Machine Learning for Absorption Cross Sections

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 propert...

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Veröffentlicht in:	The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory Molecules, spectroscopy, kinetics, environment, & general theory, 2020-09, Vol.124 (35), p.7199-7210
Hauptverfasser:	Xue, Bao-Xin, Barbatti, Mario, Dral, Pavlo O
Format:	Artikel
Sprache:	eng
Schlagworte:	A: New Tools and Methods in Experiment and Theory Chemical Sciences or physical chemistry Theoretical and
Online-Zugang:	Volltext
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