dralgroup / mlatom

What it does

• Methods. universal ML potentials (ANI, AIMNet2); AI-enhanced QM methods (UAIQM, AIQM1, AIQM2) approaching coupled-cluster accuracy at semi-empirical cost; and DFT, semi-empirical, and wavefunction methods via interfaces (PySCF, Gaussian, O
• ML models. train and use ML potentials (KREG, GAP-SOAP, ANI, MACE, and more) with active learning, Δ-learning, transfer learning, and self-correction.
• Simulations. geometry optimization, transition-state search, IRC, frequencies & thermochemistry, molecular dynamics, nonadiabatic dynamics, IR/Raman/UV–vis spectra, and periodic boundary conditions.
• Workflows. compose methods and tasks into complex pipelines from Python, input files, or the command line.