XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures

• URL: http://arxiv.org/abs/2604.12140v1
• Date: Mon, 13 Apr 2026 23:42:14 GMT
• Title: XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
• Abstract summary: XANE(3) is a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly from atomic structures.<n>We evaluate the model on a dataset of 5,941 FDMNES simulations of iron oxide surface facets and obtain a spectrum mean squared error of $1.0 times 10-3$ on the test set.
• Score: 5.177624503826816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present XANE(3), a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly from atomic structures. The model combines tensor-product message passing with spherical harmonic edge features, absorber-query attention pooling, custom equivariant layer normalization, adaptive gated residual connections, and a spectral readout based on a multi-scale Gaussian basis with an optional sigmoidal background term. To improve line-shape fidelity, training is performed with a composite objective that includes pointwise spectral reconstruction together with first- and second-derivative matching terms. We evaluate the model on a dataset of 5,941 FDMNES simulations of iron oxide surface facets and obtain a spectrum mean squared error of $1.0 \times 10^{-3}$ on the test set. The model accurately reproduces the main edge structure, relative peak intensities, pre-edge features, and post-edge oscillations. Ablation studies show that the derivative-aware objective, custom equivariant normalization, absorber-conditioned attention pooling, adaptive gated residual mixing, and global background term each improve performance. Interestingly, a capacity-matched scalar-only variant achieves comparable pointwise reconstruction error but reduced derivative-level fidelity, indicating that explicit tensorial channels are not strictly required for low intensity error on this dataset, although they remain beneficial for capturing finer spectral structure. These results establish XANE(3) as an accurate and efficient surrogate for XANES simulation and offer a promising route toward accelerated spectral prediction, ML-assisted spectroscopy, and data-driven materials discovery.

Related papers

• A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy [6.188951069930088]
  Laser-induced breakdown spectroscopy (LIBS) faces challenges in high-accuracy quantitative measurement under few-shot scenarios.<n>Traditional preprocessing methods fail to preserve subtle spectral features or capture nonlinear correlations.<n>This work proposes a standardized processing pipeline integrating diffusion-based denoising, attention-based autoencoder for dimensionality reduction, group shuffling data augmentation, and ordinary least squares regression.
  arXiv  Detail & Related papers  (2026-06-20T09:19:02Z)
• Graph Diffusion Residuals for Control-Function Instrumental Variables [82.39671985893028]
  High-capacity first stages can interpolate treatment and leave too little residual information for the outcome equation.<n>We study Adaptive Anisotropic Instrumental Heat Flow (A-IHF), a deterministic graph-diffusion residual extractor for flexible control functions.<n>A-IHF treats treatment as a signal on a graph of first-stage features, uses pilot diffusion to detect large treatment jumps, attenuates conductance across those jumps, and computes the generated control with a sparse graph resolvent.
  arXiv  Detail & Related papers  (2026-06-12T17:02:53Z)
• Coverage Analysis of Rydberg Atom Quantum Receiver Arrays: A Stochastic Geometry Approach [55.47164192369501]
  Rydberg atomic quantum receivers (RAQRs) offer quantum-limited sensitivity and broadband tunability.<n>In dense deployments, aggregate interference can push atomic transducer out of its small-signal regime.<n>This paper addresses the question by embedding the RAQR front end into a geometry coverage analysis.
  arXiv  Detail & Related papers  (2026-05-22T04:05:13Z)
• Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models [0.6487259764989486]
  This study introduces the Spectral Model eXplainer (SMX), a post-hoc, global, model-agnostic XAI framework that explains spectral classifiers through expert-informed spectral zones.<n>SMX summarizes each zone via PCA, defines quantile-based logical predicates, estimates relevance with perturbations, and aggregates bag-wise rankings in a directed weighted graph.
  arXiv  Detail & Related papers  (2026-05-04T15:01:04Z)
• FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time [52.735012862324766]
  We present FRIGID, a framework with a novel diffusion language model that generates structures conditioned on mass spectra.<n>We show how forward fragmentation models enable inference-time scaling by identifying spectrum-inconsistent fragments.<n>Further empirical analyses show that FRIGID exhibits log-linear performance scaling with increasing inference-time compute.
  arXiv  Detail & Related papers  (2026-04-17T19:11:18Z)
• Spectral Gating Networks [65.9496901693099]
  We introduce Spectral Gating Networks (SGN) to introduce frequency-rich expressivity in feed-forward networks.<n>SGN augments a standard activation pathway with a compact spectral pathway and learnable gates that allow the model to start from a stable base behavior.<n>It consistently improves accuracy-efficiency trade-offs under comparable computational budgets.
  arXiv  Detail & Related papers  (2026-02-07T20:00:49Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• Data-Driven Graph Filters via Adaptive Spectral Shaping [10.449640808601199]
  We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering.<n>The framework provides compact spectral modules that plug into graph signal processing pipelines and graph neural networks.
  arXiv  Detail & Related papers  (2026-02-03T16:20:49Z)
• Variational Autoencoder with Normalizing flow for X-ray spectral fitting [0.0]
  Black hole X-ray binaries (BHBs) can be studied with spectral fitting to provide physical constraints on accretion in extreme gravitational environments.<n>We introduce a probabilistic model, utilizing a variational autoencoder with a normalizing flow, trained to adopt a physical latent space.
  arXiv  Detail & Related papers  (2026-01-12T11:28:40Z)
• The Vekua Layer: Exact Physical Priors for Implicit Neural Representations via Generalized Analytic Functions [0.0]
  Implicit Neural Representations (INRs) have emerged as a powerful paradigm for parameterizing physical fields.<n>We introduce a differentiable spectral method grounded in the Generalized Analytic theory.<n>We show that our method can effectively act as a physics-informed spectral filter.
  arXiv  Detail & Related papers  (2025-12-11T21:57:21Z)
• Latent Dirichlet Transformer VAE for Hyperspectral Unmixing with Bundled Endmembers [1.9336815376402718]
  We propose the Latent Dirichlet Transformer Variational Autoencoder (LDVAE-T) for hyperspectral unmixing.<n>Our model combines the global context modeling capabilities of transformer architectures with physically meaningful constraints imposed by a Dirichlet prior in the latent space.<n>We evaluate our approach on three benchmark datasets, Samson, Jasper Ridge, and HYDICE Urban.
  arXiv  Detail & Related papers  (2025-11-21T20:15:37Z)
• SPECTRA: Spectral Target-Aware Graph Augmentation for Imbalanced Molecular Property Regression [45.62053904749856]
  SPECTRA is a Spectral Target-Aware graph augmentation framework.<n>It generates realistic molecular graphs in the spectral domain.<n>It consistently improves error in relevant target ranges while maintaining competitive overall MAE.
  arXiv  Detail & Related papers  (2025-11-06T21:57:21Z)
• DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models [68.19129717255053]
  We present DiffSpectra, a generative framework that formulates molecular structure elucidation as a conditional generation process.<n>Our experiments demonstrate that DiffSpectra accurately elucidates molecular structures, achieving 40.76% top-1 and 99.49% top-10 accuracy.
  arXiv  Detail & Related papers  (2025-07-09T13:57:20Z)
• Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table [49.65586812435899]
  XAStruct is a learning-based system capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input.<n>XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table.
  arXiv  Detail & Related papers  (2025-06-13T15:58:05Z)
• Geometric Algebra Planes: Convex Implicit Neural Volumes [70.12234371845445]
  We show that GA-Planes is equivalent to a sparse low-rank factor plus low-resolution matrix. We also show that GA-Planes can be adapted for many existing representations.
  arXiv  Detail & Related papers  (2024-11-20T18:21:58Z)
• Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space [54.13671100638092]
  Holistic Physics Mixer (HPM) is a framework for integrating spectral and physical information in a unified space.<n>We show that HPM consistently outperforms state-of-the-art methods in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)

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.