在人工智能时代发展光电子材料的前景
Ting Yuan1, Xianzhi Song1, Yuxin Shi1
1College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China.
Chemistry, an Asian journal
|February 6, 2024
概括
机器学习 (ML) 加快了新型光电子材料的发现,这对于先进的显示器和照明至关重要. 本综述详细介绍了ML工作流程和应用程序,为高性能光电子设备铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 光电学是指光电子产品.
- 机器学习 机器学习
背景情况:
- 光电子设备,如LED,由于其效率和质量,对显示器和照明至关重要.
- 开发这些设备的新功能材料传统上是缓慢的,资源密集的.
- 机器学习 (ML) 提供了一种有希望的方法来加快材料发现和性能提升.
研究的目的:
- 审查机器学习在光电子材料的发现工作流程.
- 突出 ML 在各种光电子功能材料中的近期应用.
- 讨论 ML 辅助光电子材料设计的挑战和未来方向.
主要方法:
- 对光电子材料进行数据收集和预处理.
- 特性工程,以有效地表示材料属性.
- 在材料发现工作流程中选择,评估和应用模型.
主要成果:
- 在发现和增强半导体量子点 (QD),矿QD,有机分子和基于碳的纳米材料方面展示了ML应用.
- 总结了从数据到材料应用的端到端ML工作流程.
- 确定了ML显著加速设计周期的关键领域.
结论:
- 机器学习是加速发现高性能光电子材料的强大工具.
- 克服当前的挑战将释放ML在光电子学中的全部潜力.
- 跨学科的合作对于在光电子设备中以机器学习为指导的创新至关重要.
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