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Springer Machine Learning in Molecular Sciences - Springer Volume 36

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Description

Machine Learning in Molecular Sciences is the 36th volume in Springer's Challenges and Advances in Computational Chemistry and Physics series, offering a comprehensive exploration of how machine learning techniques transform molecular research and computational modelling. This scholarly work bridges the gap between machine learning methodologies and their practical applications in chemistry and physics, providing researchers, scientists, and students with essential insights into algorithmic approaches for molecular prediction, property estimation, and reaction optimisation. The volume addresses both foundational concepts and cutting-edge developments, making it valuable for those seeking to understand the intersection of artificial intelligence and molecular science. Published by Springer, a leading academic publisher, this book combines rigorous scientific content with accessibility for professionals across computational chemistry, materials science, pharmaceutical development, and related fields who want to leverage machine learning for molecular discovery and innovation.

Key Features

Part of Springer's established Challenges and Advances in Computational Chemistry and Physics series, volume 36, ensuring academic credibility and peer-reviewed content.

Covers machine learning applications directly relevant to molecular sciences, bridging computational techniques and chemical research methodologies.

Suitable for researchers, academics, and professionals in computational chemistry, physics, materials science, and related molecular disciplines.

Published by Springer, providing access to high-quality, scientifically rigorous material from experienced contributors in the field.

Addresses both fundamental machine learning concepts and advanced applications in molecular modelling and prediction.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
03 October 2024
Listed Since
04 October 2024

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