Thermodynamics
These notes were authored by Agostino Patella with the help of Claude, an AI assistant by Anthropic.
Preface
There are lecture notes for the course Thermodynamics for Bachelor’s students in Physics at the Humboldt-Universität zu Berlin, taught by Prof. Dr. Agostino Patella, with the support of Lukas Holan.
Suggested books
For further reading the following textbooks are recommended:
Downloading the PDF
A PDF version of these notes can be downloaded using the icon in the top navigation bar. Note that the HTML version contains animations and videos that cannot be reproduced in PDF; this content may be replaced by static figures or omitted in the PDF version.
These lecture notes are a work in progress and are updated throughout the semester. You should re-download the PDF regularly. Each chapter carries a Last updated annotation just below its title, so you can tell at a glance whether you have the current version.
German translation
A German translation of these notes is available. To switch to it, click the flag icon in the top navigation bar. The translation was produced automatically using the DeepL translation service and has received only limited review. Readers who notice errors or awkward phrasing in the German version are encouraged to contact the author.
A note on AI tools
The following paragraph was written by Claude.
AI assistants can be a valuable companion in learning physics, but only if used in a way that builds understanding rather than substituting for it. The most productive uses are dialogic: ask an AI to re-explain a concept in different words, to suggest an analogy, to check whether your reasoning in a derivation is sound, or to help you formulate a question you cannot yet articulate precisely. Used this way, an AI acts as an infinitely patient interlocutor — one you can interrupt, challenge, and ask to start over as many times as you need. What AI cannot do is replace the slow, effortful work of constructing understanding yourself. Reading a clear explanation and genuinely understanding it are not the same thing; neither are watching a derivation and being able to reproduce it. The test is always whether you can solve a problem you have not seen before. Use AI to accelerate the parts of learning that are mechanical, to get unstuck when you are stuck, and to sharpen your thinking — but do the hard parts yourself.
Course structure
| Part | Lectures | Theme |
|---|---|---|
| I | 1–4 | Foundations: systems, temperature, the First Law, ideal and real gases |
| II | 5–7 | The Second Law, Carnot’s theorem, entropy |
| III | 8–9 | Thermodynamic potentials, Maxwell relations, equilibrium |
| IV | 10–12 | Phase transitions, chemical equilibrium, heat engines |
| V | 13–14 | Radiation thermodynamics, synthesis |