Title: Geometric Mixture Models for Electrolyte Conductivity Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Geometric Graph Neural Networks

Mixture Property Prediction

Conductivity Prediction
Abstract: Accurate prediction of ionic conductivity in electrolyte systems is crucial for advancing numerous scientific and technological applications. While significant progress has been made, current research faces two fundamental challenges: (1) the lack of high-quality standardized benchmarks, and (2) inadequate modeling of geometric structure and intermolecular interactions in mixture systems. To address these limitations, we first reorganize and enhance the CALiSol and DiffMix electrolyte datasets by incorporating geometric graph representations of molecules. We then propose GeoMix, a novel geometry-aware framework that preserves Set-SE(3) equivariance—an essential but challenging property for mixture systems. At the heart of GeoMix lies the Geometric Interaction Network (GIN), an equivariant module specifically designed for intermolecular geometric message passing. Comprehensive experiments demonstrate that GeoMix consistently outperforms diverse baselines (including MLPs, GNNs, and geometric GNNs) across both datasets, validating the importance of cross-molecular geometric interactions and equivariant message passing for accurate property prediction. This work not only establishes new benchmarks for electrolyte research but also provides a general geometric learning framework that advances modeling of mixture systems in energy materials, pharmaceutical development, and beyond.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes GeoMix, a novel geometry-aware framework that preserves Set-SE(3) equivariance for accurate prediction of ionic conductivity in mixture electrolyte systems. The authors curate and standardize data from two existing works, CALiSol and DiffMix, and augment the molecular representation by constructing geometric graphs where atoms are characterized by both invariant features and equivariant features. Then the authors develop GeoMix which maintains Set-SE(3) equivariance while capturing fine-grained geometric relationships between molecules, overcoming the limitations of existing methods. The extensive experimental results demonstrate the superior performance of GeoMix over baseline models, demonstrating its effectiveness in modeling mixture systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper addresses an important and underexplored problem in machine learning for chemistry: predicting electrolyte properties from complex mixtures with geometric and compositional diversity. By introducing a mixture-level graph that includes atomic interactions across multiple molecules, the authors move beyond traditional molecular featurization approaches that treat molecules as independent graphs.\n\n1. The paper introduces Geometric Interaction Network (GIN) which models the molecular interactions by intermolecular geometric message passing, surpassing previous methods which only learn a global feature vector of molecules while overlooking fine-grained and full-atom interactions across molecules. \n\n1. The denoise loss adds a regularization that encourages robustness to small geometric perturbations, which is especially relevant in modeling systems with inherent thermal fluctuations and conformational noise.\n\nWeaknesses:\n1. Unlike DiffMix, which fits a VFT equation to enforce physically consistent trends across temperature, GeoMix treats temperature as a scalar input without enforcing any physical constraints. As a result, predictions at different temperatures may be inconsistent. \n\n1. GeoMix constructs a mixture-level graph by placing individual molecules randomly in 3D space and connecting atoms based on proximity. However, the relative positions of molecules in a mixture are governed by thermodynamic distributions and local structuring (e.g., solvation shells), which are not captured by this random placement. Without sampling from realistic ensembles, such as those obtained via molecular dynamics, the constructed graphs may not reflect statistically meaningful intermolecular interactions.\n\nQUESTIONS:\n1. GeoMix reports mean squared error (MSE = 0.035), while DiffMix reports mean absolute error (MAE = 0.044), which corresponds to an estimated MSE of around 0.002 by a rough estimation. While both the two paper present near perfect regression based on the true values vs predicted values plot, could this be due to differences in normalization, target scaling, or task formulation, and how should we interpret these metrics fairly across the two papers?\n\n1. The GeoMix paper uses a different data split strategy from the original DiffMix paper, despite DiffMix providing the random seed for reproducibility. Therefore, is any reason for not using the same data split as DiffMix which could have been included as a baseline model?\n\n1. GeoMix generates mixture geometries by randomly translating and rotating molecular conformers, forming mixture-level graphs from a single such placement. Is there any investigation in how sensitive the model is to this sampling strategy?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeoMix, a geometry-aware framework for predicting ionic conductivity in multi-molecule electrolyte mixtures. GeoMix first augments two public datasets (CALiSol and DiffMix) with geometric graphs, then couples an intra-molecular equivariant backbone (EGNN or TFN) with a novel Geometric Interaction Network (GIN) that passes messages between molecules through learned rotation/translation operators that respect Set-SE(3) symmetry. Across both datasets, GeoMix-TFN reduces test-set MSE by a large margin relative to the strongest geometric baselines, establishing new state-of-the-art performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ Strong theoretical underpinning and clear derivations.\n+ Large performance gains on two datasets with code released.\n+ Careful ablations isolate the contribution of each design choice.\n\nWeaknesses\n+ **Dataset Curation and Conformational Noise**. One potential concern is the dataset curation step, which may introduce conformational noise, especially when molecules with multiple low-energy conformers are considered. The paper does not discuss how multiple conformers are treated—whether they are averaged, a representative structure is selected, or each conformer is treated as a separate training sample. Does this affect the quality of the dataset?\n+ **Complexity analysis absent.** The paper provides an in-depth explanation of the network architecture, but there is no discussion of the computational complexity of the GeoMix framework in terms of time and memory consumption. With potentially high-order backbone networks, what is the complexity of the overall proposed method?\n\nQUESTIONS:\n1. \"For molecules not found in PubChem, we generate 3D structures 246 using RDKit [29] or download conformations from the Materials Project [30].\" -- How do you handle cases where a single molecule has multiple low-energy conformers? Do you average over them, select a representative structure, or treat each conformer as a separate training sample?\n\n2. How does GeoMix scale with mixture size M and total atoms N in practice (time/GB)? What is the asymptotic time/space complexity of the GIN with respect to M and N?\n\n3. In Eq. (3) you state that the symmetry is defined such that each molecule is independently SE(3)-equivariant. Could you clarify why independent equivariance is reasonable? In realistic systems, inter-molecular interactions can couple the components.\n\nLIMITATIONS:\nYes, the authors have discussed their limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeoMix, a novel geometric framework for predicting properties of molecular mixtures, specifically focusing on electrolyte conductivity. The authors address two key challenges: (1) the lack of standardized benchmarks in the field, and (2) inadequate modeling of geometric interactions between molecules in mixture systems. They curate two datasets (CALiSol and DiffMix) and propose a Geometric Interaction Network (GIN) that maintains Set-SE(3) equivariance while enabling fine-grained intermolecular geometric message passing through learnable coordinate transformations.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n1. **Benchmark Standardization**: The effort to curate and standardize the CALiSol and DiffMix datasets is commendable and provides significant value to the community. The careful preprocessing with geometric graph construction addresses a real need in the field.\n\n2. **Novel Geometric Framework**: The learnable transformation approach between molecular coordinate systems is innovative. The mathematical formulation of Set-SE(3) equivariance for mixture systems is rigorous and well-motivated.\n\n3. **Strong Empirical Results**: GeoMix consistently outperforms diverse baselines, achieving significant improvements (e.g., MSE of 0.432 vs 1.107 on CALiSol).\n\n4. **Extensive Comparison on Learnable Transformation** Comparison with different learnable transformation (quaternions etc.) is interesting and commendable.\n\n5. **Thorough ablations & theory appendix**: Clarifies why non‑orthogonal matrices increase expressivity and proves equivariance.\n\n**Weakness**\n1. **Limited Physical Interpretability**:  The physical meaning of  the learnable transformations is unclear. The paper doesn't discuss what these transformations represent  in terms of actual molecular interactions.\n\n2. **Computational Complexity**: The method requires computing transformations for all atom pairs across molecules, which could be computationally expensive for large systems. The paper doesn't discuss scalability or computational costs. Furthermore, no runtime analysis or comparison with baselines.\n\n3. **Dataset Limitations**: Generalisation outside Li‑ion solvents untested. Both datasets are Li‑salt carbonates/ethers; performance on, e.g., Na‑ion or ionic‑liquid systems is unknown.\n\nQUESTIONS:\n1. Why use PCA for frame construction? Have you tried other methods (e.g., based on molecular moments of inertia)? How does the model behave for e.g. spherical molecules (PC) where PCA axes are arbitrary? \n2. Could you visualize some learned transformation matrices to see if they capture meaningful patterns? Does those transformations correlate with MD‑derived relative orientations?\n3. Can the model extrapolate to mixtures with more components than training data?\n4. Can you report “train on DiffMix, test on CALiSol” and vice‑versa, or a leave‑one‑solvent‑family‑out split?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GeoMix, an equivariant neural network model for predicting the electrical conductivity of electrolyte solutions. A systematic comparison of GeoMix with various neural network models, including MLP-based, topology-based, and geometry-based architectures, is conducted on the task of electrical conductivity prediction. Furthermore, the study supplements the CALiSol-23 and DiffMix datasets with relevant information, enhancing the future evaluation of predictive capabilities on these datasets.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\nThis work introduces a novel equivariant 3D graph neural network model for predicting the electrical conductivity of mixed molecular systems. The proposed model achieves state-of-the-art (SOTA) prediction accuracy on both the CALiSol-23 and DiffMix datasets.\n\n## Weaknesses\n\n**Major issues**\n\n1. The evaluation of electrical conductivity prediction accuracy in this work relies solely on random splitting within the CALiSol-23 and DiffMix datasets. This approach risks yielding overly optimistic results, as evidenced by the Pearson r exceeding 0.98 for GeoMix and generally above 0.95 for baseline models in Table 1. In practical electrical conductivity prediction (and molecular property prediction in general), models frequently encounter out-of-sample scenarios, such as predicting the conductivity of: unknown electrolytes in known solvents, known electrolytes in unknown solvents, or novel combinations of both unknown electrolytes and solvents. Therefore, it is recommended that the authors explicitly designate specific electrolyte and solvent molecules to be held out exclusively in test set for evaluating the model's predictive performance under these out-of-sample conditions.\n\n2. Predicting electrolytes with higher conductivity is a critical scientific objective. To assess this capability, it is recommended that the authors partition a subset of high-conductivity data as an independent test set, utilizing only low-conductivity data for training and validation. Specifically, for the CALiSol-23 dataset, data points with conductivity >20 mS/cm could be reserved solely for testing. Similarly, for the DiffMix dataset, data points with conductivity >10 mS/cm could be reserved for testing. This strategy would provide a meaningful assessment of the model's extrapolation performance.\n\n3. Upon reviewing the provided data and code, it was observed that for all lithium salts, only the 3D conformations of their anionic moieties were generated. While this approach might be adequate for the current dataset containing solely lithium cations, its applicability becomes significantly limited when datasets incorporate other cations, thereby severely constraining the model's generalization ability. Although generating accurate 3D structures for complete ionic compounds (containing both cation and anion) presents challenges, the authors should nevertheless consider and address how to effectively integrate the structural features of diverse cations into the model.\n\n**Minor issues**\n\n1. The term \"DiffMix\" is used to refer to both a dataset and a model in the manuscript, which may cause confusion. It is recommended to distinguish them by adding prefixes or suffixes\n\n2. The authors' expansion of the CALiSol-23 and DiffMix datasets appears limited to supplementing 3D molecular coordinates (without DFT optimization and excluding cation structures for ionic compounds). If accurate, the claims regarding dataset contributions should be moderated.\n\n3. Although no implementation is available for the DiffMix model, its original publication reports a conductivity prediction MAE of 0.044 mS/cm, a result comparable to or potentially better than the best performance in this work. The authors should compute and report GeoMix's MAE on the DiffMix dataset to enable direct comparison.\n\n4. The page number of reference 10 is incorrect, and the issue number of reference 11 is redundant ('(1)' should be deleted). Please check the accuracy of all references thoroughly.\n\n5. In the Checklist section, the author seems to have provided two code repositories, https://anonymous.4open.science/r/GeoMix-74AC/ and https://anonymous.4open.science/r/GeoMix-73AC/. The former is an invalid address. Please correct it.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-09"}