Statistical Thermodynamics Theory, Computation, and Molecular Applications: A Computational Approach with Python

Statistical Thermodynamics: Theory, Computation, and Molecular Applications presents a modern, computation-driven approach to one of the most fundamental subjects in physics, chemistry, and engineering. Bridging microscopic molecular behavior with macroscopic thermodynamic properties, this book provides a rigorous yet accessible treatment of statistical thermodynamics through analytical derivations, physical interpretation, and practical scientific computing.

Designed for senior undergraduate students, graduate students, researchers, and practicing engineers, the book develops the foundations of statistical mechanics from first principles, introducing probability theory, kinetic theory of gases, molecular interactions, partition functions, classical and quantum statistics, chemical equilibrium, imperfect gases, phase transitions, and the statistical thermodynamics of solids.

A distinctive feature of this book is its strong emphasis on computational statistical thermodynamics . Every chapter includes three carefully developed Python examples that progress from analytical solutions to advanced computational applications. Readers learn not only the underlying theory but also how to implement numerical algorithms, evaluate thermodynamic properties, visualize molecular behavior, and verify computational results using modern scientific programming techniques.

Topics include:

Foundations of Statistical Thermodynamics Kinetic Theory of Gases Intermolecular Potentials Maxwell–Boltzmann, Bose–Einstein, and Fermi–Dirac Statistics Partition Functions and Thermodynamic Properties Monatomic, Diatomic, and Polyatomic Ideal Gases Quantum Statistical Thermodynamics Chemical Equilibrium and Imperfect Gases Statistical Thermodynamics of Solids Phase Transitions and Critical Phenomena Computational Statistical Thermodynamics with Python Each chapter combines:

Rigorous mathematical derivations Physical interpretation of statistical concepts Complete and executable Python programs Numerical integration and Monte Carlo methods High- and low-temperature approximations Computational verification and convergence studies Publication-quality figures and visualizations Solved examples and engineering applications End-of-chapter exercises and programming projects Using only NumPy , SciPy , and Matplotlib , readers can reproduce every computational example and gain hands-on experience with modern scientific computing techniques widely used in academia and industry.

Whether used as a university textbook, a graduate-level reference, or a self-study guide, Statistical Thermodynamics: Theory, Computation, and Molecular Applications equips readers with both the theoretical foundation and the computational skills needed to understand, analyze, and model thermodynamic systems from the molecular scale to real-world engineering applications.

Ideal for students and professionals in Mechanical Engineering, Chemical Engineering, Materials Engineering, Physics, Chemistry, Aerospace Engineering, Energy Systems, and Computational Science, this book provides a comprehensive and verification-oriented approach to modern Statistical Thermodynamics.