This skill covers the systematic provenance tracking, data versioning, and environment capture required to make computational materials science campaigns reproducible: DFT dataset provenance, MLP training experiment tracking, active learning iteration versioning, environment snapshots, model registry management, and…
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…
This skill covers machine learning on small scientific datasets (roughly 20–2000 samples) in materials science and chemistry: leakage-safe splitting strategies for correlated structures and compositions, baseline model selection, descriptor screening, uncertainty quantification, learning curves, model selection under…
This skill covers Bayesian optimization (BO) for materials discovery and chemistry design: surrogate model construction, acquisition function selection and optimization, batch and constrained BO, multi-objective Pareto-front search, multi-fidelity optimization, and integration with high-throughput DFT, experimental…
This skill covers Gaussian process regression (GPR) for materials and chemistry datasets: descriptor selection, feature scaling, kernel design, small-data modeling, calibrated uncertainty, heteroscedastic noise, cross-validation, multi-output and multi-fidelity extensions, and integration with Bayesian optimization…
Run a 4-role peer reviewer simulation across the complete manuscript. Each role is a bounded, read-only subagent. All reports are presented to the human before any revisions are considered.
Claude Code instructions for SFETNI/Scientific-Writing-Skills-Claude-Code-Codex, covering claude.md — scientific-redaction-skills, what this framework is, mandatory step 0 protocol, skill routing and core rules.