xtal2png

Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Imagen, DALLE2, or Palette.[1]

Star Follow @sgbaird Issue Discuss

The latest advances in machine learning are often in natural language such as with long short-term memory networks (LSTMs) and transformers or image processing such as with generative adversarial networks (GANs), variational autoencoders (VAEs), and guided diffusion models; however, transfering these advances to adjacent domains such as materials informatics often takes years. xtal2png encodes and decodes crystal structures via grayscale PNG images by writing and reading the necessary information for crystal reconstruction (unit cell, atomic elements, atomic coordinates) as a square matrix of numbers, respectively. This is akin to making/reading a QR code for crystal structures, where the xtal2png representation is invertible. The ability to feed these images directly into image-based pipelines allows you, as a materials informatics practitioner, to get streamlined results for new state-of-the-art image-based machine learning models applied to crystal structure.

Results manuscript coming soon!

xtal2png

Contents

Indices and tables