  Mass spectrometry is a widespread approach to work out what are the
constituents of a material. Atoms and molecules are removed from the material
and collected, and subsequently, a critical step is to infer their correct
identities based from patterns formed in their mass-to-charge ratios and
relative isotopic abundances. However, this identification step still mainly
relies on individual user's expertise, making its standardization challenging,
and hindering efficient data processing. Here, we introduce an approach that
leverages modern machine learning technique to identify peak patterns in
time-of-flight mass spectra within microseconds, outperforming human users
without loss of accuracy. Our approach is cross-validated on mass spectra
generated from different time-of-flight mass spectrometry(ToF-MS) techniques,
offering the ToF-MS community an open-source, intelligent mass spectra
analysis.
