Training collective variables for enhanced sampling via neural networks based discriminant analysis

Bonati, Luigi (2021) Training collective variables for enhanced sampling via neural networks based discriminant analysis. Il nuovo cimento C, 44 (4-5). pp. 1-4. ISSN 1826-9885

Text ncc12282.pdf - Published Version Download (319kB)

Abstract

A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to choose appropriate variables. Here we review some recent developments in the data-driven design of collective variables, which combine Fisher’s discriminant analysis and neural networks. This approach allows to compress the fluctuations of metastable states into a low-dimensional representation. We illustrate through different applications the effectiveness of this method in accelerating the sampling, while also identifying the physical descriptors that undergo the most significant changes in the process.

Item Type:	Article
Subjects:	500 Scienze naturali e Matematica > 530 Fisica
Depositing User:	Marina Spanti
Date Deposited:	20 Sep 2021 09:43
Last Modified:	20 Sep 2021 09:43
URI:	http://eprints.bice.rm.cnr.it/id/eprint/21339

Actions (login required)

View Item