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SkillsMP has collected 21 skills from WuGroup-XJTLU/cc-skills-ZhenghaoWu-Group. Open a skill to review its source and details.
Showing 21 of 21 collected skills.
Use when manipulating atomistic structures, converting between file formats (CIF, POSCAR, XYZ, LAMMPS data), building supercells and surfaces, or running small geometry optimizations with ASE-supported calculators.
Use when optimizing material compositions, polymer sequences, molecular structures, or any expensive-to-evaluate property where each evaluation requires simulation or experiment. Also use when the user mentions Bayesian optimization for materials, black-box…
Use when writing, reviewing, or debugging CP2K input files, choosing DFT methods (GPW, GAPW, functionals, basis sets), setting up calculations (geometry optimization, AIMD, cell optimization, NEB, single-point energy), validating parameters (cutoff, SCF…
Use when code imports `freud` or `freud-analysis`, or user asks to analyze molecular simulation trajectories (RDF, MSD, order parameters, structure factor, PMFT, Voronoi, clustering). Also use when reading LAMMPS dump/data files for freud analysis via…
Use when setting up and running biomolecular MD simulations with GROMACS. Generate topologies, write MDP input files, execute simulations, and perform basic analysis.
Use when writing, reviewing, or debugging LAMMPS input scripts, choosing simulation methods (deformation, equilibration, transport), validating physics parameters (timestep, thermostat, units), or encountering LAMMPS runtime errors (lost atoms, DOF conflicts,…
Use when importing `lammpsio`, reading/writing LAMMPS data/dump files, or converting between LAMMPS and GSD formats.
Use when running machine-learning potential calculations with MACE. Load pretrained MACE-MP models, compute energies and forces, and run geometry optimizations on atomistic structures.
Use when querying the Materials Project database for crystal structures, material properties, or generating phase diagrams. Requires the MP_API_KEY environment variable to be set.
Use when creating matplotlib plots from scratch, customizing plot elements at a low level, or building novel visualization types that seaborn does not support.
Use when code imports `ovito`, or user asks to render molecular snapshots / animations from LAMMPS, XYZ, GSD, PDB, CIF or POSCAR files. Covers headless rendering (Tachyon/OSPRay), coloring, PBC handling, bonds, camera setup, and publication-ready output.
Use when writing, reviewing, debugging, or explaining packmol input files and packing strategies for molecular dynamics simulations.
Use when creating polymer systems, generating LAMMPS data files from SMILES, building atomistic or coarse-grained bead-spring models, setting up polymer simulations (homopolymer, copolymer, ring polymer, polymer solutions), or when user mentions AutoPoly,…
Use when working with crystal structures in Python, converting materials file formats (CIF, POSCAR, XYZ), querying the Materials Project database, or computing phase diagrams and electronic structure properties.
Use when running molecular quantum chemistry calculations in Python. Computes single-point energies, optimized geometries, vibrational frequencies, and population analysis using HF, DFT, or MP2 methods.
Use when parsing SMILES/SDF files, calculating molecular descriptors or fingerprints, performing substructure searches with SMARTS, or generating 2D/3D coordinates in Python.
Use when creating publication-quality matplotlib figures in Python — scientific line plots, scatter, error bars, heatmaps, contour, bar, histogram, 3D, multi-panel, log-scale, twin/inset axes, and colorbars. Applies APS journal styling (Helvetica, 8pt, inward…
Use when training classification or regression models, performing clustering or dimensionality reduction, tuning hyperparameters, or building preprocessing pipelines in Python.
Use when creating statistical visualizations from pandas DataFrames, exploring distributions or categorical comparisons, or producing multi-panel relational plots with minimal code.
Use when computing SHAP values, generating explanation plots (waterfall, beeswarm, bar), or debugging feature importance for tree-based, neural-network, or linear models.
Use when reducing high-dimensional data to 2D/3D for visualization, preprocessing embeddings before clustering with HDBSCAN, or training parametric dimensionality-reduction models.