3.9. Fit spin energy

In this section, we will take $deepmd_source_dir/examples/NiO/se_e2_a/input.json as an example of the input file.

3.9.1. Spin

The construction of the fitting net is give by section spin

    "spin" : {
        "use_spin":         [true, false],
        "virtual_len":      [0.4],
        "spin_norm":        [1.2737],
    },

use_spin determines whether to turn on the magnetism of the atoms.The index of this option matches option type_map <model/type_map>.
virtual_len specifies the distance between virtual atom and the belonging real atom.
spin_norm gives the magnitude of the magnetic moment for each magnatic atom.

3.9.2. Spin Loss

The spin loss function $L$ for training energy is given by

\[L = p_e L_e + p_{fr} L_{fr} + p_{fm} L_{fm} + p_v L_v\]

where $L_e$, $L_{fr}$, $L_{fm}$ and $L_v$ denote the loss in energy, atomic force, magnatic force and virial, respectively. $p_e$, $p_{fr}$, $p_{fm}$ and $p_v$ give the prefactors of the energy, atomic force, magnatic force and virial losses.

The prefectors may not be a constant, rather it changes linearly with the learning rate. Taking the atomic force prefactor for example, at training step $t$, it is given by

\[p_{fr}(t) = p_{fr}^0 \frac{ \alpha(t) }{ \alpha(0) } + p_{fr}^\infty ( 1 - \frac{ \alpha(t) }{ \alpha(0) })\]

where $\alpha(t)$ denotes the learning rate at step $t$. $p_{fr}^0$ and $p_{fr}^\infty$ specifies the $p_f$ at the start of the training and at the limit of $t \to \infty$ (set by start_pref_fr and limit_pref_f, respectively), i.e.

\[pref_fr(t) = start_pref_fr * ( lr(t) / start_lr ) + limit_pref_fr * ( 1 - lr(t) / start_lr )\]

The loss section in the input.json is

    "loss" :{
	"type":		        "ener_spin",
	"start_pref_e":	    0.02,
	"limit_pref_e":	    1,
	"start_pref_fr":	1000,
    "limit_pref_fr":	1.0,
	"start_pref_fm":	10000,
	"limit_pref_fm":	10.0,
	"start_pref_v":	    0,
	"limit_pref_v":	    0,
    },

The options start_pref_e, limit_pref_e, start_pref_fr, limit_pref_fm, start_pref_v and limit_pref_v determine the start and limit prefactors of energy, atomic force, magnatic force and virial, respectively.

If one does not want to train with virial, then he/she may set the virial prefactors start_pref_v and limit_pref_v to 0.