通过矩阵完成预测混合物中的对相互作用
Marco Hoffmann1, Nicolas Hayer1, Maximilian Kohns1
1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany. fabian.jirasek@rptu.de.
Physical chemistry chemical physics : PCCP
|July 8, 2024
概括
本研究介绍了一种机器学习方法,用于预测分子模拟的相互作用参数. 这提高了预测混合物属性的准确性,比如对新系统的亨利定律常数.
科学领域:
- 计算化学是一种计算化学.
- 物理化学 物理化学
- 化学工程是化学工程的组成部分.
背景情况:
- 分子模拟利用相互作用模型和结合规则预测混合物特性.
- 没有实验数据调整,现有的组合规则往往缺乏准确性.
- 可调节的二进制参数 (ξij) 用于改善混合物中不同相互作用的预测.
研究的目的:
- 开发一种用于预测未经研究的混合物的可调二进制参数 (ξij) 的新方法.
- 为了提高分子模拟对混合物特性的准确性.
- 为不同分子间相互作用的描述提供预测性访问.
主要方法:
- 使用来自机器学习 (ML) 的矩阵完成方法 (MCM).
- 应用了该方法来预测XXij对亨利定律常数的分子模拟.
- 证明了对未经研究的混合物的 ξij 的预测,准确度很高.
主要成果:
- 基于MCM的方法准确地预测了未经研究的混合物的 ξij.
- 使用预测的 ξij 与默认值 (ξij = 1) 相比,大大提高了亨利定律常数预测的准确性.
- 该方法显示混合物属性的预测精度大幅增加.
结论:
- 开发的基于ML的MCM为预测混合物中的分子间相互作用提供了强大的工具.
- 这种方法提高了分子模拟对各种混合物特性的可靠性.
- 通用方法可转移到其他混合物属性和状态方程.
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