基于物理的神经网络与组贡献方法
Mohammad Reza Babaei1, Ryan Stone1, Thomas Allen Knotts Iv1
1Department of Chemical Engineering, Brigham Young University, Provo, Utah 84602, United States.
Journal of chemical theory and computation
|June 9, 2023
概括
这项研究引入了基于物理学的神经网络 (PINNs),以准确预测表面张力和沸点等热物理性质,改进了传统和机器学习方法,以便更好地推断.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 对热物理性质的准确预测对于化学工程和工业应用至关重要.
- 现有的预测方法,包括传统和机器学习方法,往往存在重大错误和差异推断能力.
- 许多有机化合物的实验数据由于成本,安全性或程序性挑战而稀缺.
研究的目的:
- 开发改进的方法来预测有机化合物的热物理性质.
- 通过整合基于物理的约束来增强深度学习模型的推断能力.
- 以物理信息的神经网络 (PINNs) 作为案例研究,以表面张力和正常沸点作为案例研究来证明性能预测的有效性.
主要方法:
- 开发了一种多层物理信息神经网络 (PINN),用于预测对角 (与表面张力相关).
- 利用在1600个化合物的数据集上训练的PINN来预测正常沸点.
- 将化学和物理原理纳入神经网络训练过程中.
- 在训练,验证和测试集中确保了化合物类型的平衡划分.
主要成果:
- 通过结合基于物理的约束,PINN展示了对深度学习模型的提取能力的改进.
- 对于正常沸点预测,PINN在训练数据上达到6.95°C,在测试数据上达到11.2°C的平均绝对误差.
- 限制组贡献是积极的,进一步改善了测试集的预测.
结论:
- 基于物理学的神经网络 (PINNs) 提供了一种有前途的方法来提高热物理性质的预测准确性和推断.
- 将基于物理学的约束纳入机器学习模型是克服传统方法局限性的关键.
- 开发的PINN方法显示了在研究案例之外改善各种热物理性质预测的巨大潜力.
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