ArticleJournal of chemical theory and computation2026
Predicting Molecular Laser Properties from First-Principles Using Machine Learning-Based Nuclear Ensemble Approach Spectra.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The accurate prediction of absorption and emission spectra of molecular compounds using quantum mechanical (QM) methods is essential for understanding and designing laser materials, especially when experimental data are limited or inaccessible or when the syntheses are time- and resource-consuming. In this work, a numerical framework is developed to simulate the laser properties of molecular compounds from first principles. The methodology integrates QM calculations in combination with thermal sampling and a Gaussian Mixture Model-based Nuclear Ensemble Approach (GMM-NEA) for spectra reconstruction, and a spectrally resolved spatiotemporal laser simulation model. The GMM-NEA method is extended to include spontaneous and stimulated emission processes, enabling the generation of spectroscopic input for laser modeling. The framework is validated using two boron hydrides,
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