Overview

Please refer to the separate online manual for the command-line use of MLatom.

• Simulations

  • single-point calculations

  • geometry optimizations (minima and transition states, IRC)

  • frequencies & thermochemistry

  • molecular dynamics

  • IR and power spectra from MD

  • simulations with AI-enhanced QM methods and pre-trained ML models (AIQM1, ANI-1ccx, etc.)

  • simulations with QM methods

  • simulations with user-trained models

  • quantum dynamics with machine learning

  • UV/vis spectra (ML-NEA)

  • two-photon absorption cross sections (ML-TPA)

• Learning

  • training popular ML models (KREG, ANI, sGDML, PhysNet, DPMD, GAP-SOAP, KRR-CM)

  • training generic ML models (kernel ridge regression with many kernels)

  • optimizing hyperparameters

  • evaluating ML models (also with learning curves)

  • Δ-learning

  • self-correction

• Data

  • converting XYZ coordinates to molecular descriptor (RE, Coulomb matrix, …)

  • analyzing data sets

  • sampling (random, structure-based, farthest-point) and splitting datasets