Pinheiro Max, de Oliveira Bispo Matheus, Mattos Rafael S, Telles do Casal Mariana, Chandra Garain Bidhan, Toldo Josene M, Mukherjee Saikat, Barbatti Mario
Aix Marseille University, CNRS, ICR 13397 Marseille France
Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven 3001 Leuven Belgium.
Digit Discov. 2025 Jan 8;4(3):666-682. doi: 10.1039/d4dd00374h. eCollection 2025 Mar 12.
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.
非绝热分子动力学（NAMD）数据的分析因其高维度和复杂性而面临重大挑战。为了解决这些问题，我们引入了ULAMDyn，这是一个基于Python的开源软件包，旨在自动对NAMD模拟生成的大型数据集进行无监督分析。ULAMDyn与Newton-X平台无缝集成，并采用先进的降维和聚类技术来揭示分子轨迹中的隐藏模式，从而更直观地理解激发态过程。以富烯的光化学动力学为例，我们展示了ULAMDyn如何有效地识别关键分子几何结构和关键非绝热跃迁。该软件包为解释大型NAMD数据集提供了一种简化的、可扩展的解决方案。它有望推动广泛分子系统中激发态动力学研究的进展。
