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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 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.