Improving collective variables: The case of crystallization
- Department of Chemistry and Applied Biosciences, ETH Zurich, c/o USI Campus, Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland, and Institute of Computational Science, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Ticino, Switzerland
DOI10.24435/materialscloud:2019.0088/v1 (version v1, submitted on 12 December 2019)
How to cite this entry
Yue-Yu Zhang, Haiyang Niu, GiovanniMaria Piccini, Dan Mendels, Michele Parrinello, Improving collective variables: The case of crystallization, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0088/v1.
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.
Materials Cloud sections using this data
No Explore or Discover sections associated with this archive entry.
|310 Bytes||README.txt for all the files|
|1.0 KiB||Plumed input file for Na using HLDA combined XRD CVs|
|97.6 MiB||COLVAR file of Na using HLDA combined XRD CVs|
|1.3 KiB||Plumed input file for Al using HLDA combined XRD CVs|
|109.7 MiB||COLVAR file of Al using HLDA combined XRD CVs|
12 December 2019 [This version]