Journal
JOURNAL OF PHYSICAL CHEMISTRY A
Volume 124, Issue 35, Pages 7199-7210Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpca.0c05310
Keywords
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Funding
- European Research Council (ERC) Advanced Grant SubNano [832237]
- Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces
- European Research Council (ERC) [832237] Funding Source: European Research Council (ERC)
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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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