Michele Invernizzi^1,2*, Pablo Miguel Piaggi^3*, Michele Parrinello^4,2,5*

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.

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:9ed3e3588f3337c7c93679da83e17ba5	580 Bytes	Short description of the content
figures.zip MD5md5:606cf8271b6842107195733fd5e52b03	20.6 MiB	All the processed data used to make the figures of the paper
alanine.zip MD5md5:b48457f5198991e20255585733b41e58	2.5 MiB	Multicanonical (parallel-tempering-like) simulation of alanine dipeptide
chignolin.zip MD5md5:9a7d13aac8c0d35732b31941cfc832d2	4.0 GiB	Multithermal-multibaric simulation of chignolin in water
water.zip MD5md5:c56ec99b0ca4137feb514d4ab91da18b	4.0 MiB	Thermodynamic integration from TIP4P water to Lennard-Jones, in a single simulation
model.zip MD5md5:033e2fcc4e47a1b603b8703f91e94b89	6.5 MiB	Multiumbrella simulation of a double-well model potential in 2D
sodium.zip MD5md5:569569127d4b3d1d7289f1d1b303ae1f	150.6 MiB	Multi thermal-baric-umbrella simulation of sodium, across the bcc-liquid phase transition

License

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Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

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