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Updated: Jan 23, 2026

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热稳定性预测由实验和理论层面的协同深度学习为纳米体提供动力

Jun Mao1, Yuanpeng Song1, Ming Kong1

  • 1College of Chemistry, Sichuan University, Chengdu 610064, China.

ACS applied materials & interfaces
|January 21, 2026
PubMed
概括

我们开发了一种双级深度学习策略,以预测纳米体的热稳定性,克服数据稀缺性. 这种方法增强了纳米体查的实际应用.

关键词:
深度学习是一种深度学习.语言模型 语言模型分子动力学模拟模型一个纳米人体.热稳定性 热稳定性网络服务器的Web服务器.

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