The 2D ultrawide bandgap (UWBG) semiconductors have attracted great attentions for the next generation of electronics and optoelectronics, owing to their superiority on material flexibility, device stability, and power consumption. However, few 2D UWBG semiconductors have been discovered, impeding their prosperous developments and widespread applications. Here, a high‐throughput workflow is constructed to screen 2D UWBG semiconductors assisted by machine learning, and 507 potential candidates are obtained. Moreover, by learning, predicting, and screening Young's modulus and Poisson's ratio, 31 flexible 2D UWBG semiconductors are identified. Then the generation and the diffusion of anion vacancies, as well as the corresponding electronic properties are investigated by using the first‐principles calculations, and 3 of them are demonstrated as the most promising candidates for the flexible resistive materials. The facile interface tunneling and the increased material conductance caused by the anion vacancies will contribute to the transition from high resistive state to low resistive state. This work provides an efficient high‐throughput screening protocol to enrich the family of 2D UWBG semiconductors and is expected to foster their practical applications.

中文翻译：

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机器学习实现柔性电阻材料的 2D 超宽带隙半导体的高通量筛选


2D 超宽带隙 （UWBG） 半导体因其在材料柔韧性、器件稳定性和功耗方面的优越性，在下一代电子和光电子学中引起了极大的关注。然而，2D UWBG 半导体被发现的很少，阻碍了它们的繁荣发展和广泛应用。在这里，构建了一个高通量工作流程，在机器学习的辅助下筛选 2D UWBG 半导体，并获得了 507 个潜在的候选者。此外，通过学习、预测和筛选杨氏模量和泊松比，确定了 31 种柔性 2D UWBG 半导体。然后，使用第一性原理计算研究了阴离子空位的产生和扩散，以及相应的电子性质，其中 3 种被证明是柔性电阻材料最有前途的候选材料。由阴离子空位引起的简单界面隧穿和材料电导增加将有助于从高电阻状态到低电阻状态的转变。这项工作提供了一种高效的高通量筛选方案，以丰富 2D UWBG 半导体家族，并有望促进其实际应用。