£127.59

Springer Artificial Intelligence for Materials Science 312

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Description

Explore the transformative impact of machine learning on materials science and engineering. This volume from the Springer Series in Materials Science (312) examines how machine learning methods have reduced the costs associated with exploring new structures of unknown compounds. By predicting reasonable expectations that can be validated through experimental results, these computational tools are changing the way researchers approach material discovery. As new insights and elaborative tools emerge, this book provides a timely look at recent progress in the field. It covers the development of searchable and interactive databases that promote research on emerging materials. With the rise of databases containing high-quality properties for advanced materials, this text serves as a necessary resource for understanding the modern landscape of materials research and the role of artificial intelligence in accelerating scientific breakthroughs.

Key Features

Learn how machine learning methods lower the costs of exploring new structures for unknown compounds.

Understand how to use predictive models to establish expectations that are later validated by experimental results.

Explore recent progress and new insights developed for the fields of materials science and engineering.

Gain knowledge on how searchable and interactive databases promote research into emerging materials.

Study the impact of high-quality property databases on the development of new advanced materials.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
27 March 2021
Listed Since
19 December 2020

Barcode

No barcode data available

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