AI-Driven Structure Refinement of X-ray Diffraction

• URL: http://arxiv.org/abs/2602.16372v2
• Date: Tue, 24 Feb 2026 10:37:35 GMT
• Title: AI-Driven Structure Refinement of X-ray Diffraction
• Abstract summary: We introduce the whole-pattern expectation--maximization algorithm, which turns Bragg's law into an explicit constraint within a batch expectation--maximization framework.<n>By providing Bragg-consistent, uncertainty-aware intensity as a refinement-ready interface, WPEM closes the gap between AI-generated hypotheses and diffraction-admissible structure refinement.
• Score: 6.970927372252305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak intensities cannot be stably assigned under severe overlap and diffraction consistency is enforced only weakly. Here we introduce the whole-pattern expectation--maximization (WPEM) algorithm, a physics-constrained whole-pattern decomposition and refinement workflow that turns Bragg's law into an explicit constraint within a batch expectation--maximization framework. WPEM models the full profile as a probabilistic mixture density and iteratively infers component-resolved intensities while keeping peak centres Bragg-consistent, producing a continuous, physically admissible intensity representation that remains stable in heavily overlapped regions and in the presence of mixed radiation or multiple phases. We benchmark WPEM on standard reference patterns (PbSO$_4$ and Tb$_2$BaCoO$_5$), where it yields lower $R_p/R_{wp}$ than widely used packages (FullProf and TOPAS) under matched refinement conditions. We further demonstrate generality across realistic experimental scenarios, including phase-resolved decomposition in multiphase materials, quantitative recovery of mixture compositions, separation of crystalline peaks from amorphous backgrounds in semicrystalline systems, high-throughput operando lattice tracking, automated refinement of compositionally disordered solid solutions, and quantitative phase-resolved analysis of complex archaeological samples from synchrotron powder XRD. By providing Bragg-consistent, uncertainty-aware intensity partitioning as a refinement-ready interface, WPEM closes the gap between AI-generated hypotheses and diffraction-admissible structure refinement on challenging XRD data.

Related papers

• SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy [14.298763486164377]
  Volume microscopy, including electron and light microscopy, suffers from severe anisotropic resolution due to physical axial sectioning.<n>Existing self-supervised axial super-resolution (ASR) methods face a trilemma bounded by overly smoothed regression textures, structural hallucinations of pure diffusion models, and prohibitive inference latency.<n>We propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level.<n>SkelEM achieves the most favorable balance across the fidelity-perception trade-off among self-supervised methods, with state-of-the-art downstream membrane
  arXiv  Detail & Related papers  (2026-06-29T09:19:04Z)
• XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models [8.082200701430061]
  Generative modeling can provide a prior over atomic structure and learn the mapping from PXRD patterns to crystal structures.<n>We present XRDiff, a diffusion model that recovers crystal structures from PXRD given either the stoichiometry or, in a more challenging setting, the elemental constituents and total number of atoms in the unit cell.
  arXiv  Detail & Related papers  (2026-06-12T00:51:43Z)
• DegBins: Degradation-Driven Binning for Depth Super-Resolution [57.61783461543096]
  DegBins is a novel DSR framework that leverages degradation-driven binning to adaptively enhance residual modeling.<n>DegBins consistently outperforms existing state-of-the-art methods in terms of accuracy, robustness, and generalization.
  arXiv  Detail & Related papers  (2026-05-10T16:17:11Z)
• XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction [7.54947954086392]
  We present XDecomposer, a prior-free framework for joint decomposition and identification of multiphase XRD patterns.<n>Experiments show that XDecomposer substantially improves reconstruction accuracy and phase identification across diverse chemical systems.
  arXiv  Detail & Related papers  (2026-05-07T08:33:56Z)
• Spectral Regularization for Diffusion Models [14.919876123456747]
  We propose a loss-level spectral regularization framework that augments standard diffusion training with differentiable Fourier- and wavelet-domain losses.<n>Our approach is compatible with DDPM, DDIM, and EDM formulations and introduces negligible computational overhead.
  arXiv  Detail & Related papers  (2026-03-02T22:39:02Z)
• From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark [67.29937933325849]
  Operando IR Prediction aims to forecast the time-resolved evolution of spectral fingerprints'' from a single static spectrum.<n>OpIRSpec-7K comprises 7,118 high-quality samples across 10 distinct battery systems.<n>ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines.
  arXiv  Detail & Related papers  (2026-02-20T18:58:43Z)
• 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)
• Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines [4.6894180050630005]
  Training implicit neural representations (INRs) to capture fine-scale details typically relies on iterative backpropagation.<n>We propose ELM-INR, a backpropagation-free INR that decomposes the domain into iterative and fits each local problem using an Extreme Learning Machine (ELM) in closed form.<n>This design yields fast and numerically robust reconstruction by combining local predictors through a partition of unity.
  arXiv  Detail & Related papers  (2026-02-07T16:12:39Z)
• SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC [17.50135994670192]
  We present SpectraFormer, a transformer-based deep learning model that reconstructs the SiC Raman substrate contribution directly from post-growth partially masked spectroscopic data.<n>Subtraction of the reconstructed substrate signal reveals weak vibrational features associated with ZLG that are inaccessible through conventional analysis methods.
  arXiv  Detail & Related papers  (2026-01-07T23:20:19Z)
• SIGMA: Scalable Spectral Insights for LLM Collapse [51.863164847253366]
  We introduce SIGMA (Spectral Inequalities for Gram Matrix Analysis), a unified framework for model collapse.<n>By utilizing benchmarks that deriving and deterministic bounds on the matrix's spectrum, SIGMA provides a mathematically grounded metric to track the contraction of the representation space.<n>We demonstrate that SIGMA effectively captures the transition towards states, offering both theoretical insights into the mechanics of collapse.
  arXiv  Detail & Related papers  (2026-01-06T19:47:11Z)
• Controllable Probabilistic Forecasting with Stochastic Decomposition Layers [1.3995263206621]
  We introduce Decomposition Layers (SDL) for converting deterministic machine learning weather models into ensemble systems.<n>SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling.<n>When applied to WXFormer via transfer learning, SDL requires less than 2% of the computational cost needed to train the baseline model.
  arXiv  Detail & Related papers  (2025-12-21T17:10:00Z)
• A probabilistic foundation model for crystal structure denoising, phase classification, and order parameters [2.4960857119636963]
  Atomistic simulations generate large volumes of noisy structural data.<n>Existing tools such as PTM and CNA are restricted to a small set of hand-crafted lattices.<n>Here we introduce a log-probability foundation model that unifies denoising, phase classification, and OP extraction.
  arXiv  Detail & Related papers  (2025-12-11T19:46:56Z)
• OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction [63.318434943975255]
  We introduce OXtal, a large-scale 100M parameter all-atom diffusion model that learns the conditional joint distribution over intramolecular conformations and periodic packing.<n>By leveraging a large dataset of 600K experimentally validated crystal structures, OXtal achieves orders-of-improvement over prior ab initio machine learning CSP methods.<n> OXtal attains over 80% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization.
  arXiv  Detail & Related papers  (2025-12-07T20:46:30Z)
• Breaking the Modality Barrier: Generative Modeling for Accurate Molecule Retrieval from Mass Spectra [60.08608779794957]
  We propose GLMR, a Generative Language Model-based Retrieval framework.<n>In the pre-retrieval stage, a contrastive learning-based model identifies top candidate molecules as contextual priors for the input mass spectrum.<n>In the generative retrieval stage, these candidate molecules are integrated with the input mass spectrum to guide a generative model in producing refined molecular structures.
  arXiv  Detail & Related papers  (2025-11-09T07:25:53Z)
• Dara: Automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction [2.7076607398164705]
  Dara is a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data.<n>Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, peak-matching-based scoring to identify promising phases for refinement.
  arXiv  Detail & Related papers  (2025-10-22T15:13:47Z)
• Integrable Floquet Time Crystals in One Dimension [0.20524609401792396]
  We demonstrate the realization of a discrete-time crystal (DTC) phase in a family of periodically driven, one-dimensional quadratic lattice Hamiltonians.<n>Our work thus establishes a novel mechanism for realizing robust, long-lived DTCs in one dimension.
  arXiv  Detail & Related papers  (2025-10-05T19:16:10Z)
• Cryo-EM as a Stochastic Inverse Problem [3.7068356204071637]
  Cryo-electron microscopy (Cryo-EM) enables high-resolution imaging of biomolecules.<n>Traditional methods assume a discrete set of conformations, limiting their ability to recover continuous structural variability.<n>We formulate cryo-EM reconstruction as an inverse problem (SIP) over probability measures.<n>We numerically solve using particles to represent and evolve conformational ensembles.
  arXiv  Detail & Related papers  (2025-09-05T23:35:04Z)
• On The Sample Complexity Bounds In Bilevel Reinforcement Learning [49.19950489963245]
  Bilevel reinforcement learning (BRL) has emerged as a powerful framework for aligning generative models.<n>We present the first sample of $mathcalO(epsilon)$ in continuous state-action complexity.<n>Our analysis also extends complexity improving upon existing bounds of $mathcalO(epsilon)$.
  arXiv  Detail & Related papers  (2025-03-22T04:22:04Z)
• Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping [75.72212215739746]
  Trajectory Consistency Distillation (TCD) encompasses trajectory consistency function and strategic sampling. TCD significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model.
  arXiv  Detail & Related papers  (2024-02-29T13:44:14Z)
• Hessian-Free High-Resolution Nesterov Acceleration for Sampling [55.498092486970364]
  Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed. This work explores the sampling counterpart of this phenonemon and proposes a diffusion process, whose discretizations can yield accelerated gradient-based MCMC methods.
  arXiv  Detail & Related papers  (2020-06-16T15:07:37Z)
• Observing localisation in a 2D quasicrystalline optical lattice [52.77024349608834]
  We experimentally and numerically study the ground state of non- and weakly-interacting bosons in an eightfold symmetric optical lattice. We find extended states for weak lattices but observe a localisation transition at a lattice depth of $V_0.78(2),E_mathrmrec$ for the non-interacting system.
  arXiv  Detail & Related papers  (2020-01-29T15:54:42Z)

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.