Ab initio calculations represent the technique of election to study material system, however, they present
severe limitations in terms of the size of the system that can be simulated. Often, the results in the simulation
of amorphous materials depend dramatically on the size of the system. Here, we overcome this limitation for
the specific case of mechanical properties of amorphous silicon nitride (a-Si3N4) by training a machine learning
(ML) interatomic model. Our strategy is based on the generation of targeted training sets, which also include
deliberately stressed structures. Using this dataset, we trained a moment tensor potential (MTP) for a-Si3N4.
We show that molecular dynamics simulations using the ML model on much larger systems yield elastically
isotropic response and can reproduce experimental measurement. To do so, models containing at least ≈3, 500
atoms are necessary. The Young’s modulus calculated from the MTP at room temperature is 220 GPa, which is
very well in agreement with the nanoindentation measurement. Our study demonstrates the broader impact of
machine learning potentials for predicting structural and mechanical properties, even for complex amorphous
structures.