Tripartite interaction representation algorithm for crystal graph neural networks.

ABSTRACT: Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In the field of materials science, there is a current research emphasis on exploring techniques for characterizing material structures to achieve precise descriptions. This paper proposes a crystal graph convolution neural network model that incorporates a tripartite interaction approach. The model not only incorporates atomic information, bond lengths, and bond angles but also offers a method for updating atoms and bond lengths, facilitating accurate descriptions of crystal structures by capturing implicit structural information. Focusing on predicting the formation energy of crystalline compounds, our results demonstr

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC11496532 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature