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<p>Machine learning (ML) has emerged to play a more and more important role in material science. Here, we develop a platform named Darwin4Matter that integrates machine learning and quantum chemistry for new materials discovery. The framework consists of six steps: quantum chemistry prediction, Δ-machine learning correction, molecular augmentation, machine learning prediction, molecular production, and verification. Using this platform, we start from a very small dataset and design three new functional molecules with high refractive indexes in the visible spectrum, which serves as the capping layer of organic light-emitting diode devices for improving light extraction efficiency. The superior performance over currently used materials has been verified experimentally, exhibiting significant commercial value in the field of advanced display materials.</p>

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Darwin4Matter: A Platform Integrating Machine Learning and Quantum Chemistry for New Materials Design

  • 期刊论文

  • 发表日期：

    2025年9月17日

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  • Hui Rong,
  • Yili Chen,
  • Shubo Zhang,
  • Yue Chen,
  • Lin Shen,
  • Wei-Hai Fang

摘要

Machine learning (ML) has emerged to play a more and more important role in material science. Here, we develop a platform named Darwin4Matter that integrates machine learning and quantum chemistry for new materials discovery. The framework consists of six steps: quantum chemistry prediction, Δ-machine learning correction, molecular augmentation, machine learning prediction, molecular production, and verification. Using this platform, we start from a very small dataset and design three new functional molecules with high refractive indexes in the visible spectrum, which serves as the capping layer of organic light-emitting diode devices for improving light extraction efficiency. The superior performance over currently used materials has been verified experimentally, exhibiting significant commercial value in the field of advanced display materials.