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Rapid advancements in machine-learning methods have led to the emergence ofmachine-learning-based interatomic potentials as a new cutting-edge tool forsimulating large systems with ab initio accuracy. Still, the community awaitsuniversal inter-atomic models that can be applied to a wide range of materialswithout tuning neural network parameters. We develop a universal deep-learninginter-atomic potential (the DPA-Semi model) for 19 semiconductors ranging fromgroup IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs,InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independentdeep potential models for each semiconductor are prepared for detailedcomparison. The training data are obtained by performing density functionaltheory calculations with numerical atomic orbitals basis sets to reduce thecomputational costs. We systematically compare various properties of the solidand liquid phases of semiconductors between different machine-learning models.We conclude that the DPA-Semi model owns an ab initio accuracy and can beregarded as a pre-trained model to study group IIB to VIA semiconductors.

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: A Comparative Study of Universal and Independent Models