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A unified approach to enhanced sampling

Michele Invernizzi1,2*, Pablo Miguel Piaggi3*, Michele Parrinello4,2,5*

1 Department of Physics, ETH Zurich, c/o USI, 6900 Lugano, Switzerland

2 Facoltà di Informatica, Institute of Computational Science, Università della Svizzera Italiana, 6900 Lugano, Switzerland

3 Department of Chemistry, Princeton University, Princeton, New Jersey 08540, USA

4 Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI, 6900 Lugano, Switzerland

5 Italian Institute of Technology, 16163 Genova, Italy

* Corresponding authors emails: michele.invernizzi@phys.chem.ethz.ch, ppiaggi@princeton.edu, parrinello@phys.chem.ethz.ch
DOI10.24435/materialscloud:gr-w3 [version v1]

Publication date: Jul 20, 2020

How to cite this record

Michele Invernizzi, Pablo Miguel Piaggi, Michele Parrinello, A unified approach to enhanced sampling, Materials Cloud Archive 2020.81 (2020), doi: 10.24435/materialscloud:gr-w3.


The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.

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File name Size Description
580 Bytes Short description of the content
20.6 MiB All the processed data used to make the figures of the paper
2.5 MiB Multicanonical (parallel-tempering-like) simulation of alanine dipeptide
4.0 GiB Multithermal-multibaric simulation of chignolin in water
4.0 MiB Thermodynamic integration from TIP4P water to Lennard-Jones, in a single simulation
6.5 MiB Multiumbrella simulation of a double-well model potential in 2D
150.6 MiB Multi thermal-baric-umbrella simulation of sodium, across the bcc-liquid phase transition


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Expanded Ensembles Importance Sampling Free Energy Metadynamics Replica Exchange Parallel Tempering Thermodynamic Integration Umbrella Sampling MARVEL/DD1 SNSF ERC

Version history:

2020.81 (version v1) [This version] Jul 20, 2020 DOI10.24435/materialscloud:gr-w3