coeff_sample

# GaN neural network potential trained by HDNNP

# symmetry function parameters
3                   // number of using function types

type1 1             // function type, size
5.0                 // Rc

type2 3
5.0 0.01 2.0        // Rc eta Rs
5.0 0.01 3.2
5.0 0.01 3.8

type4 2
5.0 0.001 1.0 1.0   // Rc eta lambda zeta
5.0 0.001 -1.0 1.0

# preprocess parameters
2                   // number of preprocess

scaling             // 1st preprocess name

# target range
1.0 -1.0            // target max & min

Ga 5                // element, size
# max
0.8 0.9 1.0 1.1 1.2
# min
-0.8 -0.9 -1.0 -1.1 -1.2

N 5                // element, size
# max
0.8 0.9 1.0 1.1 1.2
# min
-0.8 -0.9 -1.0 -1.1 -1.2


pca                 // 2nd preprocess name

Ga 2 5              // element, out size, in size
# transformation matrix
0.0852851 0.307088 0.0851549 0.308185 0.0850465
0.297891 0.0840766 0.289663 0.0835047 0.316399
# mean
0.479216 0.792801 0.472354 0.788308 0.478651

N 2 5
# transformation matrix
0.1 0.2 0.3 0.4 0.5
-0.1 -0.2 -0.3 -0.4 -0.5
# mean
-2 -1 0 1 2

# neural network parameters
3                   // depth of neural network

Ga 0 2 3 tanh       // element, depth, in size, out size, activation
# weight
0.6453437826308873 0.742684458937021 0.5176822649237571
0.7434313793523288 0.3565197010122435 0.7354816264482255
# bias
0.9589526405394695 0.9844318547022946 0.30885885617505016

Ga 1 3 3 tanh
# weight
0.5419000490472067 0.3086979190990048 0.8973850664509684
0.3645089318972786 0.5765757558750704 0.26099590866599465
0.37768062473994424 0.9248276576054428 0.01937980545711704
# bias
0.15549820061757103 0.9844928205912395 0.6709257421055443

Ga 2 3 1 identity
# weight
0.5243149712074373
0.4642807229055258
0.2033809040335509
# bias
0.6310470973403294

N 0 2 3 tanh
# weight
-0.3 -0.2 -0.1
0 0.1 0.2
# bias
-1 0 1

N 1 3 3 tanh
# weight
-0.4 -0.3 -0.2
-0.1 0 0.1
0.2 0.3 0.4
# bias
-2 0 2

N 2 3 1 identity
# weight
-0.2
0
0.2
# bias
-10