Constructing Collective Variables Using Invariant Learned Representations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10465875" target="_blank" >RIV/00216208:11310/23:10465875 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/23:10465875

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Td0I5YqVWm" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Td0I5YqVWm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jctc.2c00729" target="_blank" >10.1021/acs.jctc.2c00729</a>

Result language

angličtina

Original language name

Constructing Collective Variables Using Invariant Learned Representations

Original language description

On the time scales accessible to atomistic numerical modeling, chemical reactions are considered rare events. Therefore, the atomistic simulations are commonly biased along a lowdimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variables. However, suitable collective variables are often complicated to guess a priori. We propose a novel method of collective variable discovery based on dimensionality reduction of the atomic representation vectors. These linear-scaling and invariant representations can be either fixed (untrained) or learned by supervised training of the end-to-end machine learning potential. The learned representations are expected to reflect not only the structural but also the energetic features of the system that are transferable to all of the reactive transformation covered by the machine learning potential. We demonstrate our approach on four high-barrier reactions ranging from a simple gas-phase hydrogen jump reaction to complex reactions in periodic models of industrially relevant heterogeneous catalysts. High data efficiency, automatized feature extraction, favorable scaling, and retention of inherent invariances are all properties that are expected to enable fast and largely automatic construction of suitable collective variables even in highly complex reactive scenarios such as reactive/catalytic transformations at solid-liquid interfaces.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10403 - Physical chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Chemical Theory and Computation

ISSN

1549-9618

e-ISSN

1549-9626

Volume of the periodical

19

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

887-901

UT code for WoS article

000933891600001

EID of the result in the Scopus database

2-s2.0-85147209622