Matlantis ([ Ссылка ]) supports large-scale materials discovery by simulating the behavior of various materials at an atomic level. The versatile atomistic simulator is powered by a neural network potential (NNP) in which a deep learning model is incorporated into a conventional atomic simulator. Conventional physics-based atomistic simulation of material behavior requires high-cost calculations for solving the electronic state. Matlantis bypasses such calculations thanks to the deep learning model trained with energy, force, and other data necessary for simulating atomic behavior. This allows researchers to quickly simulate a massive number of realistic, complex systems with relatively low calculation costs without compromising versatility.
