2511.0031 Equivariant Diffusion Solution for Inorganic Crystal Structure Determination from Powder X-ray Diffraction Data v1

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📋 Summary

This paper introduces XRDSol, a novel approach for solving crystal structures from powder X-ray diffraction (PXRD) patterns using a conditional equivariant diffusion model. The core idea is to train a generative model that refines random atomic coordinates to match a target PXRD pattern while maintaining chemical plausibility. The model leverages the power of equivariant graph neural networks (EGNNs) to learn the complex relationships between atomic coordinates and diffraction patterns. The authors demonstrate the effectiveness of XRDSol on both simulated and experimental PXRD datasets, achieving high success rates and significantly faster solution times compared to traditional methods. Specifically, the model achieves a success rate of 82.3% on a simulated test dataset (MP-20) and 81.6% on an experimental dataset (ICDD-20), with an average solution time of just 0.6 seconds per crystal structure. Furthermore, the paper highlights XRDSol's ability to propose more plausible structure solutions for entries in existing databases with incomplete or questionable information, showcasing its potential for improving the quality of existing crystal structure data. The authors also demonstrate the model's versatility by applying it to different chemistry systems, including light-element materials, natural minerals, and structures with substitutional disorder. The paper provides a detailed description of the model architecture, training procedure, and experimental setup, making it accessible to a broad audience. The results presented in this paper suggest that XRDSol represents a significant advancement in the field of crystal structure determination, offering a fast, accurate, and versatile alternative to traditional methods. The use of a conditional equivariant diffusion model is a notable innovation, and the empirical results demonstrate the practical utility of this approach. The paper also addresses the stochastic nature of diffusion models by running the model multiple times and selecting the best solution based on a similarity metric, which is a practical approach to mitigate the inherent uncertainty in the process. Overall, this work presents a compelling case for the use of machine learning in crystallography, and the results are promising for future applications in materials discovery and characterization.

✅ Strengths

The primary strength of this paper lies in its innovative application of equivariant diffusion models to the challenging problem of crystal structure determination from PXRD patterns. This approach leverages the power of generative models to learn complex patterns in crystal structures and efficiently generate plausible solutions conditioned on experimental data. The use of equivariant graph neural networks (EGNNs) is a key technical innovation, as it allows the model to respect the symmetries of the crystal structure, which is crucial for accurate structure prediction. The experimental results are compelling, demonstrating high success rates on both simulated and experimental PXRD datasets. The authors report a success rate of 82.3% on the simulated MP-20 dataset and 81.6% on the experimental ICDD-20 dataset, which are impressive achievements. The average solution time of 0.6 seconds per crystal structure is a significant improvement over traditional methods, making this approach highly practical for real-world applications. The paper also demonstrates the model's ability to propose more plausible structure solutions for entries in existing databases with incomplete or questionable information, which highlights its potential for improving the quality of existing crystal structure data. The authors revisited 39 entries in the ICSD database and proposed more plausible structures for all of them, which is a significant contribution. Furthermore, the paper demonstrates the model's versatility by applying it to different chemistry systems, including light-element materials, natural minerals, and structures with substitutional disorder. This shows that the model is not limited to a specific type of material and can be applied to a wide range of crystal structures. The paper is also well-written and clearly explains the technical details of the proposed model and the experimental setup. The authors provide sufficient background information to make the paper accessible to a broad audience, while also including detailed technical descriptions for experts in the field. The figures and tables are well-designed and effectively communicate the key findings. The inclusion of supplementary material, such as the performance across different crystal systems and space groups, further enhances the paper's value. The authors also address the stochastic nature of diffusion models by running the model multiple times and selecting the best solution based on a similarity metric, which is a practical approach to mitigate the inherent uncertainty in the process. The supplementary figures showing the performance improvement with increasing numbers of runs further support this approach. Overall, the paper presents a strong case for the use of machine learning in crystallography, and the results are promising for future applications in materials discovery and characterization.

❌ Weaknesses

While the paper presents a compelling approach to crystal structure determination, several limitations warrant careful consideration. First, the method is explicitly limited to structures with less than 20 atoms in the unit cell. This constraint significantly restricts the applicability of the method, as many materials of interest, particularly complex oxides and intermetallics, have larger unit cells. The authors acknowledge this limitation in the "Discussion" section, stating, "The success rate decreases significantly as the number of atoms in the unit cell exceeds twenty." Furthermore, the "Experimental details" section confirms this constraint, noting that the number of atoms should be less than 20 for the unsolved experimental XRD pattern dataset. The paper does not provide a detailed analysis of how the performance degrades as the number of atoms increases beyond this limit, nor does it delve into the computational cost associated with larger unit cells. This lack of analysis makes it difficult to assess the scalability of the method and identify the primary factors limiting its applicability. Second, the paper acknowledges that low symmetry structures pose a challenge for the proposed method. The "Results and discussion" section states, "XRDSol's strong dependence on structural symmetry, with higher success rates for high-symmetry crystals (e.g.,97.7% successrate forcubic)compared tolow-symmetryones (eg., 48.5% success rate for triclinic)." This dependence on symmetry is further supported by Figures S5 and S6 in the supplementary material, which show lower success rates for triclinic systems compared to cubic. While the paper quantifies the difference in success rates between high and low symmetry structures, it does not provide specific examples of failure modes for low symmetry structures or a detailed explanation of why the method struggles with them. This lack of detailed analysis makes it difficult to understand the underlying limitations of the model and develop strategies to overcome them. Third, the inherent stochastic nature of the diffusion model introduces uncertainty in crystal structure solutions, sometimes requiring repeated runs to achieve a satisfactory result. The "Solving details" section mentions that "For each PXRD pattern, we ran XRDSol 25 times, generating 25 crystal structures independently. First, we rank the 25 potential solutions using Rcos (top-25). The solution with the highest Rcos is chosen as the final crystal structure solution of the corresponding given XRD pattern." While the authors address this issue by running the model multiple times and selecting the best result, the paper does not provide a detailed analysis of the variability in the solutions obtained from multiple runs for the same input. Specifically, there is no discussion of the distribution of the solution quality metrics (e.g., Rcos) across multiple runs, and how this variability impacts the reliability of the method. The supplementary figures S32 and S33 show the performance improvement with increasing numbers of runs, but they do not provide a detailed analysis of the variability in the solutions. Finally, while the paper provides a comparison of the solving speed with other methods, it lacks a direct quantitative comparison of the accuracy metrics (like success rate and Rcos) of XRDSol with other state-of-the-art methods. Figure 2a compares the solving speed of XRDSol with AXS, FPASS, and Evolve&Morph, but the paper does not provide a direct quantitative comparison of the accuracy of XRDSol with these other methods. This lack of a direct accuracy comparison makes it difficult to assess the relative performance of XRDSol compared to existing approaches. These limitations, while acknowledged to some extent by the authors, require further investigation and discussion to fully understand the scope and applicability of the proposed method. The confidence level for each of these identified weaknesses is high, as they are directly supported by the paper's content and experimental results.

💡 Suggestions

To address the identified weaknesses, several concrete improvements can be made. First, to tackle the limitation of the method to structures with less than 20 atoms in the unit cell, the authors should investigate the performance of their method on a wider range of crystal structures with varying unit cell sizes. This should include a systematic analysis of the method's performance as a function of the number of atoms in the unit cell. The authors should explore techniques to improve the method's performance on larger unit cells. This could involve incorporating symmetry constraints into the model, or using a hierarchical approach where the structure is built up gradually from smaller units. Furthermore, the authors should explore the computational cost associated with larger unit cells and identify the primary factors limiting the scalability of the method. This could involve analyzing the memory requirements and processing time for different unit cell sizes. Second, to address the challenge of low symmetry structures, the authors should conduct a more detailed analysis of the failure modes for these structures. This should include specific examples of where the method fails and why. The authors should explore techniques to improve the method's performance on low symmetry structures. This could involve incorporating symmetry constraints into the model or using a more sophisticated loss function that is less sensitive to symmetry. A more granular analysis of the model's performance across different crystallographic parameters, such as space groups, would be highly informative. This would reveal potential biases or limitations of the model when dealing with structures of varying symmetry. For instance, do structures with higher symmetry (e.g., cubic) exhibit systematically better performance compared to those with lower symmetry (e.g., triclinic)? Furthermore, analyzing the model's performance based on the number of formula units per unit cell (Z) could also provide valuable insights into its scalability and ability to handle complex unit cells. Third, to address the stochastic nature of the diffusion model, the authors should provide a more detailed analysis of the variability in the solutions obtained from multiple runs for the same input. This should include a discussion of the distribution of the solution quality metrics (e.g., Rcos) across multiple runs, and how this variability impacts the reliability of the method. The authors should explore methods to reduce the stochasticity of the diffusion model, such as using a deterministic sampling scheme or incorporating a refinement step after the initial structure generation. This could involve techniques such as gradient-based optimization or other post-processing methods to improve the consistency and reliability of the solutions. Finally, to provide a more comprehensive evaluation of the method, the authors should include a detailed comparison of the model's performance with other state-of-the-art methods for solving crystal structures from powder X-ray diffraction data. This should include a comparison of the accuracy, efficiency, and robustness of the different methods, as well as a discussion of their limitations. The authors should also discuss the potential impact of their work on the field of materials science and crystallography, and how their method could be used to accelerate the discovery of new materials. This should include a discussion of the potential applications of the method to real-world problems, and the challenges that need to be overcome to make the method more widely applicable. These suggestions are all concrete and actionable, and they directly address the identified weaknesses. Implementing these changes would significantly improve the robustness and applicability of the proposed method.

❓ Questions

Several key questions arise from my analysis of this paper. First, how does the proposed method handle noisy or incomplete powder X-ray diffraction patterns? While the paper demonstrates results on experimental data, it does not explicitly address the robustness of the method to varying levels of noise or missing data. Understanding the limitations of the method in these scenarios is crucial for its practical application. Second, can the method be extended to solve crystal structures with more than 20 atoms in the unit cell, or are there fundamental limitations that prevent this? The paper explicitly states the limitation to structures with less than 20 atoms, but it does not discuss potential strategies to overcome this limitation. Exploring the feasibility of extending the method to larger unit cells is crucial for its broader applicability. Third, how does the method perform on structures with low symmetry, and are there any plans to improve its performance in these cases? The paper acknowledges the challenge with low symmetry structures, but it does not provide specific examples of failure modes or detailed explanations of why the method struggles with them. Understanding the underlying limitations and exploring potential solutions is crucial for improving the method's versatility. Fourth, what are the computational requirements for training and using the model, and how can they be optimized for practical applications? The paper mentions the average solution time and the hardware used, but it does not provide a detailed analysis of the memory requirements or the scalability of the method to larger datasets. Understanding the computational cost is crucial for assessing the practicality of the method. Finally, how does the model compare to other state-of-the-art methods for solving crystal structures from powder X-ray diffraction data in terms of accuracy and efficiency? While the paper provides a comparison of the solving speed, it lacks a direct quantitative comparison of the accuracy metrics with other methods. A more comprehensive comparison is needed to assess the relative performance of the proposed method. These questions target key uncertainties and methodological choices, and seeking clarification on these points would further strengthen the understanding of the proposed method and its potential impact.

📊 Scores

Soundness:3.0
Presentation:3.25
Contribution:3.0
Rating: 7.5

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📋 Summary

The paper proposes XRDSol, a conditional equivariant diffusion model for determining inorganic crystal structures from powder X-ray diffraction (PXRD) patterns. Given a unit cell and stoichiometry, XRDSol iteratively denoises randomly initialized atomic coordinates to produce chemically reasonable structures whose simulated PXRD matches the target pattern. The model uses a wrapped-normal forward process to respect periodicity and an equivariant graph neural network denoiser conditioned on a learned PXRD embedding to predict atomic positions. On a held-out simulated dataset (MP-20; ≤20 atoms), XRDSol achieves an 82.3% structural recovery success rate and 91.5% of reconstructions have PXRD cosine similarity R_cos > 0.9, with average inference time 0.6 s per structure on a V100 GPU. On an experimental dataset (ICDD-20), the success rate is 81.6% with average R_cos of 0.67. The method revisits energetically unfavorable ICSD entries, proposing more plausible structures for at least 39 cases (validated by reduced energy above hull, E_hull, and literature consistency), and completes 912 ICDD entries lacking coordinates with E_hull < 0.1 eV/atom in all cases. The paper discusses performance across symmetry systems (e.g., 97.7% success for cubic and 48.5% for triclinic) and demonstrates cases including light elements, natural minerals, and chemically disordered systems (for which ordered approximations are produced).

✅ Strengths

  • Clear methodological contribution: a PXRD-conditioned, periodic-E(3)-equivariant diffusion model (wrapped-normal forward noise; EGNN denoiser) directly infers coordinates from patterns, unit cell, and stoichiometry (Conditional Equivariant Diffusion section).
  • Strong empirical performance with thorough metrics: 82.3% success on MP-20 and 81.6% on ICDD-20 (Abstract; Ultrafast and High-Quality Crystalline Materials Reconstruction), with multiple structure and pattern metrics (R_cos, MaxDist, sRMS, RMSD) and per-symmetry analysis (Fig. S5, S6).
  • Practical impact demonstrated at scale: revisiting 39 energetically unfavorable ICSD entries with plausible alternatives (e.g., HoGeAg, SrPbF6, CsIO3, NbP) and completing 912 ICDD entries without coordinates, all with E_hull < 0.1 eV/atom (Results; Solving crystal structures without atomic coordinates information).
  • Speed: Average 0.6 seconds per structure (batch size 128, V100), 10^4–10^5× faster than prior DFT-supported optimization approaches (Fig. 2a; Abstract).
  • Robustness on experimental PXRD: The model handles intensity variations (texture/background/polarization effects acknowledged) with reasonable R_cos distributions (Fig. 4a,b) and high-quality reconstructions validated via DFT and literature cross-checks (e.g., (NH4)CoF3, Li2SrGeO4, ramsdellite MnO2).
  • Transparent discussion of scope and challenges: performance degrades for low symmetry and larger unit cells; current inability to natively represent chemical disorder; stochasticity of diffusion (Discussion).

❌ Weaknesses

  • Scope limitations: Restricted to structures with ≤20 atoms per primitive cell; substantial drop in success for low-symmetry systems (e.g., 48.5% for triclinic), and assumes unit cell and stoichiometry are given (Discussion; Fig. S5/S6).
  • Reproducibility and implementation details: The main text lacks complete specifications of the PXRD encoder architecture, EGNN variant and periodic handling (e.g., neighbor definitions, cutoff), diffusion noise schedule, loss formulation, training hyperparameters, random seeds, and data preprocessing for experimental patterns (e.g., background subtraction, 2θ calibration).
  • Limited ablations and failure analysis: No ablations isolating the contribution of equivariance, the PXRD conditioning, or the number of diffusion steps; limited analysis of failure modes (e.g., mis-indexed unit cells, preferred orientation, peak overlap, heavy texture).
  • Comparative evaluation: While speed is compared against classical GA/DFT pipelines (AXS, FPASS, Evolve&Morph), there is no quantitative comparison against recent ML baselines for PXRD-conditioned structure determination (e.g., PXRD-focused generative methods), which would contextualize gains under similar assumptions.
  • Uncertainty and multi-run behavior: The Discussion notes stochasticity may require repeated runs, but the number of restarts and success probabilities vs. runs are not quantified; no uncertainty estimates are provided.
  • Heavy reliance on E_hull for plausibility: Although appropriate and standard, DFT settings, convergence, and reference phase sets are not fully detailed in the main text; providing these would strengthen claims for the 39 corrected ICSD entries and the 912 completed ICDD entries.

❓ Questions

  • PXRD encoder and conditioning: What is the exact architecture of the PXRD encoder (layers, kernel sizes, positional encodings, input normalization)? How is the conditioning fused into the EGNN denoiser (e.g., FiLM, cross-attention, node context embeddings)?
  • EGNN and periodicity: Which equivariant GNN variant is used (e.g., EGNN per Satorras et al.)? How are periodic boundary conditions handled (neighbor finding, cutoff radii, minimal image convention) and ensured to be E(3)-equivariant under PBC?
  • Diffusion details: Please specify the forward noise schedule (variance schedule, number of timesteps), the wrapped-normal parameterization for periodic coordinates, and the denoising objective (e.g., epsilon-prediction vs. x0-prediction). How was T=1000 chosen versus smaller T for speed/accuracy trade-offs?
  • Training protocol: What are the full training hyperparameters (optimizer, LR schedule, batch size used for training, number of epochs/steps), initialization, and random seeds? How many GPUs and wall-clock training time? Any data augmentation on PXRD (background, intensity scaling, peak broadening)?
  • Experimental PXRD preprocessing: How are experimental patterns preprocessed (background subtraction, scaling/normalization, 2θ calibration, polarization correction)? How sensitive is R_cos and structural success to these choices?
  • Unit-cell errors and robustness: Since the method assumes a given unit cell, how sensitive is inference to small lattice parameter errors or wrong space-group assignments? Can the model still solve coordinates if lattice parameters are off by, say, 1–2%? Please provide robustness experiments.
  • Stochasticity and restarts: How many random initializations are typically needed for challenging cases? Please report success rate as a function of the number of denoising runs, and provide guidance for practitioners.
  • Ablations: Can you provide ablations disabling (i) equivariance, (ii) PXRD conditioning, (iii) the wrapped-normal forward process, and (iv) varying diffusion steps, to quantify their impact on success rate and R_cos?
  • Comparisons: Is it possible to benchmark against recent PXRD-conditioned ML baselines (e.g., PXRD-focused generative models) on a shared subset of ICDD-20 or simulated data, to contextualize XRDSol’s accuracy and speed under similar assumptions?
  • DFT validation: Please provide DFT settings (functional, pseudopotentials, k-point meshes, energy/force thresholds), the reference phase sets used for convex hull construction, and sensitivity analysis for E_hull when validating the 39 ICSD corrections and 912 ICDD completions.
  • Chemical disorder and H positions: For disordered systems and hydrogen placement, could you detail guidelines on interpreting XRDSol’s ordered approximations and best practices to follow with Rietveld refinement or complementary data (e.g., neutron) to finalize structures?
  • Code and data release: Will you release code, pretrained weights, and the curated evaluation splits (MP-20 test IDs, ICDD-20 identifiers) and the 912 solved structures to ensure reproducibility?

⚠️ Limitations

  • Assumes known unit cell and stoichiometry; does not infer lattice parameters or space group, which constrains applicability when indexing is ambiguous.
  • Performance degrades for large unit cells (>20 atoms) and low-symmetry systems (e.g., only 48.5% success for triclinic), likely due to the combinatorial growth of configurational space and reduced symmetry constraints.
  • Cannot natively represent chemical disorder; produces ordered approximations that may need careful interpretation and subsequent refinement for disordered materials.
  • Experimental PXRD idiosyncrasies (texture, background, preferred orientation, peak overlap) can reduce R_cos; while structural reasonableness can still be achieved, downstream Rietveld refinement is advisable for final validation.
  • Stochasticity of diffusion necessitates multiple runs in some cases; lack of uncertainty quantification or convergence diagnostics may hinder fully automated deployment.
  • Potential environmental/compute cost for training large diffusion models, though inference is lightweight; careful reporting of training compute would help assess sustainability.

🖼️ Image Evaluation

Cross‑Modal Consistency: 41/50

Textual Logical Soundness: 24/30

Visual Aesthetics & Clarity: 14/20

Overall Score: 79/100

Detailed Evaluation (≤500 words):

Visual ground truth (scan-first)

• Figure 1/(a): S0→ST→S0 cartoon; arrows, “XRD” condition inset; shows forward noise and reverse denoise. (b): Eu2CrSbO6 example; top: bar‑like PXRD profiles with “R=” at t={0,300,600,1000}; bottom: ball‑and‑stick snapshots; evident convergence.

Synopsis: Fig.1 conveys training/conditioning and a single example trajectory from random to solved structure with rising pattern similarity.

• Figure 2/(a): Bar chart “Solutions (per day)” comparing XRDSol vs AXS/FPASS/Evolv&Morph; no numeric ticks. (b): Histogram of Rcos clustered near 1.0; threshold 0.9 marked. (c): Histogram of sRMS with inset pie “82.3%”; (d): Six pairs of structures, rows “Ground truth/Reconstruction”, mp‑IDs shown.

Synopsis: Speed vs baselines; quantitative similarity distributions; visual reconstructions.

• Figure 3/(a): Six side‑by‑side original vs “This work” structures; element legends; Ehull original vs this work annotated. (b): Three hydroxides/imidide with added H; Ehull reductions listed.

Synopsis: Case studies demonstrating corrections and H‑site completion.

• Figure 4/(a): Histogram Rcos for 816 solved ICDD‑20. (b): Histogram Rcos for 912 solved unsolved‑ICDD entries (title says “Unsolved PXRD data”). (c): Histogram Ehull up to 0.1 eV/atom. (d–f): Example structures: light‑elements, minerals, disorder→ordered approximations.

Synopsis: Experimental performance distributions and qualitative examples.

1. Cross‑Modal Consistency

• Major 1: Quantitative verification of the “10^4–10^5× faster” claim is hindered by missing axis values in Fig. 2a. Evidence: Fig. 2a y‑axis lacks numeric ticks/values.

• Minor 1: Metric symbol inconsistency: text defines Rcos, Fig. 1b shows “R=…”. Evidence: Fig. 1b panels show “R = 0.315/0.488/0.981/0.999”.

• Minor 2: Fig. 4b title “Unsolved PXRD data” is ambiguous for results on 912 solved entries. Evidence: Fig. 4b title reads “Unsolved PXRD data”.

2. Text Logic

• Major 1: Overclaim that equivariance “guarantees” crystal symmetries of solutions without explicit space‑group constraints. Evidence: “symmetries … are guaranteed in solutions.” (XRDSol intro paragraph)

• Minor 1: Runtime evidence relies on SI (Fig. S3) not shown here; main text reports 0.6 s but lacks ablation details. Evidence: “The average solution time is only 0.6 seconds … (Fig. S3).”

• Minor 2: Some database‑scale claims (39 corrected, 912 solved) are supported by distributions and examples but lack a consolidated table in main text. Evidence: “at least 39 structures … 912 entries …” (Results).

3. Figure Quality

• Major 1: Element legends/labels in Fig. 3 and Fig. 4(d–f) are very small; critical to interpret corrected chemistries, yet hard to read at print size. Evidence: Fig. 3(a,b) and Fig. 4(d–f) small legends and site labels.

• Minor 1: Several plots lack tick marks/gridlines (Fig. 2b–c, Fig. 4a–c), reducing quantitative readability. Evidence: Histograms show axes with minimal/no ticks.

Key strengths:

• Clear problem framing and strong empirical performance on simulated and experimental data.

• Convincing breadth: corrections of ICSD entries, light‑element handling, mineral impurity robustness, ordered approximations for disorder.

• Well‑structured example trajectory (Fig. 1b) and consistent success metric (MaxDist/sRMS).

Key weaknesses:

• Overstated symmetry “guarantee” without explicit constraints.

• Limited quantitative detail on speed comparison (missing numeric axes/CI, hardware matching).

• Several figures have small, hard‑to‑read legends/labels; ambiguous title in Fig. 4b.

Recommendations:

• Add numeric axes/ticks to Fig. 2a and report exact values/hardware for baselines.

• Use Rcos consistently in figures; clarify Fig. 4b title.

• Temper “guarantee” wording or add explicit symmetry constraints.

• Increase font sizes and provide enlarged legends for structure panels.

📊 Scores

Originality:3
Quality:3
Clarity:3
Significance:3
Soundness:3
Presentation:3
Contribution:3
Rating: 7

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📋 Summary

This paper introduces XRDSol, a novel method for solving crystal structures from powder X-ray diffraction (PXRD) data using a conditional equivariant diffusion model. The core idea is to iteratively refine atomic coordinates, starting from a random configuration, guided by the target PXRD pattern until a chemically reasonable structure that matches the diffraction data is obtained. The method leverages a graph neural network (GNN) to represent the crystal structure and incorporates equivariance to preserve physical symmetries. The authors demonstrate the effectiveness of their approach on both simulated and experimental PXRD datasets, achieving high success rates and reasonable solution times. Specifically, the model shows an 82.3% success rate on the simulated MP-20 dataset and an 81.6% success rate on the experimental ICDD-20 dataset. The paper also highlights the model's ability to propose more plausible structures for entries in the ICSD database with previously identified issues. However, the paper's limitations, particularly in handling chemical disorder and the reliance on known stoichiometry, need to be addressed to fully realize its potential. The lack of detailed comparisons with existing methods and the absence of a clear explanation of the model's architecture further limit the paper's impact. Despite these limitations, the work represents a significant step forward in the application of machine learning to crystal structure determination and has the potential to accelerate the process of materials discovery and characterization.

✅ Strengths

I find the paper's core contributions to be highly innovative and well-executed. The application of a conditional equivariant diffusion model to the problem of crystal structure determination from PXRD data is a novel and promising approach. The authors have successfully demonstrated that their model, XRDSol, can achieve high success rates in solving crystal structures, both on simulated and experimental datasets. The use of equivariant GNNs ensures that the generated structures respect the inherent symmetries of crystals, which is crucial for generating physically meaningful solutions. The paper also provides a clear and detailed description of the training process, including the forward and reverse diffusion steps, and the use of a loss function that combines PXRD pattern matching and coordinate constraints. The empirical results are compelling, with the model achieving an 82.3% success rate on the simulated MP-20 dataset and an 81.6% success rate on the experimental ICDD-20 dataset. The authors also highlight the model's ability to propose more plausible structures for entries in the ICSD database with previously identified issues, which underscores the practical utility of XRDSol in revisiting and refining existing structural data. The paper's clarity in describing the overall workflow and the integration of the diffusion model with the GNN is commendable, making it accessible to readers with varying levels of expertise in machine learning and crystallography. The authors also provide a detailed discussion of the limitations of their approach, which demonstrates a thoughtful and balanced perspective on the current state of the research.

❌ Weaknesses

While the paper presents a compelling approach to crystal structure determination, several limitations need to be addressed to fully realize its potential. One significant weakness is the model's inability to handle chemical disorder. As I observed in the 'Disordered structure' section of the results, XRDSol tends to generate ordered approximations of disordered structures, which can lead to discrepancies between the predicted and actual PXRD patterns. This limitation is explicitly acknowledged by the authors, who state that the model cannot propose structures with chemical disorder because all atoms have pre-assigned element types. This is a critical issue, as many real-world materials exhibit chemical disorder, and the inability to accurately model these structures significantly restricts the applicability of the method. The authors should explore methods to incorporate disorder into their model, such as using a probabilistic approach to assign atom types or employing a mixture model to represent disordered structures. Another major limitation is the model's reliance on known stoichiometry. The paper explicitly states that the model requires stoichiometry as input, which means that it cannot be used for de novo structure determination where the composition is unknown. This reliance on known stoichiometry limits the method's applicability to situations where the chemical formula is already established, and it cannot be used to explore the full space of possible structures. The authors should consider developing methods that can handle unknown or variable stoichiometry, which would significantly broaden the scope of their approach. Additionally, the paper lacks a detailed comparison with existing structure solution methods, particularly those based on genetic algorithms and simulated annealing. While the authors provide a comparison of solution times with methods like AXS, FPASS, and Evolve&Morph, they do not offer a direct comparison of success rates or accuracy. This makes it difficult to assess the true performance of XRDSol relative to established techniques. The paper should include a more comprehensive benchmarking analysis, comparing XRDSol with these methods on a common dataset and using consistent evaluation metrics. Furthermore, the paper does not provide a clear explanation of how the PXRD patterns are generated from the predicted structures, including the specific parameters used for the simulation, such as the wavelength of the X-rays, the instrumental broadening, and the step size for the diffraction angle. This lack of detail makes it difficult to reproduce the results and to assess the validity of the method. The authors should provide a more detailed description of the PXRD simulation process, including the specific parameters used and the method for peak broadening and background noise. The paper also lacks a detailed explanation of the model's architecture, particularly the specific choices of the GNN layers, activation functions, and the number of parameters. The training process, including the optimization algorithm, learning rate, batch size, and the number of training epochs, is not fully described. This lack of detail makes it difficult to assess the reproducibility of the results and to understand the model's behavior. The authors should provide a more detailed description of the model's architecture and training process, including the specific choices of hyperparameters and the rationale behind these choices. Finally, the paper does not provide a detailed analysis of the computational cost of the method, including the time required for training and inference, and how these scale with the size of the unit cell. This information is crucial for assessing the practical applicability of the method. The authors should provide a more detailed analysis of the computational cost and scalability of their approach.

💡 Suggestions

To address the identified limitations, I recommend several concrete and actionable improvements. First, the authors should explore methods to incorporate chemical disorder into their model. One approach could be to use a probabilistic framework where the model predicts the probability of each atom type at each site, rather than a fixed assignment. This would allow the model to capture the inherent uncertainty in disordered structures. Another approach could be to use a mixture model, where the model generates multiple possible structures, each representing a different configuration of the disordered system. The authors should also investigate methods for generating PXRD patterns from disordered structures that accurately reflect the broadening and intensities observed in experimental data. This could involve incorporating models of diffuse scattering or using more sophisticated algorithms for calculating diffraction patterns from disordered systems. Second, the authors should consider developing methods that can handle unknown or variable stoichiometry. This could involve training the model on a dataset that includes structures with varying compositions or incorporating a mechanism for the model to predict the stoichiometry of the crystal structure. This would significantly broaden the applicability of the method and allow it to be used for de novo structure determination. Third, the authors should include a more detailed comparison with existing structure solution methods, particularly those based on genetic algorithms and simulated annealing. This comparison should include a direct assessment of success rates and accuracy, using a common dataset and consistent evaluation metrics. The authors should also provide a detailed analysis of the computational cost of their method compared to these alternatives, including the time required for training and inference, and how these scale with the size of the unit cell. Fourth, the authors should provide a more detailed explanation of how the PXRD patterns are generated from the predicted structures. This should include the specific parameters used for the simulation, such as the wavelength of the X-rays, the instrumental broadening, and the step size for the diffraction angle. The authors should also discuss how they handle peak broadening and background noise in the simulated patterns. This level of detail is crucial for reproducibility and for assessing the validity of the method. Fifth, the authors should provide a more detailed description of the model's architecture, including the specific choices of the GNN layers, activation functions, and the number of parameters. The training process should also be described in more detail, including the optimization algorithm, learning rate, batch size, and the number of training epochs. The authors should also discuss the rationale behind their choices and how they affect the performance of the model. Finally, the authors should consider releasing their code and trained models to the community. This would allow other researchers to reproduce their results and to build upon their work. The authors should also provide clear documentation and examples to facilitate the use of their code. This would significantly increase the impact of their work and accelerate the development of new methods for crystal structure determination.

❓ Questions

I have several questions that I believe would help clarify key aspects of the paper and its methodology. First, I am curious about the specific details of the equivariant neural network used in the model. Could the authors provide more information on the architecture of the equivariant GNN, including the specific layers, activation functions, and the number of parameters? Additionally, how does the model handle the periodic boundary conditions of crystal structures, and how does this impact the equivariance properties of the network? Second, I would like to understand more about the training process. Could the authors provide a more detailed description of the optimization algorithm, learning rate, batch size, and the number of training epochs? How were these hyperparameters chosen, and how do they affect the performance of the model? Third, I am interested in the model's ability to generalize to more complex crystal structures. Could the authors provide a more detailed analysis of the model's performance on structures with varying unit cell sizes and symmetries? How does the model's performance scale with the number of atoms in the unit cell, and what are the limitations of the current approach in handling large and complex structures? Fourth, I am curious about the model's ability to handle experimental PXRD data with varying levels of noise and preferred orientation. Could the authors provide a more detailed analysis of the model's performance on experimental data with different levels of noise and preferred orientation? How does the model handle peak broadening and background noise in the experimental patterns, and what are the limitations of the current approach in handling these challenges? Finally, I would like to understand more about the model's ability to handle structures with partial occupancy and disorder. Could the authors provide a more detailed analysis of the model's performance on structures with partial occupancy and disorder, and how does the model handle these challenges? What are the limitations of the current approach, and what are the potential avenues for future research in this area?

📊 Scores

Soundness:3.0
Presentation:3.0
Contribution:2.75
Rating: 6.0

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