Machine Learning in Molecular Sciences / / edited by Chen Qu, Hanchao Liu

• Record Nr.: UNINA9910746977103321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
• ISBN: 3-031-37196-8
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (323 pages)
• Collana: Challenges and Advances in Computational Chemistry and Physics, , 2542-4483 ; ; 36
• Disciplina: 006.31
• Soggetti: Machine learning; Artificial intelligence; Molecules - Models; Chemistry, Physical and theoretical; Chemistry - Data processing; Bioinformatics; Machine Learning; Artificial Intelligence; Molecular Modelling; Theoretical Chemistry; Computational Chemistry; Computational and Systems Biology
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: An Introduction to Machine Learning in Molecular Sciences -- Graph Neural Networks for Molecules -- Voxelized representations of atomic systems for machine learning applications -- Development of exchange-correlation functionals assisted by machine learning -- Machine-Learning for Static and Dynamic Electronic Structure Theory -- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions -- Machine Learning Applications in Chemical Kinetics and Thermochemistry -- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis ; -- Machine Learning for Protein Engineering.
• Sommario/riassunto: Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.