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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Machine learning for nonadiabatic molecular dynamics: best practices and recent progress
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10403 - Physical chemistry

Project
<a href="/en/project/GN22-13489O" target="_blank" >GN22-13489O: Optical Properties throughout Chemical Space via Machine Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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