mlsanityData to 7 Oct 2026

CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark

arXiv 2407.16131 ↗First posted 23 Jul 2024cond-mat.mtrl-sci cs.LG physics.comp-phcited by 3

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Abstract (arXiv)

The ionic bonding across the lattice and ordered microscopic structures endow crystals with unique symmetry and determine their macroscopic properties. Unconventional crystals, in particular, exhibit non-traditional lattice structures or possess exotic physical properties, making them intriguing subjects for investigation. Therefore, to accurately predict the physical and chemical properties of crystals, it is crucial to consider long-range orders. While GNN excels at capturing the local environment of atoms in crystals, they often face challenges in effectively capturing longer-ranged interactions due to their limited depth. In this paper, we propose CrysToGraph (Crystals with Transformers on Graphs), a novel transformer-based geometric graph network designed specifically for unconventional crystalline systems, and UnconvBench, a comprehensive benchmark to evaluate models' predictive performance on unconventional crystal materials such as defected crystals, low-dimension crystals and MOF. CrysToGraph effectively captures short-range interactions with transformer-based graph convolution blocks as well as long-range interactions with graph-wise transformer blocks. CrysToGraph proofs its effectiveness in modelling unconventional crystal materials in multiple tasks, and moreover, it outperforms most existing methods, achieving new state-of-the-art results on the benchmarks of both unconventional crystals and traditional crystals.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 65 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

26 of 65 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learningcited by 3
  2. 2025Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizerscited by 0
  3. 2026Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loopcited by 0