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Springer Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

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£108.01 £106.16 £106.57 £106.97 £107.37 £107.77 £108.18 27 July 2026 13 August 2026 31 August 2026 17 September 2026 05 October 2026
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Description

This Springer Theses volume explores the intersection of machine learning and spectroscopic analysis for advanced materials research. The work presents novel methodologies that combine computational intelligence with spectroscopic techniques to accelerate materials discovery and optimisation. Readers will gain insight into how machine learning algorithms can process complex spectroscopic data to identify patterns and predict material properties without extensive experimental iteration. The book addresses real-world applications in materials science, demonstrating how AI-driven approaches reduce development time and costs whilst improving accuracy in material characterisation. Essential for researchers, PhD students and materials scientists seeking to integrate machine learning tools into their spectroscopic workflows and advance their understanding of intelligent materials design.

Key Features

Springer Theses publication providing peer-reviewed research on machine learning applications in spectroscopy and materials analysis.

Covers integration of machine learning algorithms with spectroscopic techniques for enhanced materials characterisation and property prediction.

Explores methodologies for accelerating materials discovery through computational intelligence and data-driven analysis.

Targets researchers and PhD candidates working in materials science, computational chemistry and advanced manufacturing sectors.

Presents practical frameworks for applying AI-augmented spectroscopies to real-world materials design challenges.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
08 October 2023
Listed Since
10 September 2023

Barcode

No barcode data available

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