2024-03-28T12:07:36Z
https://archive.materialscloud.org/xml
oai:materialscloud.org:359
2020-04-06T00:00:00Z
DOI
Bonati, Luigi
Rizzi, Valerio
Parrinello, Michele
2020-04-06
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.
https://archive.materialscloud.org/record/2020.0035/v1
doi:10.24435/materialscloud:2020.0035/v1
mcid:2020.0035/v1
oai:materialscloud.org:359
en
Materials Cloud
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
ERC
MARVEL/DD1
ENHANCED SAMPLING
COLLECTIVE VARIABLES
PLUMED
OPES
METADYNAMICS
NEURAL NETWORKS
Data-Driven Collective Variables for Enhanced Sampling
Dataset