Learning Matter @ MIT
Official@learningmatter-mit
Rafael Gomez-Bombarelli Group @ MIT
Agent Skills by Learning Matter @ MIT
Showing 121 vetted skills indexed across 1 GitHub repositories.
learningmatter-mitmat-defect-energy-dft
Compute charged point-defect formation energies and Fermi-level transition diagrams from VASP DFT outputs.
learningmatter-mitmat-surface-energy
Calculate surface energies for (hkl) planes and construct equilibrium Wulff shapes.
learningmatter-mitml-fairchem-finetune
Fine-tune Fairchem interatomic potentials on labeled structure datasets.
learningmatter-mitdrug-pocket-detection
Detect and rank ligandable protein pockets using fpocket or P2Rank.
learningmatter-mitmat-dft-lobster
Generate and run VASP-to-LOBSTER projection workflows for COHP bonding analysis.
learningmatter-mitml-property-predict-scd
Train atomistic property prediction models from SelfConditionedDenoisingAtoms checkpoints.
learningmatter-mitml-mlip-automl
Automate MLIP hyperparameter tuning via LLM-driven iterative search over learning rate, freezing, and loss weighting.
learningmatter-mitmat-solid-free-energy
Calculate solid Helmholtz free energy via Frenkel-Ladd thermodynamic integration.
learningmatter-mitml-generative-adit
Generate periodic crystal and molecular structures as CIF or XYZ outputs.
learningmatter-mitdrug-db-pdb
Search RCSB Protein Data Bank and export structure metadata as JSON.
learningmatter-mitmat-dft-electronic-transport
Compute carrier mobility, conductivity, and Seebeck coefficient from DFT band structures using AMSET.
learningmatter-mitchem-bond-dissociation
Calculate homolytic and heterolytic bond dissociation energies for cleavable single bonds.
learningmatter-mitdrug-redocking-rmsd
Compute symmetry-corrected heavy-atom RMSD between docked poses and reference ligands.
learningmatter-mitchem-solution-md
Set up and run explicit-solvent molecular dynamics with Packmol and MLIP backends.
learningmatter-mitmat-phase-diagram
Retrieve Materials Project phase diagrams and visualize convex-hull stability.
learningmatter-mitmat-xrd-refinement
Refine powder XRD patterns against CIF phases to quantify phase fractions and lattice parameters.
learningmatter-mitgeneral-arxiv-search
Retrieve ArXiv paper metadata using keyword, author, category, and title filters.
learningmatter-mitmat-db-optimade
Query OPTIMADE-compliant materials databases and export crystal structure records as JSON.
learningmatter-mitdrug-retrosynthesis
Predict ranked retrosynthetic precursor trees from target SMILES via IBM RXN API.
learningmatter-mitmat-dielectric-response
Calculate frequency-dependent dielectric response of crystalline materials using atomate2 OpticsMaker and VASP.
learningmatter-mitmat-dft-ferroelectric
Computes spontaneous ferroelectric polarization via VASP LCAPOL=True Berry-phase workflow.
learningmatter-mitdrug-trajectory-analysis
Analyze protein–ligand MD trajectories to extract RMSD, COM drift, RMSF, hydrogen bonds, and contact occupancy.
learningmatter-mitgeneral-patent-search
Search Google Patents for patent metadata by keyword, assignee, or chemical name.
learningmatter-mitchem-neb-barrier
Calculate NEB activation energy barriers for atomic migration and reactions.
Frequently Asked Questions About Learning Matter @ MIT
FAQPage SchemaWhat specific scientific tasks are enabled by these computational capabilities?▼
These capabilities enable high-throughput screening of crystal structures, prediction of electronic and mechanical properties, transition-state optimization for chemical reactions, and protein-ligand binding affinity analysis for drug discovery.
Which target personas benefit from these research-oriented computational modules?▼
Computational chemists, materials scientists, and structural biologists focused on atomistic modeling, high-throughput screening, and predictive modeling for solid-state physics or pharmaceutical development will find these modules essential for their research.
What are the primary dependencies required to execute these simulation modules?▼
Execution requires a standard scientific computing environment with access to VASP, LAMMPS, or OpenMM, alongside specific libraries like pymatgen, ASE, and PyTorch for machine-learning-based interatomic potential inference.