mlsanityData to 7 Oct 2026

Unified Model for Crystalline Material Generation

arXiv 2306.04510 ↗First posted 7 Jun 2023cond-mat.mtrl-sci cs.LG physics.comp-phcited by 3

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Abstract (arXiv)

One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability. Code and data are available at https://github.com/aklipf/GemsNet.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

9 of 30 references.

References · 9

  1. 2021E(n) Equivariant Graph Neural Networkscited by 951
  2. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  3. 2020Directional Message Passing for Molecular Graphscited by 556
  4. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  5. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
  6. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  7. 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31
  8. 2018Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materialscited by 21
  9. 2023Equivariant Message Passing Neural Network for Crystal Material Discoverycited by 10

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.