RESUMO
Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works.
RESUMO
Effective separation of photoexcited carriers and chemisorption of the N2 molecule are two key issues to efficient nitrogen photofixation. The spatial charge separation of BiVO4 with anisotropic exposed facets, namely the transfer of photoexcited electrons and holes to {010} and {110} facets, respectively, helps to enhance the separation ability of photogenerated carriers. Theoretical calculation results predict that a surface oxygen vacancy is easier to form on the (010) facet than on the (110) facet of BiVO4. Accordingly, in this study, enhanced N2 photofixation performance has been achieved for the first time by tuning the exposure of {010} facets of BiVO4.
