Machine-learning Potential for Landscape Exploration — a fast, GPU-accelerated computational chemistry toolkit powered by machine learning potentials.
Comprehensive computational chemistry with ML potentials
Support for ANI, AIMNet2, MACE-OFF, UMA, and more. Near-DFT accuracy at a fraction of the cost.
L-BFGS, RFO, steepest descent, conjugate gradient, DIIS, and bracketing L-BFGS methods.
NEB, CI-NEB, P-RFO, Dimer, Growing String, AutoNEB methods for saddle point search.
IRC with GS, LQA, HPC, and EulerPC integrators. Verify TS connectivity to reactants and products.
Normal mode analysis, RRHO thermochemistry, IR spectra. Mass-weighted Hessian diagonalization.
1D to N-dimensional relaxed or rigid scans. Map energy landscapes along chosen coordinates.
GBSA implicit solvation and explicit solvent models. DFT-D4 dispersion corrections.
CUDA support for ML potential evaluation. Orders of magnitude faster than traditional QM.
Additional features are actively under development. Stay tuned for future releases.
A one-stop machine-learning potential platform for computational chemistry
