Harnessing machine learning for efficient large-scale interatomic potential for sildenafil and pharmaceuticals containing H, C, N, O, and S.

In this study a cutting-edge approach to producing accurate and computationally efficient interatomic potentials using machine learning algorithms is presented. Specifically, the study focuses on the application of Allegro, a novel machine learning algorithm, running on high-performance GPUs for training potentials. The choice of training parameters plays a pivotal role in the quality of the potential functions.