Basically, information like which metals were used, how those metals were arranged at the atomic level, and how each alloy performed in terms of strength, flexibility, and other mechanical properties was fed to the system.
Two machine learning models were trained using the information. The first was a Stacked Ensemble Machine Learning (SEML) model, which used the composition (the elements and their amounts) to predict how strong or flexible an alloy would be.
The second model was a Convolutional Neural Network (CNN), a type of AI usually used for analyzing images, but here, it was used to examine how atoms of different elements were arranged next to each other inside the alloy, as this structure has a big impact on how the metal behaves.