可微热力学模拟
Differentiable thermodynamic modeling
摘要：
通过引入可微规划的概念，提出了一种新的热力学建模框架，其中包括热化学量和相平衡的所有热力学观测量都可以相对于底层模型参数进行微分，从而允许通过基于梯度的优化来学习模型。结果表明，热力学建模和深度学习可以在这个框架内无缝集成和统一。初步成功地应用于Cu-Rh体系。预计深度学习风格的热力学建模可以提高模型的预测能力，并通过学习各种类型的数据为具有复杂化学性质的多组分材料的设计，合成和优化提供更有效的指导。
英文摘要：
A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemical quantities and phase equilibria can be differentiated with respect to the underlying model parameters, thus allowing the models learned by gradient-based optimization. It is shown that thermodynamic modeling and deep learning can be seamlessly integrated and unified within this framework. A preliminary successful application is demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling in a deep learning style can increase prediction power of models, and provide more effective guidance for design, synthesis and optimization of multi-component materials with complex chemistry via learning various types of data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
