机器学习应用用于热化学和运动性质预测
Lowie Tomme1, Yannick Ureel1, Maarten R Dobbelaere1
1Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Gent, Belgium.
概括
机器学习可以预测化学过程的热化学和运动性质. 提高数据质量和开发数据效率高的方法是推动机器学习在详细的动力建模中的关键.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 详细的运动模型对于理解和优化化学过程至关重要.
- 准确的热力学和运动性质是这些模型的基础.
- 对这些特性进行实验或量子化学确定是具有挑战性的.
研究的目的:
- 审查使用机器学习来预测动力建模的热化学和动力特性方面的进展.
- 评估目前用于财产预测的机器学习的最新状态.
- 确定机器学习在这个领域的挑战和未来方向.
主要方法:
- 关于用于热化学和运动性质预测机器学习的最新文献的综述.
- 专注于机器学习的数据,表示和模型方面.
- 评估机器学习技术,以有效利用数据.
主要成果:
- 机器学习显示了在气相,液相和催化过程中预测属性的前景.
- 机器学习成功的关键方面包括数据质量,表示策略和模型架构.
- 当前的机器学习应用程序往往受高质量的数据的可用性限制.
结论:
- 高质量的数据是将机器学习应用于详细的动力模型的关键瓶.
- 产生大量高质量的数据集对于进步至关重要.
- 进一步开发数据效率高的机器学习技术是必要的,以提高模型的性能和适用性.
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