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.
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…
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…
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 agoA7 tokens
originalMIT
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: