Mosavi
Timon Rabczuk
Annamária R. Varkonyi-Koczy
Copyright Year
2018
Book
Recent Advances in Technology Research and Education

Print ISBN: 978-3-319-67458-2

Electronic ISBN: 978-3-319-67459-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67459-9_7

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