AI & LLM Engineering · learningmatter-mit/AtomisticSkills

Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network.

learningmatter-mit/AtomisticSkills

Predict 1H NMR spectra from SMILES strings via NMRdb.org SPINUS neural network prediction and nmrsim quantum mechanical spin simulation.

learningmatter-mit/AtomisticSkills

Build and run LAMMPS molecular dynamics with isolated MLIP-specific binaries (MACE, MatGL/CHGNet, FairChem) to avoid Python and Torch stack conflicts.

learningmatter-mit/AtomisticSkills

Sample off-equilibrium potential energy surface (PES), used for benchmarking and fine-tuning MLIPs.

learningmatter-mit/AtomisticSkills

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

learningmatter-mit/AtomisticSkills

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

learningmatter-mit/AtomisticSkills

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

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

learningmatter-mit/AtomisticSkills

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

learningmatter-mit/AtomisticSkills