SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the first-principles modeling of point defects in crystalline materials: vacancy, interstitial, antisite, substitutional, and complex defect construction; charge-state calculations; defect formation energy formalism with chemical potential constraints; finite-size corrections…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers lattice dynamics from first principles: computing phonon dispersion curves, phonon density of states (PDOS), and thermal properties using the finite-displacement method (Phonopy + VASP/QE) and density functional perturbation theory (DFPT via Quantum ESPRESSO ph.x or VASP IBRION=7/8). It covers the…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the construction and DFT calculation of surface slab models: Miller-index surface generation, termination selection, slab and vacuum thickness convergence, dipole corrections, surface energy and adsorption energy calculations, work function extraction, and ab initio surface phase diagrams. It…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers equation-of-state (EOS) fitting for crystalline materials from DFT or ML-potential calculations: generating energy-volume datasets via fixed-volume DFT relaxations, fitting Birch-Murnaghan, Vinet, and Murnaghan EOS forms, extracting equilibrium volume V₀, ground-state energy E₀, bulk modulus B₀, and…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers automated, large-scale DFT screening workflows: workflow graph construction and provenance tracking with atomate2/jobflow or AiiDA, input-set standardization, error handling with custodian, HPC job submission and restart logic, MongoDB-backed results storage, deduplication, and quality control. It…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers end-to-end DFT calculations using Quantum ESPRESSO (QE), from input file construction and pseudopotential selection through SCF convergence, structural relaxation, band structure, and DOS workflows. QE is the dominant open-source plane-wave DFT code and the standard backend for many high-throughput…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers end-to-end DFT calculations using VASP 6, from input file construction through convergence testing to production runs. It is the entry point for electronic structure work and the primary source of training data for ML interatomic potentials. Invoke this skill when an agent needs to set up, run…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers de novo crystal structure generation using score-based diffusion and flow-matching generative models, including MatterGen (Microsoft Research), DiffCSP (Jiao et al.), and CDVAE (Xie et al.). These models learn the distribution of stable inorganic crystals from crystallographic databases and sample…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers graph neural networks for crystal and periodic-material property prediction: crystal graph construction from periodic structures, node/edge/lattice/distance/angle features, periodic neighbor finding, cutoff selection, invariant and equivariant representations, leakage-aware splitting, uncertainty…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
Use this skill when building, applying, validating, or fine-tuning MatGL models for materials property prediction, learned interatomic potentials, and graph-based materials workflows. MatGL is most useful when a workflow needs pretrained graph neural network models, pymatgen-compatible structure handling, periodic…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers graph neural networks for molecular property prediction: molecular graph construction from SMILES, SDF, RDKit molecules, and 3D conformers; node and edge featurization; 2D message-passing models; 3D continuous-filter and directional models; validation under scaffold and chemistry shifts; uncertainty…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the systematic active learning workflow for iteratively building ML interatomic potential training datasets: initial dataset construction, uncertainty-guided exploration MD, DFT oracle labeling, query strategy, dataset versioning, retraining cadence, and stopping criteria. Active learning is the…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers fine-tuning pretrained universal machine-learned interatomic potentials on system-specific DFT data: when fine-tuning is the right strategy, how to prepare a small targeted dataset, how to configure MACE's --foundationmodel workflow, how to handle reference energies and label offsets, how to avoid…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the full workflow for training MACE (Message-passing Atomic Cluster Expansion) machine-learned interatomic potentials: DFT dataset preparation in extxyz format, isolated atom energy handling, train/validation/test splitting, model architecture selection, loss weighting, training execution, validation…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the full pipeline for deploying trained ML interatomic potentials into LAMMPS for production molecular dynamics: LAMMPS compilation with MLP plugins, pairstyle and paircoeff configuration, species-to-atom-type mapping, unit consistency verification, neighbor list tuning, GPU and MPI parallelism…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the systematic validation of machine-learned interatomic potentials (MLPs) before production deployment: static test-set error analysis, physical property reproduction (equation of state, phonons, elastic constants, surface and defect energies), MD stability testing, extrapolation detection, and…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers training, validating, and deploying NequIP-style E(3)-equivariant neural network interatomic potentials from DFT reference data. Invoke it when building a system-specific equivariant potential, comparing NequIP against MACE, DeePMD, CHGNet, M3GNet, or universal potentials, diagnosing unstable MD…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers practical uncertainty quantification (UQ) for machine-learned interatomic potentials: committee (ensemble) models, model-deviation computation for forces, energies, and stresses, calibration of uncertainty thresholds against DFT errors, SOAP-based structural dissimilarity as a complementary…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the selection, evaluation, and deployment of pretrained universal machine-learned interatomic potentials (universal MLPs) — models trained on large multi-element datasets that can be applied to new chemical systems without additional DFT labeling. It covers the major model families (MACE-MP, CHGNet…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers phase-field modeling for microstructure evolution: choosing order parameters, constructing free-energy functionals, solving Allen-Cahn and Cahn-Hilliard equations, calibrating thermodynamic and kinetic parameters, verifying numerical convergence, and validating simulations of spinodal decomposition…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the Atomic Simulation Environment (ASE) as a workflow hub for atomistic simulation: Atoms objects, calculators, constraints, optimizers, trajectories, file IO, structure manipulation, dataset generation, and conversion between DFT, MD, ML potentials, Phonopy, pymatgen, LAMMPS, and visualization…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers reproducible access to materials databases for computational materials science: Materials Project, AFLOW, OQMD, NOMAD, COD, and ICSD-style crystallographic sources; API authentication, pagination, rate limits, caching, snapshot provenance, structure matching, duplicate detection, energy…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers the conversion of atomic structures, compositions, and trajectories into numerical feature vectors for physical-science ML: composition featurizers (Magpie-style elemental statistics, oxidation-state features), structure featurizers (SOAP, Coulomb matrix, Voronoi, radial distribution functions, bond…
SFETNI/Deep-Matter-Chem-Skills
Skill Claude CodeCodex
This skill covers pymatgen as a materials analysis and structure-processing framework: Structure, Molecule, Lattice, Composition, Element, Species, and Site objects; CIF/POSCAR/VASP output parsing; symmetry analysis; cell standardization; supercell, slab, defect, and substitution preparation; phase diagrams and…