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Springer Anharmonic Vibration & Dielectric Properties - Springer Theses

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Description

This Springer Theses volume combines first-principles computational methods with machine learning approaches to investigate anharmonic vibrations and dielectric properties in materials. The work bridges theoretical physics and artificial intelligence, offering researchers and graduate students a comprehensive exploration of how quantum mechanical simulations can be enhanced through data-driven machine learning techniques. The study examines the complex interplay between atomic vibrations and electronic behaviour, providing insights into material properties critical for semiconductor development, photovoltaic technology, and advanced materials science. By integrating both computational methodologies, this thesis presents novel approaches to understanding lattice dynamics and polarisation phenomena at the molecular level, making it essential reading for those pursuing advanced studies in condensed matter physics, materials science, or computational chemistry.

Key Features

Springer Theses publication combines first-principles calculations with machine learning methods for materials research.

Explores anharmonic vibration phenomena and dielectric properties using quantum mechanical simulation techniques.

Integrates data-driven machine learning with traditional computational physics for enhanced material analysis.

Provides graduate-level content for researchers in condensed matter physics and advanced materials science.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 May 2025
Listed Since
26 January 2025

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No barcode data available

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