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Updated: Apr 9, 2026

Uracil-DNA Glycosylase Assay by Matrix-assisted Laser Desorption/Ionization Time-of-flight Mass Spectrometry Analysis

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Published on: April 22, 2022

Exploring active learning strategies for excited state dynamics: application to uracil.

Juan Carlos San Vicente Veliz¹, Mark DelloStritto², Spiridoula Matsika¹

¹Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, USA. spiridoula.matsika@temple.edu.

Physical Chemistry Chemical Physics : PCCP

|April 8, 2026

Summary

We developed a machine-learning model for uracil excited state dynamics. This model accurately predicts molecular behavior, enabling longer simulations and enhancing our understanding of photochemical processes.

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Molecular Beam Mass Spectrometry With Tunable Vacuum Ultraviolet VUV Synchrotron Radiation

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Published on: October 30, 2012

Area of Science:

Computational Chemistry
Photochemistry
Machine Learning

Background:

Understanding excited state dynamics is crucial for photochemistry.
Accurate modeling of molecular systems like uracil requires efficient computational methods.

Purpose of the Study:

To implement and benchmark a machine-learned model for uracil excited state dynamics.
To improve the accuracy and efficiency of photochemical simulations.

Main Methods:

Utilized a polarizable atom interaction neural network (PaiNN) architecture.
Trained the model on trajectory surface hopping dynamics data.
Employed an adaptive active learning approach and optimized loss functions (Asinh for forces).

Main Results:

The PaiNN model accurately predicts energies, forces, and nonadiabatic couplings.
Active learning enhanced sampling around conical intersections, improving population accuracy.
Simulations were extended to 4-6 ps with minimal additional data.

Conclusions:

The developed machine-learning model provides an effective approach for simulating uracil excited state dynamics.
Combining active learning with targeted data selection improves potential energy surface accuracy.
This method enables more extensive and accurate photochemical simulations.