Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning

• Published in: Acta numerica Vol. 32; pp. 517 - 673
• Main Authors: Schütte, Christof, Klus, Stefan, Hartmann, Carsten
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.05.2023
• Subjects: Direct numerical simulation; Dynamic systems theory; Dynamical systems; Machine learning; Mathematical analysis; Molecular dynamics; Numerical methods; Operators (mathematics); System theory; Time; Variational principles; 37M10; 37M25; 60J60; 82M37; 82C31; 60J35; 47D07
• ISSN: 0962-4929; 1474-0508
• DOI: 10.1017/S0962492923000016

One of the main challenges in molecular dynamics is overcoming the ‘timescale barrier’: in many realistic molecular systems, biologically important rare transitions occur on timescales that are not accessible to direct numerical simulation, even on the largest or specifically dedicated supercomputers. This article discusses how to circumvent the timescale barrier by a collection of transfer operator-based techniques that have emerged from dynamical systems theory, numerical mathematics and machine learning over the last two decades. We will focus on how transfer operators can be used to approximate the dynamical behaviour on long timescales, review the introduction of this approach into molecular dynamics, and outline the respective theory, as well as the algorithmic development, from the early numerics-based methods, via variational reformulations, to modern data-based techniques utilizing and improving concepts from machine learning. Furthermore, its relation to rare event simulation techniques will be explained, revealing a broad equivalence of variational principles for long-time quantities in molecular dynamics. The article will mainly take a mathematical perspective and will leave the application to real-world molecular systems to the more than 1000 research articles already written on this subject.