热力学一致的图神经网络
Thermodynamics-Consistent Graph Neural Networks
AI总结：
提出热力学一致的图神经网络GE-GNN，通过预测过量吉布斯自由能并利用自动微分获得活度系数，无需额外损失项，实现二元混合物活度系数的高精度与热力学一致性预测。
摘要：
我们提出了过量吉布斯自由能图神经网络（GE-GNNs），用于预测二元混合物的组分依赖活度系数。GE-GNN架构通过预测摩尔过量吉布斯自由能，并利用热力学关系式获得活度系数，从而确保热力学一致性。由于这些关系是微分关系，我们采用自动微分以端到端方式学习活度系数。由于该架构基于基础热力学，我们无需额外的损失项来学习热力学一致性。由于输出是基础性质，我们也不施加热力学建模限制和假设。我们展示了活度系数预测的高准确性和热力学一致性。
英文摘要：
We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free energy and using thermodynamic relations to obtain activity coefficients. As these are differential, automatic differentiation is applied to learn the activity coefficients in an end-to-end manner. Since the architecture is based on fundamental thermodynamics, we do not require additional loss terms to learn thermodynamic consistency. As the output is a fundamental property, we neither impose thermodynamic modeling limitations and assumptions. We demonstrate high accuracy and thermodynamic consistency of the activity coefficient predictions.
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。
