用迁移学习精准预测熔盐密度，比传统方法更准更通用。
Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning
- 融合红里奇-基斯特模型与量子计算属性，构建化学信息引导的迁移学习框架。
- 预测准确率r²超0.99，平均绝对百分比误差低于1%。
- 适合材料设计、能源存储领域研究者快速获取熔盐物性数据。
优化熔盐应用设计需要了解其热物理性质，但现有数据库不完整，实验又困难。理想混合模型和红里奇-基斯特模型计算成本低，但或精度不足或泛化能力差。为此，提出一种结合红里奇-基斯特模型、实验数据和从头算性质的深度神经网络迁移学习方法。该方法在熔盐密度预测上表现优异，r² > 0.99，MAPE < 1%，显著优于现有方法。
原文摘要 · Abstract (English)
Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives.
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