  High-throughput material screening for the discovery and design of novel
functional materials requires automatized analyses of theoretical and
experimental data. Here we study the subject of human-free analyses of
one-dimensional spectroscopic data, {\it e.g.} in the frequency domain, via
employing deep convolution neural network. Specifically, we trained various
deep convolution neural network and benchmarked their performance in
decomposing one-dimensional noisy data into multiple nonorthogonal peaks in an
iterative manner, after which a conventional basin-hopping algorithm was
applied to further reduce residual fitting error. Among six different network
architectures, a variant of "Squeeze-and-excitation" network (SENet) structure
that we first propose in this study shows the best performance. Dependency of
training performance with respect to the choice of the loss function is also
discussed. We conclude by applying our modified SENet model to experimental
photoemission spectra of graphene, MoS$_2$, and WS$_2$ and address its
potential applications and limitations.
