SFETNI

71 mods across 2 repositories, 16 stars between them.

pinns-scientific-ml

49

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers physics-informed neural networks (PINNs) for scientific and engineering modeling: strong-form and weak-form physics losses, PDE residuals, boundary and initial condition enforcement, inverse problems, parameter identification, surrogate modeling for PDE systems, optimizer choices, validation…

not rated 8 +1 5d ago A 8 tokens original MIT

small-data-ml

50

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

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…

not rated 8 +1 5d ago A 4 tokens original MIT

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

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…

not rated 8 +1 5d ago A 4 tokens original MIT

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

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…

not rated 8 +1 5d ago A 5 tokens original MIT

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers validation of surrogate models for materials and chemistry: leakage-safe train/validation/test splits, nested cross-validation, leave-one-group-out validation, uncertainty calibration, parity and residual diagnostics, interpolation versus extrapolation checks, descriptor-domain coverage, learning…

not rated 8 +1 5d ago A 3 tokens original MIT

materials-lca

54

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers life-cycle assessment (LCA) for materials, chemicals, and manufacturing routes: goal and scope definition, functional units, system boundaries, inventory construction, allocation, impact assessment, uncertainty, scenario analysis, database provenance, prospective LCA, and sustainability-aware…

not rated 8 +1 5d ago A 3 tokens original MIT

calphad-workflow

55

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers CALPHAD workflows for computational thermodynamics and materials design: thermodynamic database selection, equilibrium and metastable calculations, binary and ternary phase diagrams, Scheil solidification, chemical potentials, activities, driving forces, parameter assessment, uncertainty, and…

not rated 8 +1 5d ago A 5 tokens original MIT

srs-review

64

SFETNI/Scientific-Writing-Skills-Claude-Code-Codex

Command Claude Code

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.

not rated 8 +1 3mo ago A 0 tokens

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill is a model-independent protocol for checking learned microstructure evolution against physical reference simulations. It applies to PINNs, neural time integrators, neural operators, and other surrogates of phase-field or grain-growth dynamics. It covers how to pre-register a benchmark, admissibility…

not rated 8 +1 5d ago A 6 tokens original MIT

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers PINN-Phase-style neural time integration of curvature-driven multiphase-field evolution. A recurrent field model advances a discrete phase-field state autonomously from an initial condition. It is trained only on the physical residual of the discrete operator evaluated on its own predicted…

not rated 8 +1 5d ago A 7 tokens original MIT

pinns-mpf-workflow

68

SFETNI/Deep-Matter-Chem-Skills

Skill Claude CodeCodex

This skill covers the PINNs-MPF approach: coordinate-based physics-informed neural networks that solve multiphase-field (MPF) interface dynamics. The skill includes.

not rated 8 +1 5d ago A 7 tokens original MIT

At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: