人工智能驱动的发现和分子工程设计,以提高黑的界面稳定性
Chao Peng1,2, Bing Wang1, Lie Wu1,3
1Materials Artificial Intelligence Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen, 518055, P.R. China.
Angewandte Chemie (International ed. in English)
|August 9, 2025
概括
人工智能加速了用于稳定二维半导体黑 (BP) 的新型分子结构的发现. 这种方法提高了材料的耐用性,使先进电子技术的新应用成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 计算化学计算化学
背景情况:
- 分子工程是先进界面材料的关键,但复杂的有机分子阻碍了最佳结构的发现.
- 黑 (BP) 是一种二维半导体,在暴露于环境氧气和水分后会迅速降解,这限制了其实际应用.
研究的目的:
- 加速发现和设计用于增强黑 (BP) 界面稳定性的分子.
- 开发一种高通量工作流程,用于使用人工智能预测和选候选分子.
主要方法:
- 利用像GPT-4o这样的大型语言模型 (LLM) 来识别BP相互作用的有效分子组.
- 在高通量工作流中使用图形神经网络 (GNN) 来选超过1.17亿个分子.
- 通过密度函数理论 (DFT) 模拟和实验合成,使用人工智能引导的协议验证了有前途的候选人.
主要成果:
- 确定了662个有前途的分子候选者来稳定血压.
- 在环境条件下达到BP的显著界面稳定,持续长达24天.
- 提出了一种协同分子工程策略,使得水友分子能够用于BP稳定.
结论:
- 包括LLM和ML在内的AI技术可以显著优化BP界面稳定性.
- 由人工智能驱动的开发方法为各种材料的分子工程提供了一个强大的平台.
- 这项研究克服了设计用于敏感二维材料的稳定分子的传统局限性.
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