learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate Pourbaix (pH-voltage) diagrams for aqueous electrochemical stability using water-corrected MLIP energies and pymatgen.
not rated 158 today A 35 tokens
MIT
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learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate Quasi-Harmonic Approximation (QHA) thermal properties using MLIPs.
not rated 158 today A 26 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate Raman-active phonon mode frequencies and simulate Raman spectra from MLIP phonon calculations; optionally compute full Raman intensities with DFT Born charges via atomate2.
not rated 158 today A 42 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Generate random crystal structures for a given composition (AIRSS-style) and relax with MLIPs to find low-energy candidates.
not rated 158 today A 31 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Predict thermodynamically optimal solid-state inorganic synthesis pathways and tabulates basic reactions.
not rated 158 today A 22 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.
not rated 158 today A 29 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate absolute solid Helmholtz free energy, and optional Gibbs free energy, with Frenkel-Ladd switching using portable MLIP wrappers on a pre-equilibrated periodic structure.
not rated 158 today A 40 tokens
MIT
mat-stability
104learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate the thermodynamic stability and energy above the convex hull (Ehull) of a material at 0K.
not rated 158 today A 28 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.
not rated 158 today A 27 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate surface adsorption energies for adsorbate-surface combinations using MLIPs.
not rated 158 today A 22 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).
not rated 158 today A 27 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
not rated 158 today A 39 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.
not rated 158 today A 31 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Calculate the X-ray Diffraction (XRD) spectrum of a material using pymatgen.
not rated 158 today A 25 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.
not rated 158 today A 27 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Phase identification from experimental XRD using DARA's tree search (Ray-based).
not rated 158 today A 22 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).
not rated 158 today A 24 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Iteratively optimize expensive black-box objectives — such as materials properties, experimental yields, or simulation outputs — by learning from past evaluations to select the most promising next candidates.
not rated 158 today A 40 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
train a Cluster Expansion (CE) for lattice-based Monte Carlo simulation of disordered materials.
not rated 158 today A 24 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Quantify prediction uncertainty of MACE MLIPs using committee (ensemble) models; flag high-uncertainty structures for DFT verification.
not rated 158 today A 36 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.
not rated 158 today A 28 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Guide for selecting the most appropriate foundation MLIP model based on simulation requirements.
not rated 158 today A 21 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Generate novel crystal structures and molecules using ADiT (All-atom Diffusion Transformer), a unified latent diffusion model.
not rated 158 today A 30 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
not rated 158 today A 36 tokens
MIT
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