低融盐的低融效应的计算设计:一种结合机器学习和热力学建模方法
Ashwin Ravichandran1, Shreyas Honrao1, Stephen Xie1
1KBR Inc., Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States.
The journal of physical chemistry letters
|December 26, 2023
概括
我们开发了一个计算模型来预测融盐的融化温度. 这种方法选了数百万种混合物,确定了低点的设计规则.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 预测融盐的融化行为对于各种工业应用至关重要.
- 预测融温度和融成分的现有方法在范围和准确性上往往是有限的.
- 设计具有所需性质的新溶盐混合物需要有效地探索广的组合空间.
研究的目的:
- 开发一个计算框架,准确预测融盐的融温度.
- 选大量潜在的菌混合物,并识别那些具有低点的混合物.
- 制定设计规则,用于制造低点的优质混合物.
主要方法:
- 将热力学和机器学习模型结合起来,以预测eutectic融温度 (T_eut).
- 数以百万计的二进制到六进制的组合式eutectics的高通量虚拟选.
- 分析分子大小异质性和混合物配置对T_eut的影响.
- 开发初步模型来预测欧特克组合物.
主要成果:
- 在预测T_eut.中获得了大约6%的准确性 (平均绝对百分比误差).
- 确定分子大小和混合物成分数量的异质性是低T_eut的关键因素.
- 成功选了数百万种潜在的菌混合物和拟议的设计规则.
- 开发初步模型来预测eutectic组合,解决一个以前具有挑战性的方面.
结论:
- 开发的计算框架能够准确地预测和高通量选融盐欧特克混合物.
- 基于分子大小异质性和配置的设计规则可以指导低T_eut混合物的开发.
- 这种方法通过有效地导航设计空间,显著加快了针对特定应用的新型盐混合物的发现.
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