ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning

The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations. ULaMDyn integrates seamlessly with the Newton-X platform and employs advanced dimensionality reduction and clustering techniques to uncover hidden patterns in molecular trajectories, enabling a more intuitive understanding of excited-state processes. Using the photochemical dynamics of fulvene as a test case, we demonstrate how ULaMDyn efficiently identifies critical molecular geometries and critical nonadiabatic transitions. The package offers a streamlined, scalable solution for interpreting large NAMD datasets. It is poised to facilitate advances in the study of excited-state dynamics across a wide range of molecular systems.

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Publication Details

Journal
Digital Discovery
Published
2025-01-01
DOI
https://doi.org/10.1039/d4dd00374h
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.55

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article

ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning

Bidhan Chandra Garain, Matheus de Oliveira Bispo, Max Pinheiro, Saikat Mukherjee et al.
3 citations
Digital Discovery
Machine Learning in Materials Science
1.55
article

ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning

Bidhan Chandra Garain, Matheus de Oliveira Bispo, Max Pinheiro, Saikat Mukherjee, Mariana T. do Casal, Josene M. Toldo, Mario Barbatti, Rafael S. Mattos
article en
3 citations

Abstract

The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations. ULaMDyn integrates seamlessly with the Newton-X platform and employs advanced dimensionality reduction and clustering techniques to uncover hidden patterns in molecular trajectories, enabling a more intuitive understanding of excited-state processes. Using the photochemical dynamics of fulvene as a test case, we demonstrate how ULaMDyn efficiently identifies critical molecular geometries and critical nonadiabatic transitions. The package offers a streamlined, scalable solution for interpreting large NAMD datasets. It is poised to facilitate advances in the study of excited-state dynamics across a wide range of molecular systems.

Digital DiscoveryVol. 4(3)
Université Claude Bernard Lyon 1 (FR), École Normale Supérieure de Lyon (FR), Centre National de la Recherche Scientifique (FR), Institut Universitaire de France (FR), Aix-Marseille Université (FR), Nicolaus Copernicus University (PL), Laboratoire de Chimie (FR), KU Leuven (BE)
Aix-Marseille Université, Fondation Aix-Marseille Universite, Sony Electronics, H2020 European Research Council
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Openalex Percentile: Top 20%
Machine Learning in Materials Science
1.55
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