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Molecular dynamics with the stochastic process provides a convenient way to compute structural and thermodynamic properties of chemical, biological, and materials systems. It is demonstrated that the virtual dynamics case that we proposed…

Statistical Mechanics · Physics 2018-03-20 Dezhang Li , Zifei Chen , Zhijun Zhang , Jian Liu

A systematic comparison was carried out to assess the influence of representative thermostat methods in constant-temperature molecular dynamics simulations. The thermostat schemes considered include the Nos\'e--Hoover thermostat and its…

Computational Physics · Physics 2026-04-24 Kumpei Shiraishi , Emi Minamitani , Kang Kim

Recently, we have shown how a colored-noise Langevin equation can be used in the context of molecular dynamics as a tool to obtain dynamical trajectories whose properties are tailored to display desired sampling features. In the present…

Computational Physics · Physics 2012-04-05 Michele Ceriotti , Giovanni Bussi , Michele Parrinello

We show a unified second-order scheme for constructing simple, robust and accurate algorithms for typical thermostats for configurational sampling for the canonical ensemble. When Langevin dynamics is used, the scheme leads to the BAOAB…

Chemical Physics · Physics 2017-10-17 Zhijun Zhang , Xinzijian Liu , Zifei Chen , Haifeng Zheng , Kangyu Yan , Jian Liu

Stochastic thermostats based on the Langevin equation, in which a system is coupled to an external heat bath, are popular methods for temperature control in molecular dynamics simulations due to their ergodicity and their ease of…

Chemical Physics · Physics 2018-05-23 Mahdi Hijazi , David M. Wilkins , Michele Ceriotti

Generalized Langevin Equation (GLE) thermostats have been used very effectively as a tool to manipulate and optimize the sampling of thermodynamic ensembles and the associated static properties. Here we show that a similar, exquisite level…

Chemical Physics · Physics 2017-09-06 Mariana Rossi , Venkat Kapil , Michele Ceriotti

Reliable dynamical properties from molecular dynamics simulations require careful control of thermostatting artifacts. We systematically assess how NVE, deterministic thermostats, velocity-rescale dynamics, and stochastic Langevin-type…

Chemical Physics · Physics 2025-12-01 Frederick Heinz , Sascha Jähnigen , Joana-Lysiane Schäfer , Bettina G. Keller

We present a new and improved method for simultaneous control of temperature and pressure in molecular dynamics simulations with periodic boundary conditions. The thermostat-barostat equations are build on our previously developed…

Statistical Mechanics · Physics 2014-11-20 Niels Grønbech-Jensen , Oded Farago

We study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods are discussed which have application to…

Numerical Analysis · Mathematics 2016-03-08 Benedict Leimkuhler , Xiaocheng Shang

The path integral molecular dynamics (PIMD) method provides a convenient way to compute the quantum mechanical structural and thermodynamic properties of condensed phase systems at the expense of introducing an additional set of…

Statistical Mechanics · Physics 2010-09-29 Michele Ceriotti , Michele Parrinello , Thomas E. Markland , David E. Manolopoulos

Diffusion maps approximate the generator of Langevin dynamics from simulation data. They afford a means of identifying the slowly-evolving principal modes of high-dimensional molecular systems. When combined with a biasing mechanism,…

Data Analysis, Statistics and Probability · Physics 2020-07-01 Zofia Trstanova , Ben Leimkuhler , Tony Lelièvre

The Langevin equation accounts for unresolved bath degrees of freedom driving the system toward the bath temperature. Because of this, numerical solutions of the Langevin equation have a long history. Here, we recapitulate, combine, and…

Statistical Mechanics · Physics 2025-06-19 Shubham Agarwal , Sergey V. Sukhomlinov , Marc Honecker , Martin H. Müser

Monte Carlo sampling for Bayesian posterior inference is a common approach used in machine learning. The Markov Chain Monte Carlo procedures that are used are often discrete-time analogues of associated stochastic differential equations…

Machine Learning · Statistics 2020-02-14 Xiaocheng Shang , Zhanxing Zhu , Benedict Leimkuhler , Amos J. Storkey

In light of the recently developed complete GJ set of single random variable stochastic, discrete-time St{\o}rmer-Verlet algorithms for statistically accurate simulations of Langevin equations, we investigate two outstanding questions: 1)…

Statistical Mechanics · Physics 2020-10-05 Joshua Finkelstein , Chungho Cheng , Giacomo Fiorin , Benjamin Seibold , Niels Grønbech-Jensen

We reformulate the algorithm of Gr{\o}nbech-Jensen and Farago (GJF) for Langevin dynamics simulations at constant temperature. The GJF algorithm has become increasingly popular in molecular dynamics simulations because it provides robust…

Statistical Mechanics · Physics 2019-08-14 Oded Farago

Non-Markovian stochastic Langevin-like equations of motion are compared to their corresponding Markovian (local) approximations. The validity of the local approximation for these equations, when contrasted with the fully nonlocal ones, is…

Statistical Mechanics · Physics 2009-12-23 R. L. S. Farias , Rudnei O. Ramos , L. A. da Silva

We recently showed that the dynamics of coarse-grained observables in systems out of thermal equilibrium are governed by the non-stationary generalized Langevin equation [J. Chem. Phys. 147, 214110 (2017), J. Chem. Phys. 150, 174118…

Statistical Mechanics · Physics 2021-05-21 Fabian Glatzel , Tanja Schilling

When modeling charge dynamics in a chain of N sites at a temperature T, a Langevin thermostat and a Hamiltonian system, i.e., a chain heated to a given temperature before charge is injected, are compared. It is shown that the polaron…

Statistical Mechanics · Physics 2023-10-10 N. Fialko , M. Olshevets , V. D. Lakhno

The applicability of stochastic differential equations to thermodynamics is considered and a new form, different from the classical Ito and Stratonovich forms, is introduced. It is shown that the new presentation is more appropriate for the…

Statistical Mechanics · Physics 2015-06-05 R. Tsekov

We consider stochastic thermodynamics as a theory of statistical inference for experimentally observed fluctuating time-series. To that end, we introduce a general framework for quantifying the knowledge about the dynamical state of the…

Statistical Mechanics · Physics 2015-05-19 Bernhard Altaner , Jürgen Vollmer
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