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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
6
74 farbige Abbildungen, 6 s/w Abbildungen
X, 317 p. 80 illus., 74 illus. in color.
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 18 mm
Gewicht
ISBN-13
978-3-031-37198-1 (9783031371981)
DOI
10.1007/978-3-031-37196-7
Schweitzer Klassifikation
Chen Qu is currently a research associate of National Institute of Standards and Technology. His current research focuses on applying machine learning methods to predict important chemical properties such as gas chromatography retention indices and mass spectra. He received his Ph.D. at Emory University, where he conducted research primarily on machine learning potential energy surfaces, under the guidance of Prof. Joel Bowman.
Hanchao Liu is currently a machine learning engineer at Google. His work focuses on building large-scale machine learning infrastructures and platforms. Dr. Liu received his Ph.D. in computational chemistry at Emory University under the tutelage of Prof. Joel Bowman, where he applied computational and machine learning methods to study the vibrational dynamics and spectra of various forms of water.
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.
