# Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws

arXiv: https://arxiv.org/abs/2411.00803 · HTML: https://mlsanity.com/paper/2411.00803/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures](https://mlsanity.com/paper/2609.00663/) (2026)

## Abstract (arXiv)

In this paper, a dataset of one-dimensional powder diffraction patterns was designed with new strategy to train Convolutional Neural Networks for predicting space groups. The diffraction pattern was calculated based on lattice parameters and Extinction Laws, instead of the traditional approach of generating it from a crystallographic database. This paper demonstrates that the new strategy is more effective than the conventional method. As a result, the model trained on the cubic and tetragonal training set from the newly designed dataset achieves prediction accuracy that matches the theoretical maximums calculated based on Extinction Laws. These results demonstrate that machine learning-based prediction can be both physically reasonable and reliable. Additionally, the model trained on our newly designed dataset shows excellent generalization capability, much better than the one trained on a traditionally designed dataset.