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ML-cross-section_v2.pdf (2.04 MB)

Machine Learning for Absorption Cross Sections

revised on 22.07.2020 and posted on 22.07.2020 by Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral
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 relatively few points in the ensemble. 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.


Computational Photochemistry in the Long Timescale: Sub-ns Photoprocesses in DNA

European Research Council

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State Key Laboratory of Physical Chemistry of Solid Surfaces


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Aix Marseille University



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Declaration of Conflict of Interest

No conflict of interest.


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in The Journal of Physical Chemistry A

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