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走向一种可概括的盐混合物密度预测模型,使用化学信息转移学习
Julian Barra1, Shayan Shahbazi2, Anthony Birri3
1Department of Chemical Engineering, University of Massachusetts Lowell, 1 University Ave, SOU-202E, Lowell, MA, 01854, USA.
概括
设计盐应用需要准确的热物理数据. 经过转移学习训练的深度神经网络 (DNN) 可以高准确度地预测盐密度,克服数据缺口并提高概括性.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 盐的精确热物理性质对于设计应用至关重要.
- 现有的数据库有数据缺口,实验测量由于高温和腐蚀性而具有挑战性.
- 当前的Redlich-Kister (RK) 模型缺乏通用性,因为它们需要每个新系统的子组件数据.
研究的目的:
- 为了解决数据稀疏性,并提高预测盐密度的概括性.
- 开发一种使用深度神经网络 (DNN) 进行盐性能预测的新方法.
- 在盐密度预测中超越现有的方法.
主要方法:
- 使用转移学习程序来训练深度神经网络 (DNN).
- 通过使用半经验关系 (RK模型),盐热性质数据库和JARVIS数据库描述符的组合来训练DNN.
- 这些模型预测了融盐的密度.
主要成果:
- 深度神经网络 (DNN) 实现了超过0.99.99的R平方值.
- 对于DNN预测的平均绝对百分比误差低于1%.
- 与替代方法相比,DNN方法显示出更高的性能.
结论:
- 提出的DNN转移学习方法有效地预测了融盐的密度.
- 这种方法克服了现有模型的局限性,通过提高概括性和解决数据稀疏性.
- 这些发现为更准确,更高效的盐应用设计铺平了道路.
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