Research map / Probabilistic and scientific modeling / Molecules and proteins
Local-Global Associative Frames for Symmetry-Preserving Crystal Structure Modeling
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes SPFrame, a local-global frame for crystal property prediction that uses invariant local frames (quaternion-based) plus a global QR/PCA-style frame to keep SO(3) invariance without breaking crystal symmetry, applied to eComFormer.
Problem
Equivariant local frames for crystal property prediction can break crystal symmetry, making symmetry-equivalent atoms indistinguishable, while global frames are too coarse to capture local heterogeneity.
Approach
Construct invariant local frames (predicted quaternions from SO(3)-invariant message passing, converted to rotation matrices) that assign identical frames to symmetry-equivalent atoms, combined with a shared equivariant global frame that enforces SO(3) invariance of the whole structure.
Key results
- On JARVIS-DFT, the quaternion-based SPFrame on eComFormer achieves formation energy MAE 0.0261 eV/atom vs 0.0284 for eComFormer and bandgap (MBJ) 0.239 vs 0.283.
- On Materials Project, quaternion-based SPFrame reaches formation energy MAE 0.0171 and bandgap 0.181, best on those two of four tasks.
- Visual example on PrBPt3 (JVASP-16632): equivariant local frame MAE 0.0551 vs SPFrame 0.0503.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add quaternion-based SPFrame to eComFormer | eComFormer · JARVIS-DFT formation energy | MAE (eV/atom): 0.0284 -> 0.0261 | Helped |
| Add quaternion-based SPFrame to eComFormer | eComFormer · JARVIS-DFT total energy | MAE (eV/atom): 0.0315 -> 0.0276 | Helped |
| Add quaternion-based SPFrame to eComFormer | eComFormer · JARVIS-DFT bandgap (OPT) | MAE (eV): 0.124 -> 0.107 | Helped |
| Add quaternion-based SPFrame to eComFormer | eComFormer · MP bandgap | MAE (eV): 0.202 -> 0.181 | Helped |
| Add quaternion-based SPFrame to eComFormer | eComFormer · MP bulk modulus | MAE (log(GPa)): 0.0417 -> 0.0371 | Hurt |
| Add quaternion-based SPFrame to eComFormer | eComFormer · MP shear modulus | MAE (log(GPa)): 0.0729 -> 0.0672 | Helped |
| Replace the SO(3)-equivariant Gram-Schmidt local frame with quaternion-based SPFrame (ablation) | eComFormer · JARVIS-DFT formation energy | MAE (eV/atom): 0.0285 -> 0.0261 | Helped |
| Replace the SO(3)-equivariant Gram-Schmidt local frame with quaternion-based SPFrame (ablation) | eComFormer · MP bandgap | MAE (eV): 0.187 -> 0.181 | Helped |
| Use quaternion-based rather than Gram-Schmidt-based SPFrame (ablation) | eComFormer · JARVIS-DFT formation energy | MAE (eV/atom): 0.0268 -> 0.0261 | Helped |
| Use quaternion-based rather than Gram-Schmidt-based SPFrame (ablation) | eComFormer · MP formation energy | MAE (eV/atom): 0.0174 -> 0.0171 | Helped |
| Use quaternion-based rather than Gram-Schmidt-based SPFrame (ablation) | eComFormer · MP bandgap | MAE (eV): 0.191 -> 0.181 | Helped |
Surprising findings
- On MP bulk and shear modulus, SPFrame does not clearly beat the baselines (e.g., shear modulus 0.0672 vs iComFormer 0.0637), which the authors acknowledge as weaker performance.
Limitations
- Gains on bulk and shear modulus on MP are weak or absent.
- Frame-based variants have more trainable components and are less efficient than the eComFormer backbone, though the quaternion version is cheaper than Gram-Schmidt variants.
- The theoretical superiority argument is a mutual-information sketch that relies on assumptions about distinguishability.
Experimental setup
Datasets: JARVIS-DFT Materials Project
Benchmarks: JARVIS-DFT Materials Project
Metrics: MAE
Baselines: CGCNN SchNet MEGNet GATGNN ALIGNN Matformer PotNet Crystalformer eComFormer iComFormer Crystalframer
Builds on: eComFormer Crystalframer frame averaging canonicalization
Closest papers by meaning
- Space Group Equivariant Crystal Diffusion (NeurIPS 2025 · similarity 0.88)
- Wyckoff Transformer: Generation of Symmetric Crystals (ICML 2025 · similarity 0.88)
- WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry (ICML 2025 · similarity 0.88)
- Conformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance (AAAI 2024 · similarity 0.88)
- Complete and Efficient Graph Transformers for Crystal Material Property Prediction (ICLR 2024 · similarity 0.87)
- SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models (ICLR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
