---
pretty_name: "SIB-CL Datasets"
license: apache-2.0
tags:
- photonic-crystal
- quantum
- contrastive-learning
- scientific-machine-learning
- hdf5
- pytorch
- science
- physics
---

# SIB-CL Datasets

This repository contains the **Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL)** datasets for two scientific problems:

* **PhC2D**: 2D photonic crystal density-of-states (DOS) and bandstructure data.
* **TISE**: 3D time-independent Schrödinger equation eigenvalue and eigenvector solutions.

The data and loader scripts reproduce the behavior of the original PyTorch `Dataset` classes from the SIB-CL paper:

> Loh, C., Christensen, T., Dangovski, R., Kim, S., & Soljačić, M. (2022). "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science." *Nature Communications*, 13, 4223. [https://www.nature.com/articles/s41467-022-31915-y](https://www.nature.com/articles/s41467-022-31915-y)

## Repository Structure

* `phc2d.h5`: HDF5 with groups:

  * `unitcell/mpbepsimage/<index>`: 32×32 permittivity images
  * `unitcell/epsavg/<index>`: scalar average permittivity
  * `mpbcal/DOS/<index>`, `mpbcal/DOSeldiff/<index>`: DOS arrays
  * `mpbcal/efreq/`, `mpbcal/emptylattice/`, `mpbcal/eldiff/` for bandstructure

* `tise.h5`: HDF5 with groups:

  * `unitcell/potential_fil` (or `_res<downres>`): potential arrays
  * `eigval_fil/<index>`: eigenvalues
  * `eigvec_fil/<index>`: eigenvectors


## Processing & Training

For processing pipelines and model training code, see the SIB-CL GitHub repository:
[https://github.com/clott3/SIB-CL](https://github.com/clott3/SIB-CL)

---

**Citation**:
Loh, C., Christensen, T., Dangovski, R., Kim, S., & Soljačić, M. (2022). "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science." *Nature Communications*, 13, 4223. [https://www.nature.com/articles/s41467-022-31915-y](https://www.nature.com/articles/s41467-022-31915-y)