Crystalline Material Discovery in the Era of Artificial Intelligence

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for efficient and accurate materials discovery. These…

• NS
  Natural Science Foundation of Xiamen City

  Award: 3502Z202571027
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2024A1515010031
• FR
  Fundamental Research Funds for the Central Universities

  Award: 20720250164