Introduction

Kristof T. Schütt; Stefan Chmiela; O. Anatole von Lilienfeld; Alexandre Tkatchenko; Koji Tsuda; Klaus Robert Müller

doi:10.1007/978-3-030-40245-7_1

Introduction

Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 Citations (Scopus)

Abstract

Rational design of molecules and materials with desired properties requires both the ability to calculate accurate microscopic properties, such as energies, forces, and electrostatic multipoles of specific configurations, and efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. The tools that provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Both of these come with a high computational cost that prohibits calculations for large systems or sampling-intensive applications, like long-timescale molecular dynamics simulations, thus presenting a severe bottleneck for searching the vast chemical compound space. To overcome this challenge, there have been increased efforts to accelerate quantum calculations with machine learning (ML).

Original language	English
Title of host publication	Lecture Notes in Physics
Publisher	Springer
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-030-40245-7_1
Publication status	Published - 2020

Publication series

Name	Lecture Notes in Physics
Volume	968
ISSN (Print)	0075-8450
ISSN (Electronic)	1616-6361

Bibliographical note

Funding Information:
All editors gratefully acknowledge support by the Institute of Pure and Applied Mathematics (IPAM) at the University of California Los Angeles during the long program on Understanding Many-Particle Systems with Machine Learning.

Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus subject areas

Physics and Astronomy (miscellaneous)

Access to Document

10.1007/978-3-030-40245-7_1

Cite this

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AU - von Lilienfeld, O. Anatole

AU - Tkatchenko, Alexandre

AU - Tsuda, Koji

AU - Müller, Klaus Robert

N1 - Funding Information: All editors gratefully acknowledge support by the Institute of Pure and Applied Mathematics (IPAM) at the University of California Los Angeles during the long program on Understanding Many-Particle Systems with Machine Learning. Publisher Copyright: © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Introduction

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