Skip to content

Latest commit

 

History

History
52 lines (40 loc) · 2.77 KB

train-energy-spin.md

File metadata and controls

52 lines (40 loc) · 2.77 KB
Raw

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.

Spin

The construction of the fitting net is give by section {ref}spin <model/spin>

    "spin" : {
        "use_spin":         [true, false],
        "virtual_len":      [0.4],
        "spin_norm":        [1.2737],
    },
  • {ref}use_spin <model/spin[ener_spin]/use_spin> determines whether to turn on the magnetism of the atoms.The index of this option matches option type_map <model/type_map>.
  • {ref}virtual_len <model/spin[ener_spin]/virtual_len> specifies the distance between virtual atom and the belonging real atom.
  • {ref}spin_norm <model/spin[ener_spin]/spin_norm> gives the magnitude of the magnetic moment for each magnatic atom.

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 {ref}start_pref_fr <loss[ener_spin]/start_pref_fr> and {ref}limit_pref_f <loss[ener_spin]/limit_pref_fr>, respectively), i.e.

$$pref_fr(t) = start_pref_fr * ( lr(t) / start_lr ) + limit_pref_fr * ( 1 - lr(t) / start_lr )$$

The {ref}loss <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 {ref}start_pref_e <loss[ener_spin]/start_pref_e>, {ref}limit_pref_e <loss[ener_spin]/limit_pref_e>, {ref}start_pref_fr <loss[ener_spin]/start_pref_fr>, {ref}limit_pref_fm <loss[ener_spin]/limit_pref_fm>, {ref}start_pref_v <loss[ener_spin]/start_pref_v> and {ref}limit_pref_v <loss[ener_spin]/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 {ref}start_pref_v <loss[ener_spin]/start_pref_v> and {ref}limit_pref_v <loss[ener_spin]/limit_pref_v> to 0.