Data-Driven Discovery of 2D Materials for Solar Water Splitting
نویسندگان
چکیده
Hydrogen economy, wherein hydrogen is used as the fuel in transport and energy sectors, holds significant promise mitigating deleterious effects of global warming. Photocatalytic water splitting using sunlight perhaps cleanest way producing fuel. Among various other factors, widespread adoption this technology has mainly been stymied by lack a catalyst material with high efficiency. 2D materials have shown efficient photocatalysts for splitting. The availability open databases containing “computed” properties advancements deep learning now enable us to do “inverse” design these We use one such database (Jain et al., ACS Energ. Lett. 2019, 4, 6, 1410–1411) build generative model discovery novel photocatalysts. structures were converted into 3D image–based representation that was train cell, basis autoencoder segmentation network ascertain lattice parameters well position atoms from images. Subsequently, cell encodings conditional variational (CVAE) learn continuous latent space. space CVAE then sampled generate several new likely be bandgap generated predicted graph neural while band edge positions obtained via empirical correlations. Although our modeling framework discover reaction, it generic nature can directly applications well.
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ژورنال
عنوان ژورنال: Frontiers in Materials
سال: 2021
ISSN: ['2296-8016']
DOI: https://doi.org/10.3389/fmats.2021.679269