Literature DB >> 30849916

Improving collective variables: The case of crystallization.

Yue-Yu Zhang1, Haiyang Niu1, GiovanniMaria Piccini1, Dan Mendels1, Michele Parrinello1.   

Abstract

Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.

Year:  2019        PMID: 30849916     DOI: 10.1063/1.5081040

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  3 in total

Review 1.  Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Authors:  Paraskevi Gkeka; Gabriel Stoltz; Amir Barati Farimani; Zineb Belkacemi; Michele Ceriotti; John D Chodera; Aaron R Dinner; Andrew L Ferguson; Jean-Bernard Maillet; Hervé Minoux; Christine Peter; Fabio Pietrucci; Ana Silveira; Alexandre Tkatchenko; Zofia Trstanova; Rafal Wiewiora; Tony Lelièvre
Journal:  J Chem Theory Comput       Date:  2020-07-16       Impact factor: 6.006

2.  Deep learning the slow modes for rare events sampling.

Authors:  Luigi Bonati; GiovanniMaria Piccini; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2021-11-02       Impact factor: 11.205

3.  Ab initio phase diagram and nucleation of gallium.

Authors:  Haiyang Niu; Luigi Bonati; Pablo M Piaggi; Michele Parrinello
Journal:  Nat Commun       Date:  2020-05-27       Impact factor: 14.919

  3 in total

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