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Graph-based discovery and analysis of atomic-scale one-dimensional materials.

Shunning LiZhefeng ChenZhi WangMouyi WengJianyuan LiMingzheng ZhangJing LuKang XuFeng Pan
Published in: National science review (2022)
Recent decades have witnessed an exponential growth in the discovery of low-dimensional materials (LDMs), benefiting from our unprecedented capabilities in characterizing their structure and chemistry with the aid of advanced computational techniques. Recently, the success of two-dimensional compounds has encouraged extensive research into one-dimensional (1D) atomic chains. Here, we present a methodology for topological classification of structural blocks in bulk crystals based on graph theory, leading to the identification of exfoliable 1D atomic chains and their categorization into a variety of chemical families. A subtle interplay is revealed between the prototypical 1D structural motifs and their chemical space. Leveraging the structure graphs, we elucidate the self-passivation mechanism of 1D compounds imparted by lone electron pairs, and reveal the dependence of the electronic band gap on the cationic percolation network formed by connections between structure units. This graph-theory-based formalism could serve as a source of stimuli for the future design of LDMs.
Keyphrases
  • convolutional neural network
  • small molecule
  • electron microscopy
  • high throughput
  • single cell
  • deep learning
  • machine learning
  • neural network
  • genome wide
  • solar cells
  • ionic liquid