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arxiv: 2604.02524 · v1 · submitted 2026-04-02 · ❄️ cond-mat.mtrl-sci · cs.LG

Recognition: 2 theorem links

· Lean Theorem

AQVolt26: High-Temperature r²SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries

Jiyoon Kim , Chuhong Wang , Aayush R. Singh , Tyler Sours , Shivang Agarwal , AJ Nish , Paul Abruzzo , Ang Xiao
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Omar Allam
Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:27 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci cs.LG
keywords halide solid-state electrolytesmachine learning interatomic potentialshigh-temperature samplingr2SCANlithium halidesion transportsolid-state batteriesuniversal potentials
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The pith

Co-training with a new high-temperature halide dataset corrects energy prediction failures in universal machine learning potentials.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper generates the AQVolt26 dataset with 322,656 r²SCAN calculations on lithium halides sampled at high temperatures from roughly 5,000 structures. It tests universal ML interatomic potentials and finds they provide good baselines for stable structures and transfer forces effectively but produce inaccurate absolute energies in the distorted, high-temperature conditions relevant to ion transport. Co-training the models on the new dataset fixes the energy accuracy issue. The findings show that adding near-equilibrium data from projects like Materials Project helps performance close to equilibrium but weakens robustness at extreme strains and does not improve high-temperature forces. Overall, the work concludes that targeted high-temperature configurational sampling is needed to make these potentials reliable for screening halide electrolytes dynamically.

Core claim

Foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for the

What carries the argument

AQVolt26, the dataset of 322,656 high-temperature r²SCAN single-point calculations for lithium halides generated via configurational sampling of approximately 5,000 structures, which augments universal ML interatomic potentials through co-training to correct energy predictions in distorted regimes.

Load-bearing premise

The high-temperature configurational sampling across about 5,000 structures sufficiently captures the distorted regimes and dynamics needed to probe ion transport in halide electrolytes.

What would settle it

A direct test of absolute energy prediction accuracy on an independent collection of high-temperature, highly distorted lithium halide structures, comparing models trained with and without the AQVolt26 data, would confirm whether the reported improvement holds outside the training set.

Figures

Figures reproduced from arXiv: 2604.02524 by Aayush R. Singh, AJ Nish, Ang Xiao, Chuhong Wang, Jiyoon Kim, Omar Allam, Paul Abruzzo, Shivang Agarwal, Tyler Sours.

Figure 1
Figure 1. Figure 1: A summary of the AQVolt26 dataset and models. A configurational landscape of 200 million Li [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the AQVolt26 data generation and training approach (top). A dataset of 322,656 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the distributions of cohesive energies, interatomic force magnitudes, and pressures [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Benchmarking of energy, force, and stress predictions across trained models, evaluated on four [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distributions of structural similarity (top) and formation energy per atom (bottom) when compar [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: NpT MD controlled heating simulations (300–2,100 K) conducted across ML interatomic potentials [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r$^2$SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across $\sim$5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces the AQVolt26 dataset consisting of 322,656 r²SCAN single-point calculations generated from high-temperature configurational sampling of approximately 5,000 lithium halide structures. It claims that universal ML potentials trained on foundational datasets transfer local forces adequately but exhibit degraded absolute energy accuracy in distorted high-temperature regimes relevant to ion transport; co-training on AQVolt26 resolves this limitation. It further reports that augmenting with Materials Project relaxation data improves near-equilibrium performance while degrading extreme-strain robustness and high-temperature force accuracy, concluding that domain-specific high-temperature sampling is essential for reliable dynamic screening of halide solid-state electrolytes.

Significance. If the quantitative performance claims are substantiated with error metrics and coverage analysis, the work supplies a targeted dataset that could improve the reliability of ML potentials for anharmonic, high-temperature simulations of soft halide electrolytes. The empirical comparison of co-training versus foundational data alone provides a concrete example of when domain-specific augmentation is required, which may guide future dataset curation for solid-state battery materials. The release of the r²SCAN data at this scale is a positive contribution to the community.

major comments (3)
  1. [Abstract] Abstract: The central claim that co-training with AQVolt26 resolves absolute-energy degradation in distorted higher-temperature regimes is stated without any quantitative metrics (e.g., energy MAE or force RMSE values, with or without error bars) comparing baseline and co-trained models, making it impossible to evaluate the magnitude or statistical significance of the reported improvement.
  2. [Abstract / §3] Abstract / §3 (sampling description): The assertion that the ~5K high-temperature structures sufficiently populate the anharmonic and diffusive configurations responsible for baseline-model failure lacks supporting details on temperature range, MD thermostat/timestep, active-learning criteria, or quantitative coverage diagnostics (pair-distance histograms, coordination-number fluctuations, or soft-mode participation ratios) relative to the observed failure modes.
  3. [Abstract / Results] Abstract / Results section on Materials Project augmentation: The statement that MP relaxation data degrades extreme-strain robustness is presented without specifying the strain magnitudes tested or providing comparative error statistics (energy/force errors at high strain) that would allow assessment of the claimed trade-off.
minor comments (2)
  1. [Tables] Ensure all performance tables include error bars or standard deviations from multiple runs or cross-validation folds.
  2. [Methods] Clarify the exact definition of 'extreme-strain' configurations and how they differ from the high-temperature sampling protocol.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments highlight opportunities to strengthen the quantitative presentation and methodological transparency of the work. We address each major comment below and will incorporate revisions to improve clarity and substantiation without altering the core findings.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that co-training with AQVolt26 resolves absolute-energy degradation in distorted higher-temperature regimes is stated without any quantitative metrics (e.g., energy MAE or force RMSE values, with or without error bars) comparing baseline and co-trained models, making it impossible to evaluate the magnitude or statistical significance of the reported improvement.

    Authors: We agree that the abstract would benefit from explicit quantitative metrics. The full manuscript reports energy MAE and force RMSE values (with standard deviations) for baseline foundational models versus co-trained models in Section 4 and the associated figures. In the revised version we will add a concise summary of these key metrics (including error bars) directly into the abstract to allow immediate evaluation of the improvement magnitude. revision: yes

  2. Referee: [Abstract / §3] Abstract / §3 (sampling description): The assertion that the ~5K high-temperature structures sufficiently populate the anharmonic and diffusive configurations responsible for baseline-model failure lacks supporting details on temperature range, MD thermostat/timestep, active-learning criteria, or quantitative coverage diagnostics (pair-distance histograms, coordination-number fluctuations, or soft-mode participation ratios) relative to the observed failure modes.

    Authors: We acknowledge that additional sampling details will improve transparency. Section 3 of the manuscript describes the high-temperature configurational sampling procedure; we will expand this section to explicitly state the temperature range, MD thermostat and timestep settings, and any active-learning criteria employed. We will also add quantitative coverage diagnostics, including pair-distance histograms and coordination-number fluctuation statistics, to demonstrate how the sampled configurations address the anharmonic regimes where baseline models fail. revision: yes

  3. Referee: [Abstract / Results] Abstract / Results section on Materials Project augmentation: The statement that MP relaxation data degrades extreme-strain robustness is presented without specifying the strain magnitudes tested or providing comparative error statistics (energy/force errors at high strain) that would allow assessment of the claimed trade-off.

    Authors: We agree that the strain magnitudes and comparative statistics should be stated explicitly. The manuscript already contains the underlying error data for high-strain configurations; we will revise the relevant Results subsection and abstract to report the specific strain magnitudes tested (tensile and compressive) together with the corresponding energy and force error statistics for models trained with and without MP relaxation data. This will allow readers to evaluate the reported trade-off directly. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset generation and empirical ML evaluation are independent of fitted inputs

full rationale

The paper generates a new r²SCAN dataset via high-temperature configurational sampling (~5K structures, 322k points) and reports empirical performance of ML potentials trained on foundational data versus co-training with AQVolt26. No derivations, equations, or predictions are presented that reduce by construction to the paper's own inputs or fitted parameters. Claims rest on held-out test metrics for energies and forces rather than self-referential definitions. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked. This matches the expected non-circular outcome for a dataset-plus-benchmarking manuscript.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the representativeness of the high-temperature sampling and the accuracy of r2SCAN for generating training labels in halide systems.

axioms (1)
  • domain assumption r2SCAN density functional theory calculations provide sufficiently accurate energies and forces for training ML potentials on lithium halides
    All 322,656 data points in AQVolt26 are generated with r2SCAN single-point calculations.

pith-pipeline@v0.9.0 · 5595 in / 1315 out tokens · 47138 ms · 2026-05-13T20:27:53.680567+00:00 · methodology

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