TY - CHAP

T1 - Introduction

AU - Schütt, Kristof T.

AU - Chmiela, Stefan

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.

PY - 2020

Y1 - 2020

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=85086099251&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-40245-7_1

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

M3 - Chapter

AN - SCOPUS:85086099251

T3 - Lecture Notes in Physics

SP - 1

EP - 4

BT - Lecture Notes in Physics

PB - Springer

ER -