Agent skills by learningmatter-mit, page 3

Calculate the thermodynamic stability and energy above the convex hull (Ehull) of a material at 0K.

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Determine if a given structure matches known experimental or theoretical structures, or compare two user-provided structures.

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Calculate surface adsorption energies for adsorbate-surface combinations using MLIPs.

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Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).

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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.

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Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.

learningmatter-mit/AtomisticSkills

Construct and validate Wannier tight-binding models, inspect orbital localization and hopping amplitudes, and interpolate electronic bands from DFT or existing Wannier90 files.

learningmatter-mit/AtomisticSkills

Calculate the X-ray Diffraction (XRD) spectrum of a material using pymatgen.

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Digitize an image of an XRD plot into a numeric .xy data file by extracting visual peaks.

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Phase identification from experimental XRD using DARA's tree search (Ray-based).

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Perform Rietveld refinement from experimental XRD patterns using DARA (BGMN).

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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…

learningmatter-mit/AtomisticSkills

train a Cluster Expansion (CE) for lattice-based Monte Carlo simulation of disordered materials.

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Quantify prediction uncertainty of MACE MLIPs using committee (ensemble) models; flag high-uncertainty structures for DFT verification.

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Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.

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Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.

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Generate inorganic material structures using MatterGen, a diffusion-based generative model.

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Fine-tune MACE machine learning interatomic potentials on custom datasets.

learningmatter-mit/AtomisticSkills

Fine-tune MatGL machine learning interatomic potentials on custom datasets.

learningmatter-mit/AtomisticSkills

Benchmark MLIP accuracy against a labeled dataset — compute MAE/RMSE for energy/atom and forces, and generate parity plots.

learningmatter-mit/AtomisticSkills

Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs).

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Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive properties from crystal or molecular…

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Optional experimental GPU-accelerated batched inference for MACE, MatGL (TensorNet/M3GNet/CHGNet), and FairChem MLIPs using NValchemi, enabling parallel static, relax, and MD workflows across…

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Compute RDKit physicochemical descriptors and rule-based drug-likeness heuristics (Ro5, Veber, QED) from SMILES.

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Compute Morgan/ECFP fingerprints, Tanimoto similarity, and optional Butina clusters/heatmaps for small-molecule comparison.

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Perform iterative, deep, and comprehensive literature research on a specific materials/chemistry topic.

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Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation…

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Act as a reviewer to critically review research plans, manuscripts, or task summaries, pointing out missing baselines, statistical flaws, and weak assumptions.

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Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.

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Hierarchically decompose high-level scientific workflows (from literature or user-proposed) into executable sequences of existing SKILLs and MCP tools for the research plan.

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Real-time monitoring tools for stability, equilibration, and diffusion during ASE molecular dynamics simulations.

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Guide for selecting the most appropriate foundation MLIP model based on simulation requirements.

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Generate novel crystal structures and molecules using ADiT (All-atom Diffusion Transformer), a unified latent diffusion model.

learningmatter-mit/AtomisticSkills