Learning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems

Key points

• Complex chemical systems containing heterogeneous substructures are common in real-world applications.1
• However, existing graph representation learning methods for inter-graph interactions assumed graph-level interactions between homogeneous structures, such as organic molecules and inorganic crystalline materials.1
• We applied the proposed method to predict the physical properties of hybrid solar cell materials containing heterogeneous substructures, which have received significant attention for sustainable energy resources.1
• By learning heterogeneous inter-graph interactions, the proposed method achieved state-of-the-art accuracy in predicting band gaps of 1,682 hybrid solar cell materials.1

Sources (1)

• [1]Learning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:27 AM

  We propose a data descriptor of the complex chemical systems and a graph neural network for learning inter-graph interactions between organic and inorganic compounds.

  Complex chemical systems containing heterogeneous substructures are common in real-world applications.

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