scientific-visualization

Scientific Visualization

Description

This skill covers publication-quality visualization for computational materials science, chemistry, atomistic simulation, electronic structure, and scientific ML: plotting scalar observables with matplotlib, rendering atomic structures and trajectories with OVITO, ASE, pymatgen, VESTA, nglview, and VMD, and building reproducible figure pipelines for papers, talks, reports, and model diagnostics. Invoke this skill when converting validated simulation or ML results into figures, movies, notebooks, or report artifacts, or when auditing whether a visualization could mislead through units, color scales, cherry-picked frames, or non-reproducible preprocessing.

Domain Context

Scientific visualization is not decoration; it is a measurement interface. A plot or rendering encodes choices about units, normalization, sampling, smoothing, color scale, camera angle, periodic wrapping, and what data were excluded. In computational materials science, those choices can change the scientific conclusion as strongly as a simulation parameter.

Atomistic visualizations depend on the representation of coordinates and topology. A crystal structure can be shown as a conventional cell, primitive cell, supercell, slab, molecule, interface, or trajectory frame. Wrapped coordinates may make a diffusing ion appear stationary; unwrapped coordinates may make a compact periodic crystal appear split across cell boundaries. A surface slab must show vacuum and exposed facets clearly enough that viewers can distinguish surface atoms from bulk-like atoms. A defect rendering must identify the reference lattice, otherwise a colored per-atom scalar field can be mistaken for a defect classification.

Scalar plots have their own physical constraints. RDFs should approach 1 at large distance for homogeneous liquids; MSDs should be plotted on log-log and linear axes when diffusion regimes matter; temperature and pressure traces require equilibration trimming before averages are annotated; parity plots for ML potentials should preserve equal aspect ratio so slope errors are visible. Band structures and DOS require a consistent energy zero, usually the Fermi level or valence-band maximum. Phonon dispersions require negative frequencies to be displayed rather than hidden, because imaginary modes are often the result.