LASPAI: AI-powered platform for the future atomic simulation

Abstract

Atomic simulation is becoming a vital tool in modern science, bridging the gap between theory and experiments. Since its birth in 1950s, the balance between accuracy and speed has been the main theme in simulating atomic world and in recent years machine learning potential based methods emerged as a promising alternative to density functional theory calculations for exploring complex potential energy surface (PES). Here we report our implementation of LASPAI (www.laspai.com), a web-based platform for future atomic simulations, which is built using the generalized global neural network potential for fast PES evaluation as implemented in LASP software, together with a series of general diffusion generative models, stochastic surface walking (SSW) global optimization, and other common simulation tools for the PES exploration of molecules and materials. We show that LASPAI platform offers a task-orientated, user-friendly, web-based graphical user interface (GUI) to greatly simplify and speed-up atomic simulations for a wide range of scientific areas, ranging from molecule and material structure prediction to solid-gas, solid-liquid, solid-solid interface identification, and reaction pathway simulations. It aims to provide a fast chemical knowledge delivery for scientists to design new materials and reactions.