ondrejmarsalek / aml

A... M... L...

AML

Short Description

This is a Python package to automatically build the reference set for the training of Neural Network Potentials (NNPs), and eventually other machine-learned potentials, in an automated, data-driven fashion. For that purpose, a large set of reference configurations sampled in a physically meaningful way (typically with molecular dynamics) is filtered and the most important points for the representation of the Potential Energy Surface (PES) are identified. This is done by using a set of NNPs, called a committee, for error estimates of individual configurations. By iteratively adding the points with the largest error in the energy/force prediction, the reference set is progressively extended and optimized.

Keywords:

• Active learning
• Query by committee
• Ensemble averaging
• Committee machines
• Neural Network Potentials

More information can be found in the following references:

• C. Schran, F. L. Thiemann, P. Rowe, E. A. Müller, O. Marsalek, A. Michaelides,
  "Machine learning potentials for complex aqueous systems made simple",
  PNAS 118, e2110077118 (2021), 10.1073/pnas.2110077118
• C. Schran, K. Brezina, O. Marsalek,
  "Committee neural network potentials control generalization errors and enable active learning",
  J. Chem. Phys. 153, 104105 (2020), 10.1063/5.0016004

Installation

For now, just clone the repository and source the env.sh file.

Dependencies:

• NumPy
• SciPy
• Matplotlib
• MDTraj (for scoring)
• cp2k-input-tools (for CP2K calculations)