A machine learning (ML) framework to predict the physical band potentials for a range of semiconductor materials, from metal sulfide, oxide, and nitride, to oxysulfide and oxynitride, is hereby described. A valence band maximum (VBM) model was established via the transfer learning of a large dataset of 2D materials (1382 samples, but with incorrect VBM potentials) onto a much smaller dataset of physically measured VBM for bulk 3D materials (87 samples) on a crystal graph convolutional neural network. This resulted in predictions with experimental accuracy (RMSE = 0.27 eV), which is a 3-fold improvement compared with ML trained on the physical dataset without transfer learning (RMSE = 0.75 eV). When combined with the bandgap prediction model (RMSE = 0.29 eV), a full prediction of conduction and valence band potentials can be made, which to the best of our knowledge, is the first for any ML framework. The variation of band potentials across low-index surfaces was predicted correctly and verified with reported physical potentials. In fact, the framework is able to capture variation in band potentials associated with minor atomic position alterations. Based on this, a general trend between surface atomic displacement and VBM shift was observed across various semiconductor materials. The model is not yet able to cope with major rearrangement of atomic sequence on surface layers, i.e., surface reconstructions, since it was not trained with such data but can be easily done so with specifically designed dataset. As an example application, the ML framework was used for the screening of potential photocatalytic materials for visible light water splitting. A total of 824 materials was successfully identified, including those experimentally-verified in the literature.

中文翻译：

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半导体材料能带势的全面预测


本文描述了一种机器学习 (ML) 框架，用于预测一系列半导体材料（从金属硫化物、氧化物和氮化物到氧硫化物和氧氮化物）的物理带势。通过将大型 2D 材料数据集（1382 个样本，但 VBM 势）迁移学习到较小的块状 3D 材料物理测量 VBM 数据集（87 个样本），建立了价带最大值 (VBM) 模型。晶体图卷积神经网络。这导致了具有实验精度的预测 (RMSE = 0.27 eV)，与在没有迁移学习的物理数据集上训练的 ML (RMSE = 0.75 eV) 相比，提高了 3 倍。与带隙预测模型（RMSE = 0.29 eV）结合使用时，可以对导带电势和价带电势进行全面预测，据我们所知，这对于任何机器学习框架来说都是首创。正确预测了低折射率表面上带电势的变化，并用报告的物理电势进行了验证。事实上，该框架能够捕获与微小原子位置变化相关的能带势的变化。基于此，在各种半导体材料中观察到表面原子位移和 VBM 位移之间的总体趋势。该模型尚无法应对表面层上原子序列的主要重新排列，即表面重建，因为它没有使用此类数据进行训练，但可以使用专门设计的数据集轻松完成。作为一个示例应用，ML 框架用于筛选用于可见光水分解的潜在光催化材料。总共成功鉴定了 824 种材料，其中包括文献中经过实验验证的材料。