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Related papers: Sampling Free Energy Surfaces as Slices by Combini…

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Metadynamics is a powerful method to accelerate molecular dynamics simulations, but its efficiency critically depends on the identification of collective variables that capture the slow modes of the process. Unfortunately, collective…

Chemical Physics · Physics 2023-07-17 Ofir Blumer , Shlomi Reuveni , Barak Hirshberg

We present a method for determining the free energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting…

Statistical Mechanics · Physics 2008-03-31 Alessandro Barducci , Giovanni Bussi , Michele Parrinello

A family of fast sampling methods is introduced here for molecular simulations of systems having rugged free energy landscapes. The methods represent a generalization of a strategy consisting of adjusting a model for the free energy as a…

Computational Physics · Physics 2022-02-07 Pablo F. Zubieta Rico , Juan J. de Pablo

Calculating free energies is an important and notoriously difficult task for molecular simulations. The rapid increase in computational power has made it possible to probe increasingly complex systems, yet extracting accurate free energies…

Chemical Physics · Physics 2014-07-29 Viveca Lindahl , Jack Lidmar , Berk Hess

Ab initio molecular dynamics (AIMD) simulations employing density functional theory (DFT) and plane waves are routinely carried out using density functionals at the level of Generalized Gradient Approximation (GGA). AIMD simulations…

Chemical Physics · Physics 2020-03-04 Sagarmoy Mandal , Nisanth N. Nair

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error…

We investigate the kinetics and the free energy landscape of the crystallization of hard spheres from a supersaturated metastable liquid though direct simulations and forward flux sampling. In this first paper, we describe and test two…

Soft Condensed Matter · Physics 2018-08-17 David Richard , Thomas Speck

In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine learning tasks. Towards this end, we present a data driven…

Biomolecules · Quantitative Biology 2024-01-23 Paula Mercurio , Di Liu

We develop an efficient parallel multiscale method that bridges the atomistic and mesoscale regimes, from nanometer to micron and beyond, via concurrent coupling of atomistic simulation and mesoscopic dynamics. In particular, we combine an…

Computational Physics · Physics 2020-12-23 Yuying Wang , Zhen Li , Junbo Xu , Chao Yang , George Em Karniadakis

Transition interface sampling (TIS) and replica exchange TIS (RETIS) are powerful methods for computing rates of rare events inaccessible to straightforward molecular dynamics (MD) simulations. Path reweighting extends their output,…

Chemical Physics · Physics 2026-05-15 Titus S. van Erp , Daniel T. Zhang , Elias Wils , Sina Safaei , An Ghysels

We introduce the well-tempered ensemble (WTE) which is the biased ensemble sampled by well-tempered metadynamics when the energy is used as collective variable. WTE can be designed so as to have approximately the same average energy as the…

Statistical Mechanics · Physics 2015-05-14 M. Bonomi , M. Parrinello

We present a general extension of the metadynamics allowing for an automatic sampling of quantum property manifolds (ASQPM) giving rise to functional landscapes that are analogous to the potential energy surfaces in the frame of the…

Chemical Physics · Physics 2020-01-08 Joachim O. Lindner , Merle I. S. Röhr

Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery.…

Machine Learning · Computer Science 2023-12-12 Ellis R. Crabtree , Juan M. Bello-Rivas , Andrew L. Ferguson , Ioannis G. Kevrekidis

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based…

High Energy Physics - Experiment · Physics 2025-05-12 CMS Collaboration

Hybrid simulation (HS) is a widely used structural testing method that combines a computational substructure with a numerical model for well-understood components and an experimental substructure for other parts of the structure that are…

Machine Learning · Computer Science 2020-04-07 Elif Ecem Bas , Mohamed A. Moustafa , David Feil-Seifer , Janelle Blankenburg

We present a molecular simulation method to simultaneously find multiple transition pathways, and their associated free-energy profiles. The scheme extends path-metadynamics (PMD) [Phys. Rev. Lett. 109, 020601 (2012)] with multiple paths…

Statistical Mechanics · Physics 2025-06-23 Alberto Pérez de Alba Ortíz , Bernd Ensing

Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remain a central challenge for designing energy-efficient adsorption-based separation processes. Traditional…

Statistical Mechanics · Physics 2026-03-31 Sunghyun Yoon , Jui Tu , Li-Chiang Lin , Yongchul G. Chung

Using a coarse molecular-dynamics (CMD) approach with an appropriate choice of coarse variable (order parameter), we map the underlying effective free-energy landscape for the melting of a crystalline solid. Implementation of this approach…

Materials Science · Physics 2009-11-11 Miguel A. Amat , Ioannis G. Kevrekidis , Dimitrios Maroudas

Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a…

Machine Learning · Computer Science 2024-03-21 Paulami Banerjee , Mohan Padmanabha , Chaitanya Sanghavi , Isabel Michel , Simone Gramsch

Recovering unbiased kinetic and thermodynamic observables from the enhanced sampling simulations is a central challenge in rare-event sampling. Classical Girsanov Reweighting (GR) offers a principled solution by yielding exact pathwise…

Quantitative Methods · Quantitative Biology 2026-04-21 Yan Wang , Hao Wu , Simon Olsson