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In The journal of physical chemistry letters ; h5-index 129.0

Theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), solely based on chemical composition, already becomes a routine tool to determine the structures of physical and chemical systems, e.g. solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded owing to the unfavourable scaling of the computational cost with respective to the system size. During recent years, machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction on the basic principles and advantages for the combination of structure prediction and MLP, as well as challenges and opportunities along this promising direction.

Tong Qunchao, Gao Pengyue, Liu Hanyu, Xie Yu, Lv Jian, Wang Yanchao, Zhao Jijun