By Battery Power Online Staff
April 9, 2026 | SandboxAQ is releasing AQVolt26, a specialized dataset and suite of machine-learning interatomic potentials (MLIPs) designed to accelerate the discovery of next-generation solid-state battery materials. Containing 322,656 high-fidelity Density Functional Theory (DFT) calculations of lithium halide electrolytes at the rigorous r2SCAN level of theory, AQVolt26 addresses a critical bottleneck in computational materials science: accurately modeling the complex, high-temperature dynamics required to simulate battery performance. Leveraging GCP and NVIDIA DGX H100 cloud hardware, SandboxAQ created AQVolt26 to help transition the industry from slow, iterative lab-based synthesis to rapid, AI-driven computation.
SandboxAQ drives impact at scale through Large Quantitative Models (LQMs), AI models trained on rigorous scientific, rather than linguistic, data. AQVolt26 builds on a multi-year effort to transform battery and energy storage materials R&D through AI LQMs. AQVolt26 brings LQMs into materials discovery for next-generation batteries, complementing prior work in performance prediction and lifecycle modeling.
Challenges in Modeling Solid-State Battery Materials
For the electric vehicle (EV), consumer electronics, defense, grid energy storage markets, the transition to All-Solid-State Batteries (ASSBs) promises higher energy densities and the elimination of flammable liquid electrolytes. Among the leading candidates for solid electrolytes are halides, which offer superior ionic mobility, wide electrochemical stability, and the mechanical deformability necessary to maintain robust interfacial contact within the battery.
Discovering and optimizing these materials requires massive computational screening to project the rate of ion movement. While traditional DFT calculations are prohibitively expensive for large-scale dynamic simulations, SandboxAQ believes AI-driven machine-learned force fields can run these simulations thousands of times faster.
Recent foundational open-source datasets, such as MatPES, MP-ALOE, and the Materials Project, have driven progress in this field, enabling the creation of highly capable, universal foundation potentials with broad coverage of the periodic table. However, dynamically “soft” materials like halogenated solid-state electrolytes present a unique edge case. Their highly polarizable anions create shallow potential energy basins, meaning atoms undergo extreme distortion at the elevated temperatures (>1,000 K) required to computationally simulate ion transport, the company explains.
Foundational potentials, while exceptional for general-purpose applications and stable chemistries, may experience a critical force-energy asymmetry when confronted with these highly specific, far-from-equilibrium states.
How AQVolt26 Solves the High-Temperature Modeling Gap
AQVolt26 does not replace foundational datasets; rather, it serves as a highly targeted complement to resolve this high-temperature blind spot. By specifically mapping the highly anharmonic, molten-sublattice configurational landscape of lithium halide materials, AQVolt26 allows universal models to maintain physical consistency under extreme conditions.
When co-trained with MatPES and MP-ALOE, AQVolt26 models provide three critical advantages for battery development:
- Maps Extreme High-Temperature States: AQVolt26 was generated through surrogate-driven high-temperature phase space exploration. By explicitly exposing the models to highly distorted atomic environments, the AI is prevented from algorithmically failing during the dynamic simulations required to screen active batteries.
- High-Fidelity r2SCAN Calculations: Building on the gold standard established by MatPES, AQVolt26 uses the r2SCAN meta-GGA functional. This ensures a highly accurate representation of the complex coordination environments and mid-range dispersive interactions inherent in soft halide lattices.
- Unprecedented Dynamic Stability: The eSEN models being released demonstrate exceptional algorithmic robustness. In rigorous static stress tests evaluating potential energy surfaces under extreme ±20% lattice deformations (via the public MLIP Arena benchmark), the models achieved a near-zero failure rate of 0.2% and perfect monotonic energy scaling, far outperforming existing baselines.
For battery manufacturers and automotive OEMs, AQVolt26 represents a significant reduction in computational cost and experimental risk, the company claims. By co-training with AQVolt26 alongside near-equilibrium data, they have bridged the gap between strict 0 K ground-state precision and high-temperature dynamic robustness. This allows researchers to confidently run high-throughput screening for ionic conductivity on novel battery materials without sacrificing accuracy or stability.
