3.8. Fit energy

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

3.8.1. The fitting network

The construction of the fitting net is given by section fitting_net

	"fitting_net" : {
	    "neuron":		[240, 240, 240],
	    "resnet_dt":	true,
	    "seed":		1
  • neuron specifies the size of the fitting net. If two neighboring layers are of the same size, then a ResNet architecture is built between them.

  • If the option resnet_dt is set to true, then a timestep is used in the ResNet.

  • seed gives the random seed that is used to generate random numbers when initializing the model parameters.

3.8.2. Loss

The loss function \(L\) for training energy is given by

\[L = p_e L_e + p_f L_f + p_v L_v\]

where \(L_e\), \(L_f\), and \(L_v\) denote the loss in energy, forces and virials, respectively. \(p_e\), \(p_f\), and \(p_v\) give the prefactors of the energy, force and virial losses. The prefectors may not be a constant, rather it changes linearly with the learning rate. Taking the force prefactor for example, at training step \(t\), it is given by

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

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

pref_f(t) = start_pref_f * ( lr(t) / start_lr ) + limit_pref_f * ( 1 - lr(t) / start_lr )

The loss section in the input.json is

    "loss" : {
	"start_pref_e":	0.02,
	"limit_pref_e":	1,
	"start_pref_f":	1000,
	"limit_pref_f":	1,
	"start_pref_v":	0,
	"limit_pref_v":	0

The options start_pref_e, limit_pref_e, start_pref_f, limit_pref_f, start_pref_v and limit_pref_v determine the start and limit prefactors of energy, 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.