  The International Atomic Energy Agency (IAEA) stopping power database is a
highly valued public resource compiling most of the experimental measurements
published over nearly a century. The database-accessible to the global
scientific community-is continuously updated and has been extensively employed
in theoretical and experimental research for more than 30 years. This work aims
to employ machine learning algorithms on the 2021 IAEA database to predict
accurate electronic stopping power cross sections for any ion and target
combination in a wide range of incident energies. Unsupervised machine learning
methods are applied to clean the database in an automated manner. These
techniques purge the data by removing suspicious outliers and old isolated
values. A large portion of the remaining data is used to train a deep neural
network, while the rest is set aside, constituting the test set. The present
work considers collisional systems only with atomic targets. The first version
of the ESPNN (electronic stopping power neural-network code), openly available
to users, is shown to yield predicted values in excellent agreement with the
experimental results of the test set.
