ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

Digital Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

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 automate the unsupervised large datasets generated by NAMD simulations. ULaMDyn integrates seamlessly with Newton-X platform employs advanced reduction clustering techniques uncover hidden patterns in trajectories, enabling more intuitive understanding excited-state processes. Using photochemical fulvene as test case, demonstrate how efficiently identifies critical geometries transitions. offers streamlined, scalable solution for interpreting datasets. It is poised facilitate advances study across wide range systems.

Language: Английский

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(23), P. 13077 - 13077

Published: Dec. 5, 2024

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made predicting these values using machine learning deep learning. Usually, model predictions do not contain any indication possible error (uncertainty) or only one criterion is used for this purpose. The spread several models included ensemble, molecular similarity considered molecule most "similar" from training set, are that allow us to estimate uncertainty. Euclidean distance between vectors, calculated based on real-valued descriptors, assessment similarity. Another factor indicating uncertainty molecule's belonging clusters (data set clustering). Together, all three factors features model. Classification predict whether a prediction belongs worst 15% were obtained. area under receiver operating curve value range 0.73-0.82 tasks: retention indices gas chromatography, times liquid collision cross-sections ion mobility spectroscopy.

Language: Английский

Complementing Adiabatic and Nonadiabatic Methods To Understand Internal Conversion Dynamics in Porphyrin Derivatives

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

We analyze the internal conversion dynamics within

Language: Английский