ArticleThe journal of physical chemistry. A2020
Machine Learning for Absorption Cross Sections.
Article in The journal of physical chemistry. A, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Efficient Excitonic Configuration Interaction for Large-Scale Multichromophoric Systems Using the Resolution-of-Identity Approximation.The journal of physical chemistry letters · 2025Article
- Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory.The journal of physical chemistry. A · 2024Article
- MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.Journal of chemical theory and computation · 2024Article
- Fast and accurate excited states predictions: machine learning and diabatization.Physical chemistry chemical physics : PCCP · 2024Article
- The OpenMolcasJournal of chemical theory and computation · 2023Article
- Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles.Journal of chemical theory and computation · 2022Article
- Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.Journal of chemical theory and computation · 2022Article
- Choosing the right molecular machine learning potential.Chemical science · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Review
- Physically inspired deep learning of molecular excitations and photoemission spectra.Chemical science · 2021Article
- MLatom 2: An Integrative Platform for Atomistic Machine Learning.Topics in current chemistry (Cham) · 2021Review
- Molecular excited states through a machine learning lens.Nature reviews. Chemistry · 2021Review
- Nonadiabatic Absorption Spectra and Ultrafast Dynamics of DNA and RNA Photoexcited Nucleobases.Molecules (Basel, Switzerland) · 2021Article
- Machine learning dielectric screening for the simulation of excited state properties of molecules and materials.Chemical science · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.