Quantitative Predictive Theories through Integrating Quantum, Statistical, and Irreversible Thermodynamics

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. The combined law of thermodynamics derived by Gibbs laid the foundation of thermodynamics though only applicable to equilibrium or freezing-in systems. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system, which was extended to quantum mechanics-based statistical thermodynamics by Landau, while the irreversible thermodynamics was derived by Onsager and Prigogine. The development of density function theory (DFT) by Kohn enabled the quantitative prediction of properties of the ground state of a system from quantum mechanics. In this presentation, we will present our theories that integrate quantum, statistical, and irreversible thermodynamics in a coherent framework by utilizing the predicative capability of DFT to revise the statistical thermodynamics (zentropy theory) and by keeping the entropy production due to irreversible processes in the combine law of thermodynamics to derive flux equations (theory of cross phenomena). It will be demonstrated that the zentropy theory is capable of predicting the free energy landscape as a function of internal degrees of freedom of a system including singularity and instability at critical point and emergent positive and negative divergences of properties, while the theory of cross phenomena can predict the coefficients of internal processes between conjugate variables (direct phenomena) and non-conjugate variables (cross phenomena) in the combined law of thermodynamics, both with inputs from DFT-based calculations only and without fitting parameters, as shown in this publication https://doi.org/10.1080/21663831.2022.2054668.