ArticleJournal of chemical theory and computation2022
Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Aqua-Chloro Complexes of Plutonyl in Hydrochloric Aqueous Solutions: Theoretical Study of Individual Contributions to XAS and UV-Vis Spectra.Inorganic chemistry · 2026Article
- Predicting Molecular Laser Properties from First-Principles Using Machine Learning-Based Nuclear Ensemble Approach Spectra.Journal of chemical theory and computation · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory.The journal of physical chemistry. A · 2024Article
- 2-in-1 Phase Space Sampling for Calculating the Absorption Spectrum of the Hydrated Electron.Journal of chemical theory and computation · 2024Article
- The OpenMolcasJournal of chemical theory and computation · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting significant attention is the so-called Nuclear Ensemble Approach (NEA), that relies on generating a representative ensemble of nuclear geometries around the equilibrium structure and computing the vertical excitation energies (Δ
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Registered trials
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