1,057 research outputs found
Systematic Coarse-Grained Models for Molecular Systems Using Entropy â€
The development of systematic coarse-grained mesoscopic models for complex molecular systems is an intense research area. Here we first give an overview of different methods for obtaining optimal parametrized coarse-grained models, starting from detailed atomistic representation for high dimensional molecular systems. We focus on methods based on information theory, such as relative entropy, showing that they provide parameterizations of coarse-grained models at equilibrium by minimizing a fitting functional over a parameter space. We also connect them with structural-based (inverse Boltzmann) and force matching methods. All the methods mentioned in principle are employed to approximate a many-body potential, the (n-body) potential of mean force, describing the equilibrium distribution of coarse-grained sites observed in simulations of atomically detailed models. We also present in a mathematically consistent way the entropy and force matching methods and their equivalence, which we derive for general nonlinear coarse-graining maps. We apply, and compare, the above-described methodologies in several molecular systems: A simple fluid (methane), water and a polymer (polyethylene) bulk system. Finally, for the latter we also provide reliable confidence intervals using a statistical analysis resampling technique, the bootstrap method
Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems
Machine learning (ML) has emerged as a pervasive tool in science,
engineering, and beyond. Its success has also led to several synergies with
molecular dynamics (MD) simulations, which we use to identify and characterize
the major metastable states of molecular systems. Typically, we aim to
determine the relative stabilities of these states and how rapidly they
interchange. This information allows mechanistic descriptions of molecular
mechanisms, enables a quantitative comparison with experiments, and facilitates
their rational design. ML impacts all aspects of MD simulations -- from
analyzing the data and accelerating sampling to defining more efficient or more
accurate simulation models.Comment: 36 pages, 4 figure
Modeling of biomolecular machines in non-equilibrium steady states
Numerical computations have become a pillar of all modern quantitative
sciences. Any computation involves modeling--even if often this step is not
made explicit--and any model has to neglect details while still being
physically accurate. Equilibrium statistical mechanics guides both the
development of models and numerical methods for dynamics obeying detailed
balance. For systems driven away from thermal equilibrium such a universal
theoretical framework is missing. For a restricted class of driven systems
governed by Markov dynamics and local detailed balance, stochastic
thermodynamics has evolved to fill this gap and to provide fundamental
constraints and guiding principles. The next step is to advance stochastic
thermodynamics from simple model systems to complex systems with ten thousands
or even millions degrees of freedom. Biomolecules operating in the presence of
chemical gradients and mechanical forces are a prime example for this
challenge. In this Perspective, we give an introduction to isothermal
stochastic thermodynamics geared towards the systematic multiscale modeling of
the conformational dynamics of biomolecular and synthetic machines, and we
outline some of the open challenges.Comment: Comments are welcom
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