mlff

Mitigating the effect of activity cliffs using Triplet Soft Margin (TSM) loss on High-Value Activity Cliff Triplets (HV-ACTs).

Running molecular geometry optimization and molecular dynamics (MD) simulations using ASE and MLIPs.

Generating 3D molecular conformations and preparing input files for quantum chemistry DFT calculations.

Constructing Gaussian output neural networks and training deep ensembles to quantify aleatoric and epistemic uncertainty.

Implementing Delta-ML (residual learning between low and high levels of theory) and model transfer learning.

Optimizing molecular feature weights and performing feature selection via Differentiable Information Imbalance (DII).

Active learning loops for MLIPs using uncertainty-biased configuration selection and DFT query oracle simulation.

Training Machine Learning Interatomic Potentials (MLIP) to predict energies and forces, and wrapping as an ASE Calculator.

Molecular graph representations and Bemis-Murcko scaffold splitting to assess OOD generalization.

Multi-task GNN learning with Adaptive Checkpointing with Specialization (ACS) to mitigate negative transfer.