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 NEP2, 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 

select between NEP2, NEP3, and NEP4 

number of atom types and list of chemical species 

force weights for different atom types 

select to train potential, dipole, or polarizability 

select between training and prediction (inference) 

outer cutoff for the universal ZBL potential [Ziegler1985] 

radial (\(r_\mathrm{c}^\mathrm{R}\)) and angular (\(r_\mathrm{c}^\mathrm{A}\)) cutoffs 

size of radial (\(n_\mathrm{max}^\mathrm{R}\)) and angular (\(n_\mathrm{max}^\mathrm{A}\)) basis 

number of radial (\(N_\mathrm{bas}^\mathrm{R}\)) and angular (\(N_\mathrm{bas}^\mathrm{A}\)) basis functions 

expansion order for angular terms 

number of neurons in the hidden layer (\(N_\mathrm{neu}\)) 

weight of \(\mathcal{L}_1\)norm regularization term 

weight of \(\mathcal{L}_2\)norm regularization term 

weight of energy loss term 

weight of force loss term 

weight of virial loss term 

bias term that can be used to make smaller forces more accurate 

batch size for training 

population size used in the SNES algorithm [Schaul2011] 

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.