Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

Understand

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields.

• This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions.
• A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models.
• These models are proposed as foundational models suitable for any system, covering most elements from the periodic table.