Machine learning for nonadiabatic molecular dynamics: best practices and recent progress

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F25%3A43932659" target="_blank" >RIV/60461373:22340/25:43932659 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc05579b" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc05579b</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d5sc05579b" target="_blank" >10.1039/d5sc05579b</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning for nonadiabatic molecular dynamics: best practices and recent progress

Popis výsledku v původním jazyce

Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data.

Název v anglickém jazyce

Machine learning for nonadiabatic molecular dynamics: best practices and recent progress

Popis výsledku anglicky

Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GN22-13489O" target="_blank" >GN22-13489O: Predikce a optimalizace optických vlastností v chemickém prostoru pomocí strojového učení</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Chemical Science

ISSN

2041-6520

e-ISSN

2041-6539

Svazek periodika

16

Číslo periodika v rámci svazku

38

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

26

Strana od-do

17542-17567

Kód UT WoS článku

001571121600001

EID výsledku v databázi Scopus

2-s2.0-105017706666