Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction

• URL: http://arxiv.org/abs/2509.21778v1
• Date: Fri, 26 Sep 2025 02:30:23 GMT
• Title: Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction
• Authors: Bin Cao, Yang Liu, Longhan Zhang, Yifan Wu, Zhixun Li, Yuyu Luo, Hong Cheng, Yang Ren, Tong-Yi Zhang,
• Abstract summary: Crystal property prediction is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory.<n>We introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations.<n>Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance.
• Score: 23.89478649565297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as efficient approximations for large-scale applications, their performance is strongly influenced by the choice of atomic representation. Although modern graph-based approaches have progressively incorporated more structural information, they often fail to capture long-term atomic interactions due to finite receptive fields and local encoding schemes. This limitation leads to distinct crystals being mapped to identical representations, hindering accurate property prediction. To address this, we introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations. To enhance sensitivity to elemental and environmental variations, we employ a data-driven pseudo-particle to generate a synthetic diffraction pattern. PRDNet ensures full invariance to crystallographic symmetries. Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance.

Related papers

• Parallel Complex Diffusion for Scalable Time Series Generation [50.01609741902786]
  PaCoDi is a spectral-native architecture that decouples generative modeling in the frequency domain.<n>We show that PaCoDi outperforms existing baselines in both generation quality and inference speed.
  arXiv  Detail & Related papers  (2026-02-10T14:31:53Z)
• CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning [0.0]
  We introduce CLOUD (Crystal Language mOdel for Unified and Differentiable materials modeling), a transformer-based framework trained on a novel Symmetry-Consistent SCOPE (SCOPE)<n>CLOUD is pre-trained on over six million crystal structures and achieves competitive performance in predicting a wide range of material properties.<n>As proof of concept of differentiable materials modeling, CLOUD is applied to predict the phonon internal energy and heat capacity.
  arXiv  Detail & Related papers  (2025-06-19T15:45:24Z)
• ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction [18.68276260557569]
  We introduce Reciprocal Geometry Network (ReGNet), a novel architecture that integrates geometric GNNs and reciprocal blocks to model short-range and long-range interactions.<n> Experimental results on JARVIS, Materials Project, and MatBench demonstrate that ReGNet achieves state-of-the-art predictive accuracy across a range of crystal property prediction tasks.
  arXiv  Detail & Related papers  (2025-02-04T22:31:39Z)
• CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction [0.0]
  In material science literature, it is well-known that crystalline materials exhibit topological structures. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms.
  arXiv  Detail & Related papers  (2024-09-07T06:58:55Z)
• Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding [10.170537065646323]
  Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. We show that crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in infinitely connected attention. We propose a simple yet effective Transformer-based encoder architecture for crystal structures called Crystalformer.
  arXiv  Detail & Related papers  (2024-03-18T11:37:42Z)
• Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction [62.36797874900395]
  In computational chemistry, crystal structure prediction is an optimization problem. One approach to tackle this problem involves building simulators based on density functional theory (DFT) followed by running search in simulation. We show that our approach, dubbed LCOMs (latent conservative objective models), performs comparably to the best current approaches in terms of success rate of structure prediction.
  arXiv  Detail & Related papers  (2023-10-16T04:35:44Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels [82.53569355337586]
  This work offers an efficient solution to temporal point processes inference using general parametric kernels with finite support. The method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG) Results show that the proposed approach leads to an improved estimation of pattern latency than the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-10T12:35:02Z)
• Transformer with Implicit Edges for Particle-based Physics Simulation [135.77656965678196]
  Transformer with Implicit Edges (TIE) captures the rich semantics of particle interactions in an edge-free manner. We evaluate our model on diverse domains of varying complexity and materials.
  arXiv  Detail & Related papers  (2022-07-22T03:45:29Z)
• Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns [48.53244254413104]
  We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images. We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
  arXiv  Detail & Related papers  (2022-02-01T03:53:39Z)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.