# Molecular and Materials Modelling *A Computational Materials Science Notebook Companion* — by Raymond Amador. This is a companion to the graduate course **Molecular and Materials Modelling**, taught at ETH Zürich. It teaches the methods of computational materials science — Monte Carlo and molecular dynamics, electronic structure, reaction paths, and machine-learning descriptors — *by computation and visualisation*: every idea is something you run, plot, and check, not just read. ## How these notebooks are built Each notebook is self-contained and follows the same shape, so the series reads as one coherent work: - a short **theory in brief** — a genuine review of the governing equations, not a textbook chapter, with full derivations deferred to the literature; - a sequence of **guided exercises**, each stating exactly what to compute and why, with a labelled setup figure where a physical configuration is described; - an independent **validation** at the end of every exercise. This is the rule that matters most: a result is not finished until it is checked against something the computation did *not* assume — an exact limit, a conservation law, a closed-form result (Onsager's $T_c$), or an exact enumeration. The checks print a ✓/✗ line and *fail the build* if the physics is wrong, so the published site is trustworthy by construction. A word on reading a check: a ✗ means the output did not match what the validation expected. That can be a real error, a different-but-valid convention, or simply Monte Carlo noise against a tight tolerance — so treat it as a prompt to locate the discrepancy, never an automatic verdict. We also validate only what the code *honestly* reproduces; where a phenomenon needs physics beyond a clean, fast implementation, we show it qualitatively and say what is missing rather than overclaim. Animations appear only where motion or evolution is the point; figures and equations are numbered continuously and cross-referenced ("Fig. 7", "Eq. 12"). Where the physics is dimensionless we use natural units and say so. ## How to use them Every page has a **download** button and **launch** buttons (Binder/Colab) in the toolbar — take a notebook and run it yourself. The published notebooks ship **without the worked solutions**: you see each problem, the resulting figures, and the validation outcomes, but not the answer code. If you would like the reference solutions — to teach from, or to check your own work — the contact is in the footer of any page. ## Organisation, and continuity with the course The notebooks are grouped **thematically** into volumes rather than in strict lecture order, because related methods (say, Monte Carlo in equilibrium and in real time) belong together. Continuity with the original course is preserved a different way: **every notebook states the lecture it develops, with the number explicit**, both in its banner and in its overview. The FS 2023 course numbered its lectures and exercises together (Lecture/Exercise 1 … 13); the full map lives in `manifest.yml`, and each volume's landing page lists the lectures it draws on. ## Acknowledgement and provenance These materials are based on the lecture and exercise materials of *Molecular and Materials Modelling* at **ETH Zürich and Empa (FS 2023)**, developed by **Prof. Dr. Daniele Passerone** (lectures), **Dr. Carlo Pignedoli**, and the author (exercises) — the author having led the exercise sessions for three years. Each notebook is *largely, though not always verbatim,* based on that material: where a topic was Dr. Pignedoli's, the author adopts, reimplements, and rephrases it; elsewhere the author's exercises develop Prof. Passerone's lectures. Here it is all synthesised, expanded, and restyled into a single companion. The same credit appears in the footer of every page. To cite this companion, use its DOI, [10.5281/zenodo.21418786](https://doi.org/10.5281/zenodo.21418786) (all versions; full metadata in the repository's `CITATION.cff`). ## A note on how this companion was written These notebooks were written in collaboration with a generative AI assistant (Anthropic's Claude), and the fact belongs in the open, in a series whose central rule is that nothing is trusted unchecked. The scientific content and its provenance are exactly as credited above. The design of the companion is the author's: the thematic organisation, the fidelity-first rule that the course's real simulation inputs and outputs are shown and analysed rather than reinvented, the validation discipline, and the per-notebook specifications from which every page was built. The assistant drafted prose and code to those specifications; the author reviewed, corrected, and approved every page, and the final word on each is a human's. And the rule that matters most applies to the authors too: neither was taken on trust — every exercise ends by checking its result against something the computation did *not* assume, so the reader never has to take anyone's word, human or machine, for a single number. ## Volumes ```{tableofcontents} ``` ```{raw} html ```