Hydrogen economy, wherein hydrogen is used as the fuel in the transport and energy sector, holds significant promise in mitigating the deleterious effects of global warming. Photo-catalytic water splitting using sunlight is perhaps the cleanest way of producing hydrogen fuel. Among various other factors, widespread adoption of this technology has mainly been stymied by the lack of a catalyst material with high efficiency. 2D materials have shown significant promise as efficient photo-catalysts for water splitting. The availability of open databases containing the ‘computed’ properties of 2D materials and advancements in deep learning now enable us to do ‘inverse’ design of these 2D photo-catalysts for water splitting. We use one such database (Jain et al, ACS Energy Lett. 2019, 4, 6, 1410–1411) to build a generative model for the discovery of novel 2D photo-catalysts. The structures of the materials were converted into a 3D image-based representation that was used to train a cell, basis autoencoder and segmentation network to ascertain the lattice parameters as well as position of atoms from the images. Subsequently, the cell and basis encodings were used to train a conditional variational autoencoder (CVAE) to learn a continuous representation of the materials in a latent space. The latent space of the CVAE was then sampled to generate several new 2D materials that were likely to be efficient photo-catalysts for water splitting. The bandgap of the generated materials was predicted using a graph neural network model while the band edge positions were obtained via empirical correlations. Although our generative modeling framework was used to discover novel 2D photo-catalysts for water splitting reaction, it is generic in nature and can be used directly to discover novel materials for other applications as well.

中文翻译：

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用于太阳能水分解的二维材料的数据驱动发现

氢经济，其中氢被用作运输和能源部门的燃料，在减轻全球变暖的有害影响方面具有重要的前景。利用阳光进行光催化分解水可能是生产氢燃料的最清洁方式。在各种其他因素中，该技术的广泛采用主要受到缺乏高效催化剂材料的阻碍。二维材料已显示出作为用于水分解的高效光催化剂的显着前景。包含 2D 材料“计算”特性的开放数据库的可用性和深度学习的进步现在使我们能够对这些用于水分解的 2D 光催化剂进行“逆向”设计。我们使用一个这样的数据库（Jain 等人，ACS Energy Lett. 2019, 4, 6, 1410-1411）建立一个用于发现新型二维光催化剂的生成模型。材料的结构被转换为基于 3D 图像的表示，用于训练细胞、基础自动编码器和分割网络，以确定晶格参数以及来自图像的原子位置。随后，使用单元和基础编码来训练条件变分自编码器 (CVAE) 以学习潜在空间中材料的连续表示。然后对 CVAE 的潜在空间进行采样以生成几种新的 2D 材料，这些材料可能是有效的水分解光催化剂。生成材料的带隙是使用图神经网络模型预测的，而带边位置是通过经验相关性获得的。