ESPNN: A novel electronic stopping power neural-network code built on the IAEA stopping power database. I. Atomic targets
This work provides a tool for researchers in nuclear physics and materials science to predict stopping powers, but it is incremental as it builds on an existing database and focuses on atomic targets.
The authors tackled the problem of predicting electronic stopping power cross sections for ion-target combinations by applying machine learning to the IAEA database, resulting in a neural-network code (ESPNN) that shows excellent agreement with experimental test data.
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.