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---
license: mit
pipeline_tag: graph-ml
---
# Shift Current Prediction (DPA3-$\sigma$)
This model is based on the DPA3 architecture for predicting shift current in materials.
The training data follow a **long-tail distribution**, thus the model is trained in **log1p space** using `log1p(x) = log(1 + x)`. Predictions are also in log1p space.
## Dependency
Install DeepMD:
```bash
pip install deepmd-kit
````
## Usage
Basic command:
```bash
dp --pt test \
-m model.weights.pt \
-f [INPUT_FILE] \
-n 0 \
-d [OUTPUT_PREFIX]
```
* `-m model.weights.pt`: path to the trained model.
* `-f [INPUT_FILE]`: a text file listing all systems to be evaluated.
* `-d [OUTPUT_PREFIX]`: prefix of the output result files.
Example:
```bash
dp --pt test \
-m model.weights.pt \
-f sys_test.txt \
-n 0 \
-d test_result
```
## Input format
### 1. System list file (`[INPUT_FILE]`)
`[INPUT_FILE]` is a plain text file.
Each line contains the path to a DeepMD-format system directory, for example:
```text
.../mp-14_Se_32_spg152_gap0.88eV/
.../mp-19_Te_32_spg152_gap0.19eV/
.../mp-154_N2_23_spg198_gap7.34eV/
.../mp-181_KGa3_spg119_gap0.22eV/
.../mp-189_SiRu_23_spg198_gap0.23eV/
```
### 2. System directory layout (DeepMD npy format)
Each system directory must follow the standard DeepMD **npy** structure, such as:
```text
system_X/
└── set.000/
├── box.npy
├── coord.npy
├── v.npy
├── type_map.raw
└── type.raw
```
Notes:
* The `.npy` dataset can be converted from VASP using official DeepMD tools.
* A placeholder `v.npy` file is required; writing zeros in it is sufficient.
## Output
Running inference produces a file like:
```text
test_result_property.out.0
```
A typical block looks like:
```text
# /path/to/system_X/: data_property pred_property
0.0000000000000000e+00 2.04...
# /path/to/system_Y/: data_property pred_property
0.0000000000000000e+00 2.35...
```
* Lines starting with `#` indicate the system being evaluated.
* Each numeric line contains the reference value (if available) and the model prediction.
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