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Alec Owens
Researcher at University College London
Publications - 47
Citations - 1299
Alec Owens is an academic researcher from University College London. The author has contributed to research in topics: Ab initio & Rotational–vibrational spectroscopy. The author has an hindex of 15, co-authored 41 publications receiving 952 citations. Previous affiliations of Alec Owens include University of Hamburg & Max Planck Society.
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Journal ArticleDOI
The ExoMol database: Molecular line lists for exoplanet and other hot atmospheres
Jonathan Tennyson,Sergei N. Yurchenko,Ahmed Al-Refaie,Emma J. Barton,Katy L. Chubb,Phillip A. Coles,S. Diamantopoulou,Maire N. Gorman,Christian Hill,Aden Lam,Lorenzo Lodi,Laura K. McKemmish,Yueqi Na,Alec Owens,Oleg L. Polyansky,Tom Rivlin,Clara Sousa-Silva,Daniel S. Underwood,Andrey Yachmenev,Emil J. Zak +19 more
TL;DR: The ExoMol database as mentioned in this paper provides extensive line lists of molecular transitions which are valid over extended temperature ranges, including lifetimes of individual states, temperature-dependent cooling functions, Lande g-factors, partition functions, cross sections, k-coefficients and transition dipoles with phase relations.
Journal ArticleDOI
The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres
Jonathan Tennyson,Sergei N. Yurchenko,Ahmed Al-Refaie,Victoria H. J. Clark,Katy L. Chubb,Katy L. Chubb,Eamon K. Conway,Eamon K. Conway,Akhil Dewan,Maire N. Gorman,Maire N. Gorman,Christian Hill,Christian Hill,A. E. Lynas-Gray,A. E. Lynas-Gray,A. E. Lynas-Gray,T. Mellor,Laura K. McKemmish,Laura K. McKemmish,Alec Owens,Oleg L. Polyansky,Oleg L. Polyansky,Mikhail Semenov,Wilfrid Somogyi,Giovanna Tinetti,Apoorva Upadhyay,Ingo Waldmann,Yixin Wang,Yixin Wang,Samuel Wright,Olga P. Yurchenko +30 more
TL;DR: The ExoMol database as mentioned in this paper provides molecular data for spectroscopic studies of hot atmospheres, including 80 molecules and 190 isotopologues with over 700 billion transitions.
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Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.
TL;DR: In this article, a self-correcting, kernel ridge regression (KRR) based machine learning (ML) was used to generate high-level ab initio potential energy surfaces (PESs) using structure-based sampling.
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Hierarchical machine learning of potential energy surfaces
TL;DR: In this article, a hierarchical machine learning (hML) model is used to estimate energy and energy corrections with a hierarchy of quantum chemical methods, and the optimal training set size and composition of each constituent machine learning model is determined to minimize the computational effort necessary to achieve the required accuracy.
Journal ArticleDOI
Accurate ab initio vibrational energies of methyl chloride
TL;DR: It is believed that it would be extremely challenging to go beyond the accuracy currently achieved for CH3Cl without empirical refinement of the respective PESs, and the combined effect of the HL corrections and CBS extrapolation on the vibrational wavenumbers indicates that both are needed to compute accurate theoretical results for methyl chloride.