Citation: Han-Zhi Luo, Qi-Ming Liang, Zi-Xing Guo, Xin-Tian Xie, Jin-Peng Tang, Tong Guan, Ye-Fei Li, Si-Cong Ma, Ying-Chen Xu, Zhen-Xiong Wang, Cheng Shang, Zhi-Pan Liu. LASPAI: AI-powered platform for the future atomic simulation[J]. Acta Physico-Chimica Sinica, ;2026, 42(6): 100235. doi: 10.1016/j.actphy.2025.100235 shu

LASPAI: AI-powered platform for the future atomic simulation

1.
State Key Laboratory of Porous Materials for Separation and Conversion, Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China;
2.
State Key Laboratory of Metal Organic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China

Corresponding author: Zhen-Xiong Wang, Cheng Shang, Zhi-Pan Liu,
Received Date: 17 November 2025
Revised Date: 30 December 2025
Accepted Date: 30 December 2025

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.
- Atomic simulation,
- Generative model,
- Global machine learning potential,
- LASPAI platform
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