# Fit electronic density of states (DOS) {{ tensorflow_icon }} {{ pytorch_icon }} {{ jax_icon }} {{ dpmodel_icon }} :::{note} **Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, JAX {{ jax_icon }}, DP {{ dpmodel_icon }} ::: Here we present an API to DeepDOS model, which can be used to fit electronic density of state (DOS) (which is a vector). See the [PRB paper](https://doi.org/10.1103/PhysRevB.105.174109) for details. In this example, we will show you how to train a model to fit a silicon system. A complete training input script of the examples can be found in ```bash $deepmd_source_dir/examples/dos/input.json ``` The training and validation data are also provided our examples. But note that **the data provided along with the examples are of limited amount, and should not be used to train a production model.** Similar to the `input.json` used in `ener` mode, training JSON is also divided into {ref}`model `, {ref}`learning_rate `, {ref}`loss ` and {ref}`training `. Most keywords remain the same as `ener` mode, and their meaning can be found [here](train-se-e2-a.md). To fit the `dos`, one needs to modify {ref}`model[standard]/fitting_net ` and {ref}`loss `. ## The fitting Network The {ref}`fitting_net ` section tells DP which fitting net to use. The JSON of `dos` type should be provided like ```json "fitting_net" : { "type": "dos", "numb_dos": 250, "sel_type": [0], "neuron": [120,120,120], "resnet_dt": true, "fparam": 0, "seed": 1, }, ``` - `type` specifies which type of fitting net should be used. It should be `dos`. - `numb_dos` specifies the length of output vector (density of states), which the same as the `NEDOS` set in VASP software, this argument defines the output length of the neural network. We note that the length of `dos` provided in training set should be the same. - The rest arguments have the same meaning as they do in `ener` mode. ## Loss DeepDOS supports trainings of the global system (a global `dos` label is provided in a frame) or atomic system (atomic labels `atom_dos` is provided for **each** atom in a frame). In a global system, each frame has just **one** `dos` label. For example, when fitting `dos`, each frame will just provide a `1 x numb_dos` vector which gives the total electronic density of states. By contrast, in an atomic system, each atom in has a `atom_dos` label. For example, when fitting the site-projected electronic density of states, each frame will provide a `natom x numb_dos` matrices, The {ref}`loss ` section tells DP the weight of these two kinds of loss, i.e. ```python loss = pref * global_loss + pref_atomic * atomic_loss ``` The loss section should be provided like ```json "loss" : { "type": "dos", "start_pref_dos": 0.0, "limit_pref_dos": 0.0, "start_pref_cdf": 0.0, "limit_pref_cdf": 0.0, "start_pref_ados": 1.0, "limit_pref_ados": 1.0, "start_pref_acdf": 0.0, "limit_pref_acdf": 0.0 }, ``` - {ref}`type ` should be written as `dos` as a distinction from `ener` mode. - `pref_dos` and `pref_ados`, respectively specify the weight of global and atomic loss. If set to 0, the corresponding label will not be included in the training process. - We also provides a combination training of vector and its cumulative distribution function `cdf`, which can be defined as $$D(\epsilon) = \int_{e_{min}}^{\epsilon} g(\epsilon')d\epsilon'$$ ## Training Data Preparation The global label should be named `dos.npy/raw`, while the atomic label should be named `atomic_dos.npy/raw`. If wrongly named, DP will report an error. To prepare the data, we recommend shifting the DOS data by the Fermi level. ## Train the Model The training command is the same as `ener` mode, i.e. ::::{tab-set} :::{tab-item} TensorFlow {{ tensorflow_icon }} ```bash dp --tf train input.json ``` ::: :::{tab-item} PyTorch {{ pytorch_icon }} ```bash dp --pt train input.json ``` ::: :::: The detailed loss can be found in `lcurve.out`: ``` # step rmse_trn rmse_ados_trn rmse_ados_lr 0 1.11e+00 1.11e+00 1.0e-03 100 5.00e-02 5.00e-02 1.0e-03 200 4.70e-02 4.70e-02 1.0e-03 300 6.45e-02 6.45e-02 1.0e-03 400 3.39e-02 3.39e-02 1.0e-03 500 4.60e-02 4.60e-02 1.0e-03 600 3.98e-02 3.98e-02 1.0e-03 700 9.50e-02 9.50e-02 1.0e-03 800 5.49e-02 5.49e-02 1.0e-03 900 5.57e-02 5.57e-02 1.0e-03 1000 3.73e-02 3.73e-02 1.0e-03 1100 4.33e-02 4.33e-02 1.0e-03 1200 3.27e-02 3.27e-02 1.0e-03 1300 3.68e-02 3.68e-02 1.0e-03 1400 3.09e-02 3.09e-02 1.0e-03 1500 3.42e-02 3.42e-02 1.0e-03 1600 5.62e-02 5.62e-02 1.0e-03 1700 6.12e-02 6.12e-02 1.0e-03 1800 4.10e-02 4.10e-02 1.0e-03 1900 5.30e-02 5.30e-02 1.0e-03 2000 3.85e-02 3.85e-02 1.0e-03 ``` ## Test the Model In this earlier version, we can use `dp test` to infer the electronic density of state for given frames. ::::{tab-set} :::{tab-item} TensorFlow {{ tensorflow_icon }} ```bash dp --tf freeze -o frozen_model.pb dp --tf test -m frozen_model.pb -s ../data/111/$k -d ${output_prefix} -a -n 100 ``` ::: :::{tab-item} PyTorch {{ pytorch_icon }} ```bash dp --pt freeze -o frozen_model.pth dp --pt test -m frozen_model.pth -s ../data/111/$k -d ${output_prefix} -a -n 100 ``` ::: :::: if `dp test -d ${output_prefix} -a` is specified, the predicted DOS and atomic DOS for each frame are output in the working directory ``` ${output_prefix}.ados.out.0 ${output_prefix}.ados.out.1 ${output_prefix}.ados.out.2 ${output_prefix}.ados.out.3 ${output_prefix}.dos.out.0 ${output_prefix}.dos.out.1 ${output_prefix}.dos.out.2 ${output_prefix}.dos.out.3 ``` for `*.dos.out.*`, it contains matrix with shape of `(2, numb_dos)`, for `*.ados.out.*`, it contains matrix with shape of `(2, natom x numb_dos)`, ``` # frame - 0: data_dos pred_dos 0.000000000000000000e+00 1.963193264917645342e-03 0.000000000000000000e+00 1.178440836781313727e-03 0.000000000000000000e+00 1.441258071790407769e-04 0.000000000000000000e+00 1.787297933314058174e-03 0.000000000000000000e+00 1.901603280243024940e-03 0.000000000000000000e+00 2.279848925571981155e-03 0.000000000000000000e+00 2.149355854688561607e-03 0.000000000000000000e+00 1.829848459515726056e-03 0.000000000000000000e+00 1.905156512419792225e-03 ```