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About MAPLE

A fast, GPU-accelerated computational chemistry toolkit powered by machine learning potentials for molecular modeling and quantum chemical calculations.

What is MAPLE?

MAPLE (Machine-learning Potential for Landscape Exploration) is a computational chemistry software package designed for molecular modeling and quantum chemical calculations. It provides a unified computational environment suitable for research at different theoretical levels, from small molecules to complex reaction networks.

MAPLE leverages modern machine learning potentials to achieve near-DFT accuracy at a fraction of the computational cost of traditional quantum mechanical methods. Its modular design allows users to perform a wide variety of calculations within a consistent framework, with flexible control over computational parameters and accuracy requirements.

All energy values in MAPLE are expressed in Hartree, and all distances are expressed in Angstroms.

Supported ML Potentials

MAPLE supports a wide range of machine learning interatomic potentials. Each model offers different trade-offs between speed, accuracy, and element coverage.

UMA — Universal Model

Universal

UMA

Universal Machine-learning Architecture from Meta. A foundation model covering broad chemical space with a single unified potential — the most general-purpose ML potential in MAPLE.

ANI Family

ANI Family

ANI-2x

General-purpose neural network potential supporting H, C, N, O, S, F, Cl. Trained on DFT data with active learning.
ANI Family

ANI-1x

Neural network potential for organic molecules containing H, C, N, and O. Trained on the ANI-1x data set.
ANI Family

ANI-1ccx

Coupled-cluster accuracy neural network potential for H, C, N, O molecules. Transfer-learned from ANI-1x to CCSD(T)/CBS data.
ANI Family

ANI-1xnr

Non-reactive variant of ANI-1x optimized for stable molecular geometries and vibrational properties.

AIMNet Family

AIMNet

AIMNet2

Atoms-in-Molecules network potential with broad element coverage and high accuracy for organic and drug-like molecules.
AIMNet

AIMNet2-NSE

AIMNet2 variant with non-stoichiometric energy corrections for improved thermochemical accuracy.

MACE Family

MACE

MACE-OFF23 (s/m/l)

Equivariant message-passing potentials for organic molecules. Available in small, medium, and large variants balancing speed and accuracy.
MACE

MACE-OMol

MACE potential trained on the OMol25 data set for broad organic molecular coverage with high accuracy.
MACE

EGRET

Equivariant graph neural network potential designed for reactive and non-reactive molecular simulations.

Computational Capabilities

MAPLE provides a comprehensive suite of computational chemistry methods, all driven by machine learning potentials for fast evaluation on GPU hardware.

Supported Task Types

sp

Single Point

Compute molecular energies and properties at fixed geometries.
freq

Frequency

Normal mode analysis, RRHO thermochemistry, and IR spectra via mass-weighted Hessian diagonalization.
opt

Optimization

Locate equilibrium geometries with L-BFGS, RFO, steepest descent, conjugate gradient, DIIS, or bracketing L-BFGS.
ts

Transition State

Saddle point search via NEB, CI-NEB, P-RFO, Dimer, Growing String, or AutoNEB methods.
scan

PES Scan

1D, 2D, or 3D relaxed or rigid scans along chosen internal coordinates.
irc

IRC

Intrinsic reaction coordinate with GS, LQA, HPC, and EulerPC integrators.

Additional Features

Function

Solvation

GBSA implicit solvation and explicit solvent models with DFT-D4 dispersion corrections.
Function

Constraints

Fix atoms, bonds, angles, and dihedral angles during optimization and scanning.
Runtime

GPU Acceleration

Full CUDA support for ML potential evaluation, providing orders of magnitude speedup over traditional QM methods.

Correction

D4 Dispersion

DFT-D4 dispersion correction available as a global option for any calculation.