v0.1.1

This release provides several improvements and bug fixes.

Major bug fixes:

• Related to calculating losses when training with forces: #239, #240, #243
• PhysNet interaction module: #236

Notable additions:

• AimNet2 added to the available: NNPs #253
• Additional PhAlkEthOH dataset versions, including a version that removes configurations with high energy: #245
• Support to enable multiple cutoffs for models: #238
• Routines for handling long-range electrostatics (following the PhysNet approach): #235
• Charge conservation scheme: #234

v0.1.0

This is the initial release of the modelforge package.

This provides support for training several different Neural Network Potentials, including SchNet and ANI2x (Invariant architectures) and PaiNN, PhysNet, TensorNet, and SAKE (Equivariant architectures) using several curated datasets (QM9, ANI1x, ANI2x, SPICE 1, SPICE 1 openff, SPICE 2, and PhAlkEthOH openff).