Train

The default value can be found in DeepH-pack/deeph/default.ini. The following arguments can be set in the configuration file for Train:

basic

device : The device you used for training (cpu or cuda:x, where x is the index of the cuda device). If the cuda is disabled by disable_cuda or it is not available in your environment, you may not set this option as it will be automatically set as ‘cpu’.

save_to_time_folder : Whether to create a subfolder named with the current time in save_dir.

save_csv : Whether to output labels and predictions for all the structures in the format of csv.

tb_writer : Whether to track and visualize the training process by TensorBoard.

multiprocessing : Number of processes to use multiprocessing to generate crystal graphs. Set to -1 to use all available CPUs. Set to 0 (default) to disable multiprocessing. WARNING: The keyword “num_threads” is incompatible with multiprocessing. If you use multiprocessing with a value of 1 or higher, the crystal graphs generation process will ignore the num_threads keyword. For optimal performance and memory usage, we recommend setting multiprocessing = 0 and adjusting num_threads to control the number of threads. This is because generating crystal graphs can consume large memory.

orbital : A JSON format string that defines matrix elements to be predicted. For example, let $H_{i, \alpha}^{j, \beta}$ denotes DFT Hamiltonian matrix element between orbital $\alpha$ of atom $i$ and orbital $\beta$ of atom $j$ . An input of [{"N1 N2": [a1, a2], "N3 N4": [a3, a4], "N5 N6": [a5, a6]}, {"N7 N8": [a7, a8]}] can be set for the orbital option, if you want to predict two matrix elements H1 and H2 for the edge feature of an atom pair $ij$ , where
$\textsf{H1}=\left\{ \begin{aligned} H_{i, \textsf{a1}}^{j, \textsf{a2}} &\textsf{, if the atomic number of } i \textsf{ and } j \textsf{ is N1 and N2, respectively}\\ H_{i, \textsf{a3}}^{j, \textsf{a4}} &\textsf{, if the atomic number of } i \textsf{ and } j \textsf{ is N3 and N4, respectively}\\ H_{i, \textsf{a5}}^{j, \textsf{a6}} &\textsf{, if the atomic number of } i \textsf{ and } j \textsf{ is N5 and N6, respectively}\\ \textsf{None} &\textsf{, otherwise}\\ \end{aligned} \right.$
and
$\textsf{H2}=\left\{ \begin{aligned} H_{i, \textsf{a7}}^{j, \textsf{a8}} &\textsf{, if the atomic number of} i \textsf{and} j \textsf{is N7 and N8, respectively}\\ \textsf{None} &\textsf{, otherwise}\\ \end{aligned} \right.$
Alternatively, a Python script at DeepH-pack/tools/get_all_orbital_str.py can be used to generate a default configuration to predict all orbitals with one model.

create_from_DFT : Whether to use the DFT Hamiltonian matrices to create the graph instead of setting the cut-off radius by hand. It is recommended to set create_from_DFT to True and not to set radius for training.

radius : The cut-off radius to create graph. Keyword radius has no effect if create_from_DFT is set to True.

epochs : The number of passes of the entire training dataset the learning algorithm has completed.

pretrained : The path to the pretrained model, e.g. /your/pretrained/model/best_state_dict.pkl.

resume : The path to the half-trained model, e.g. /your/half_trained/model/best_state_dict.pkl.

gauss_stop : The stopping radius of basis functions used to represent interatomic distances.

num_l : The number of angular quantum numbers that spherical harmonic functions have.

distance_expansion : Which basis functions are used to represent interatomic distances. choices = ['GaussianBasis', 'BesselBasis', 'ExpBernsteinBasis']

normalization : Which form of normalization layers are used. choices = ['BatchNorm', 'LayerNorm', 'PairNorm', 'InstanceNorm', 'GraphNorm', 'DiffGroupNorm', 'None']

-atom_update_net : Which form of convolutional layers to update atom features are used. choices = ['CGConv', 'GAT', 'PAINN']