Self-consistent training

In order to use NeuralXC functionals within DFT calculations, models need to be trained self-consistently on the provided structures. This means, an initial model is fit to the reference energies. This model is then used to run self-consistent calculations on the dataset producing updated baseline energies. The model is re-fitted on the difference between the reference and updated baseline energies and self-consistent calculations are run with the updated model. This is done iteratively until the model error converges within a given tolerance.

The self-consistent training procedure requires that a set commands is executed in a well-defined order. In addition to running the self-consistent calculations which requires communication with an electronic structure code, data needs to be tracked, models optimized and error statistics need to be collected.

In order to facilitate self-consistent training, NeuralXC provides a simple command that combines the required steps and automatically produces a final model that is ready for use within DFT calculations:

neuralxc sc <xyz> <config> <hyper>

conducts self-consistent training for the structures specified in a system geometry file <xyz> using the engine and basis set specified in <config> and ML hyperparameters provided in <hyper>. Optional flags include:

--maxit <int>   Maximum number of iterations,
                  if zero create an energy-correcting model only (default: 5)
--tol <float>   Energy tolerance in eV that
                  determines whether self-consistent training has converged (default 0.0005)
--hyperopt      If set, optimize hyperparameters
                  by cross-validation at every ML training iteration