pyzentropy: A Python package implementing recursive entropy for first-principles thermodynamics

TL;DR

This paper introduces pyzentropy, an open-source Python package that applies recursive entropy concepts to first-principles thermodynamics, enabling detailed analysis of material properties.

Contribution

The work provides the first computational tool implementing recursive entropy in thermodynamics, demonstrated through a case study on Fe3Pt.

Findings

Reproduced Invar behavior and anomalous thermal expansion in Fe3Pt

Constructed phase diagrams aligning with experimental data

Highlighted importance of high-probability configurations for accuracy

Abstract

While the recursive property of entropy is well known in information theory, it is rarely utilized in thermodynamics, despite entropy originating in this field. Moreover, computational tools to implement this concept within first-principles thermodynamics remain lacking. In this work, we introduce an open-source Python package, pyzentropy, to implement this approach. We demonstrate its effectiveness using $Fe_3Pt$ as a case study, considering a 12-atom supercell with multiple magnetic configurations. By applying the recursive formulation of entropy to compute the total entropy of the system, we reproduce the Invar behavior, along with the anomalous temperature dependence of the linear coefficient of thermal expansion (LCTE), heat capacity $C_P$, and bulk modulus $B$. We also construct the $T$-$V$ and $P$-$T$ phase diagrams in good agreement with experimental observations. Finally, we highlight the importance of determining key high-probability configurations to accurately capture material properties.