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Machine learning potential enables molecular dynamics simulations of systemsbeyond the capability of classical force fields. The traditional approach todevelop structural sets for training machine learning potential typicallygenerate a great number of redundant configurations, which will result inunnecessary computational costs. This work investigates the possibility ofreducing redundancy in an initial data set containing 6183 configurations for aLi-Si machine learning potential. Starting from the initial data set, weconstructed a series of subsets ranging from 25 to 1500 configurations bycombining a structural similarity analysis algorithm and the farthest pointsampling method. Results show that the machine learning potential trained froma data set containing 400 configurations can achieve an accuracy comparable tothe one developed from the initial data set of 6183 configurations indescribing potential energies, atomic forces, and structural properties ofLi-Si systems. In addition, the redundancy reducing approach also demonstratesadvantages over the classic stochastic method for constructing a concisetraining data set for Li-Si systems.

Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis