nep.in

This file specifies hyperparameters used for training neuroevolution potential (NEP) models, the functional form of which is outline here. The NEP approach was proposed in [Fan2021] (NEP1) and later improved in [Fan2022a] (NEP2) and [Fan2022b] (NEP3). Currently, we support NEP3 and NEP4 (to be published), which can be chosen by the version keyword.

File format

In this input file, blank lines and lines starting with # are ignored. One can thus write comments after #.

All other lines need to be of the following form:

keyword parameter_1 parameter_2 ...

Keywords can appear in any order with the exception of the type_weight keyword, which cannot appear before the type keyword.

The type keyword does not have default parameters and must be set. All other keywords have default values.

Keywords

Keyword	Brief description
version	select between NEP3 and NEP4
type	number of atom types and list of chemical species
type_weight	force weights for different atom types
model_type	select to train potential, dipole, or polarizability
prediction	select between training and prediction (inference)
zbl	outer cutoff for the universal ZBL potential [Ziegler1985]
cutoff	radial (\(r_\mathrm{c}^\mathrm{R}\)) and angular (\(r_\mathrm{c}^\mathrm{A}\)) cutoffs
n_max	size of radial (\(n_\mathrm{max}^\mathrm{R}\)) and angular (\(n_\mathrm{max}^\mathrm{A}\)) basis
basis_size	number of radial (\(N_\mathrm{bas}^\mathrm{R}\)) and angular (\(N_\mathrm{bas}^\mathrm{A}\)) basis functions
l_max	expansion order for angular terms
neuron	number of neurons in the hidden layer (\(N_\mathrm{neu}\))
lambda_1	weight of \(\mathcal{L}_1\)-norm regularization term
lambda_2	weight of \(\mathcal{L}_2\)-norm regularization term
lambda_e	weight of energy loss term
lambda_f	weight of force loss term
lambda_v	weight of virial loss term
force_delta	bias term that can be used to make smaller forces more accurate
batch	batch size for training
population	population size used in the SNES algorithm [Schaul2011]
generation	number of generations used by the SNES algorithm [Schaul2011]

Example

Here is an example nep.in file using all the default parameters:

type          2 Te Pb # this is a mandatory keyword
version       4       # default
cutoff        8 4     # default
n_max         4 4     # default
basis_size    8 8     # default
l_max         4 2 0   # default
neuron        30      # default
lambda_e      1.0     # default
lambda_f      1.0     # default
lambda_v      0.1     # default
batch         1000    # default
population    50      # default
generation    100000  # default

The NEP tutorial illustrates the construction of a NEP model. More examples can be found in this repository.