Molecular Networking Statistical Mechanics in the Age of Ai and Machine Learning

The book builds on an analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, machine learning and data science. Applications to systems at the cutting-edge of research e.g. environmental and energy applications, will be used.

Info autore

Dr. Caroline Desgranges received a DEA in Physics in 2005 from the University Paul Sabatier-Toulouse III (France) and a PhD in Chemical Engineering from the University of South Carolina (USA) in 2008. She is currently a Research Assistant Professor in Physics & Applied Physics at the University of Massachusetts Lowell.
Dr. Jerome Delhommelle did his undergraduate studies at the Ecole Normale Superieure Paris-Saclay and received his PhD in Chemistry from the University of Paris-Saclay (France) in 2000. He is currently an Associate Professor in Chemistry at the University of Massachusetts Lowell.

Riassunto

The book builds on an analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, machine learning and data science. Applications to systems at the cutting-edge of research e.g. environmental and energy applications, will be used.

Dettagli sul prodotto

• Autori: Jerome Delhommelle, Caroline Desgranges, Jerome (University of Dakota Delhommelle, Caroline (Research Assistant Professor Desgranges
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Copertina rigida
• Data pubblicazione: 31.01.2024
• Categoria: Scienze naturali, medicina, informatica, tecnica > Chimica > Chimica teorica
• EAN: 9780367438937
• ISBN: 978-0-367-43893-7
• Numero di pagine: 236
• Categorie: Artificial Intelligence, SCIENCE / Physics / General, SCIENCE / Physics / Atomic & Molecular, SCIENCE / Chemistry / Physical & Theoretical, Atomic & molecular physics, Quantum & theoretical chemistry, Applied physics, Statistical physics, COMPUTERS / Artificial Intelligence / General, Computational Chemistry, Atomic and Molecular Physics, Quantum and theoretical chemistry, thermodynamic modeling, molecular simulation techniques, quantum interactions, machine learning for molecular systems, active matter dynamics, neural network potentials