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Main Title: Molecular Dynamics with Neural Network Potentials
Author(s): Gastegger, Michael
Marquetand, Philipp
Type: Book Part
Language Code: en
Abstract: Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine learning techniques can help to overcome these limitations by providing access to potential energies, forces, and other molecular properties modeled directly after an accurate electronic structure reference at only a fraction of the original computational cost. The present text discusses several practical aspects of conducting machine learning driven molecular dynamics simulations. First, we study the efficient selection of reference data points on the basis of an active learning inspired adaptive sampling scheme. This is followed by the analysis of a machine learning based model for simulating molecular dipole moments in the framework of predicting infrared spectra via molecular dynamics simulations. Finally, we show that machine learning models can offer valuable aid in understanding chemical systems beyond a simple prediction of quantities.
Issue Date: 4-Jun-2020
Date Available: 19-Jun-2020
DDC Class: 541 Physikalische Chemie
006 Spezielle Computerverfahren
Subject(s): machine learning
molecular dynamics
neural network potentials
active learning
Sponsor/Funder: EC/H2020/792572/EU/Machine Learning for Catalytic Carbon Dioxide Activation/MachineCat
Book Title: Machine Learning Meets Quantum Physics
Editor: Schütt, Kristof T.
Chmiela, Stefan
von Lilienfeld, Anatole
Tkatchenko, Alexandre
Tsuda, Koji
Müller, Klaus-Robert
Publisher: Springer
Publisher Place: Cham
Publisher DOI: 10.1007/978-3-030-40245-7_12
Page Start: 233
Page End: 252
Series: Lecture Notes in Physics
Series Number: 968
EISSN: 1616-6361
ISBN: 978-3-030-40245-7
ISSN: 0075-8450
Appears in Collections:FG Maschinelles Lernen » Publications

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