Cairn.jl is a toolkit of active learning algorithms for training machine learning interatomic potentials (ML-IPs) for molecular dynamics simulation.
Cairn.jl is constructed as an extension to Molly.jl, implementing enhanced MD samplers, and interfaces with other packages in the Julia ecosystem for molecular simulation, developed by CESMIX and JuliaMolSim.
Active learning algorithms build efficient training datasets which maximally improve accuracy of a scientific machine learning model. These algorithms take an iterative structure, looping through the steps:
-
Data generation. A system's potential energy landscape is sampled by generating trajectories of molecular configurations through the simulation of Newton's equation of motion or its modifications. Users have a choice between standard MD simulation, such as Langevin dynamics or Velocity-Verlet, or enhanced sampling algorithms, such as uncertainty driven dynamics (UDD), Stein repulsive Langevin dynamics, or Stein variational molecular dynamics. These methods are specified under the abstract type
Simulator. -
Trigger for retraining. Sampling is terminated and retraining is triggered when the trajectory has met a certain criteria. A "fixed trigger" calls on retraining after a fixed number of simulation steps. "Adaptive triggers" are based on metrics of uncertainty, from Gaussian process or ensemble-based estimates of variance; metrics of extrapolation, based on a MaxVol vector basis; or metrics of diversity, such as a DPP inclusion probability. These methods are specified under the abstract type
ActiveLearningTrigger. -
Data subset selection and labelling. A subset of the data from the simulated trajectory is selected for labelling using reference calculations and appending to the training set. The most basic selection is a random subset of the trajectory. "Adaptive" selections can be made based on data which exceeds a threshold or data which are chosen by a subset selection algorithm, such as MaxVol, k-means clustering, or DPPs. These methods are specified under the abstract type
SubsetSelector. -
Model updating. The machine learning model is retrained on the augmented dataset according to the choice of objective function defined by the abstract type
LinearProblem. These methods live in the package PotentialLearning.jl.
For a technical manual on the package, see the docs. For a demo, see the Jupyter notebooks in the examples folder.