Produktbild: Machine Learning in Molecular Sciences
Band 36

Machine Learning in Molecular Sciences

Fr. 264.00

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.10.2024

Herausgeber

Chen Qu + weitere

Verlag

Springer

Seitenzahl

317

Maße (L/B/H)

23.5/15.5/1.8 cm

Gewicht

499 g

Sprache

Englisch

ISBN

978-3-031-37198-1

Beschreibung

Portrait

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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.10.2024

Herausgeber

Verlag

Springer

Seitenzahl

317

Maße (L/B/H)

23.5/15.5/1.8 cm

Gewicht

499 g

Sprache

Englisch

ISBN

978-3-031-37198-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

  • Produktbild: Machine Learning in Molecular Sciences
  • 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.