This letter proposes a transfer learning (TL) method to generate neural network (NN) database to model doping and alloy. By leveraging the valuable potential energy surface (PES) information already available in source system and similarities between source and target systems, the proposed TL successfully reduces computational cost by several orders of magnitude, while keeping ab-initio level high accuracy. We show that it is generally applicable to model ${p}$ -type, ${n}$ -type, and alloy atomic substitutions.

中文翻译：

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用于有效掺杂和合金原子建模的势能面转移学习

这封信提出了一种迁移学习 (TL) 方法来生成神经网络 (NN) 数据库来模拟掺杂和合金。通过利用源系统中已有的有价值的势能面 (PES) 信息以及源系统和目标系统之间的相似性，所提出的 TL 成功地将计算成本降低了几个数量级，同时保持了从头开始的高精度。我们证明它普遍适用于模型 ${p}$ -类型， ${n}$ -类型和合金原子取代。