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Data-Driven Collective Variables for Enhanced Sampling

Luigi Bonati1*, Valerio Rizzi2, Michele Parrinello2

1 Department of Physics, ETH Zurich, 8092 Zurich, Switzerland and Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana, 6900 Lugano, Switzerland

2 Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland

* Corresponding authors emails: luigi.bonati@phys.chem.ethz.ch
DOI10.24435/materialscloud:2020.0035/v1 [version v1]

Publication date: Apr 06, 2020

How to cite this record

Luigi Bonati, Valerio Rizzi, Michele Parrinello, Data-Driven Collective Variables for Enhanced Sampling, Materials Cloud Archive 2020.0035/v1 (2020), doi: 10.24435/materialscloud:2020.0035/v1.

Description

Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the non-linearly separable dataset composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing non-linear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

Materials Cloud sections using this data

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Files

File name Size Description
data-driven-CVs-inputs-and-results.zip
MD5md5:c903c8b59310aaea6f3bb6b302507bd6
63.4 MiB Inputs and results (ZIP file)
README.txt
MD5md5:1f1cad8d7a59d68a8af86b8053101aa7
1.5 KiB README

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper in which the method is described)
L. Bonati, V. Rizzi, M. Parrinello, J. Phys. Chem. Lett., 11, 2998-3004 (2020) doi:10.1021/acs.jpclett.0c00535
Software (The latest release of the code)
Github repository
Software (Tutorial for the training of the Deep-LDA CV )
Google Colab notebook
Preprint (Open-access preprint of the method)
L. Bonati, V. Rizzi, M. Parrinello, arXiv:2002.06562

Keywords

ERC MARVEL/DD1 ENHANCED SAMPLING COLLECTIVE VARIABLES PLUMED OPES METADYNAMICS NEURAL NETWORKS

Version history:

2020.0035/v1 (version v1) [This version] Apr 06, 2020 DOI10.24435/materialscloud:2020.0035/v1