Eibar Flores

Binder selection strongly influences silicon–graphite (Si–Gr) anode performance, yet the relationship between slurry processing, electrode structure, and electrochemical behaviour remains unclear. Here, we systematically investigate the influence of binder chemistry and binder pH on Si–Gr composite anodes using unmodified poly(acrylic acid) (PAA), lithiated PAA (LiPAA) at pH 5–7, and a conventional carboxymethyl cellulose (CMC) binder. Rheological, adhesion, zeta potential and microscopy analysis reveal that binder pH strongly governs slurry processability, binder–component interactions, and the resulting electrode structure. These differences were found to directly influence electrochemical performance in full cells versus NMC532. Among the binders studied, partially neutralised LiPAA at pH5 exhibited the most stable cycling performance, combining the favourable binder interactions associated with free acid PAA with the improved slurry processability required for practical electrode manufacturing. In contrast, unmodified PAA showed favourable electrochemical behaviour but poor rheological stability, while increasing binder pH resulted in progressively poorer electrode integrity, increased polarisation, and accelerated capacity fade. Differential capacity and electrochemical impedance analyses further revealed substantial differences in the electrochemical response and interfacial stability of the electrodes, while post‐mortem cross‐sectional scanning electron microscopy showed preservation of the electrode structure at pH 5 and severe structural degradation at pH 7 after 50 cycles. Overall, these results highlight the critical role of binder‐controlled processing in determining the stability of Si–Gr composite anodes by demonstrating how binder pH influences slurry interactions, electrode structure, and the subsequent electrochemical behaviour during cycling.

Incremental capacity (IC) analysis is a widely used non-destructive method to characterize the state of Li-ion batteries. However, there is no standard approach to compute IC curves robustly and reproducibly. Most implementations rely on numerical differentation, which amplifies measurement noise and thus requires extensive noise filtering. The smoothing method and associated parameters are selected by trial-and-error, producing inconsistent ad hoc IC curves hindering reliable comparison across studies. We promote voltage histograms as a simple alternative for computing IC curves and introduce voltage reconstruction as an objective criterion to assess their quality. Histogram-based ICs use the intuitive bin size as the only parameter, are robust to voltage noise, require little or no filtering, handle non-monotonic voltage features, and apply to the low-rate voltage curves of a variety of active materials. Voltage histograms also reconstruct the voltage curve accurately, enabling quantification of individual IC contributions. We discuss the limitations of the method and associated mitigation strategies. Together, histogram-based implementation and voltage reconstruction offer a reproducible framework for computing IC curves comparable across battery materials, samples, and studies.

Incremental capacity (IC) analysis is a widely used non-destructive method to characterise the state of Li-ion batteries. However, there is no standard approach to compute IC curves robustly and reproducibly. Most implementations rely on numerical differentation, which amplifies measurement noise and thus requires extensive noise filtering. The smoothing method and associated parameters are selected by trial-and-error, producing inconsistent ad hoc IC curves hindering reliable comparison across studies. We promote voltage histograms as a simple alternative for computing IC curves and introduce voltage reconstruction as an objective criterion to assess their quality. Histogram-based ICs use the intuitive bin size as the only parameter, are robust to voltage noise, require little or no filtering, handle non-monotonic voltage features, and apply to the open-circuit voltage profiles of a variety of active materials. Voltage histograms also reconstruct the voltage curve accurately, enabling quantification of individual IC contributions. We discuss the limitations of the method and associated mitigation strategies. Together, histogram-based implementation and voltage reconstruction offer a reproducible framework for computing IC curves comparable across battery materials, samples, and studies.

Interoperability and cross‐validation remain significant challenges in the computational materials discovery community. In this context, we introduce a common input/output standard designed for internal translation by various workflow managers (AiiDA, PerQueue, Pipeline Pilot, and SimStack) to produce results in a unified schema. This standard aims to enable engine‐agnostic workflow execution across multiple density functional theory (DFT) codes, including CASTEP, GPAW, Quantum ESPRESSO, and VASP. As a demonstration, we have implemented a workflow to calculate the open‐circuit voltage across several battery cathode materials using the proposed universal input/output schema. We analyze and resolve the challenges of reconciling energetics computed by different DFT engines and document the code‐specific idiosyncrasies that make straightforward comparisons difficult. Motivated by these challenges, we outline general design principles for robust automated DFT workflows. This work represents a practical step toward more reproducible and interoperable workflows for high‐throughput materials screening, while highlighting challenges of aligning electronic properties, especially for nonpristine structures.

Interdisciplinary collaboration in battery science is required for rapid evaluation of better compositions and materials.

Semantic technology is revolutionizing how the battery research community collaborates. It is becoming even more important as artificial intelligence agents emerge in the field. This article explores the role of semantic resources in advancing battery research by enabling the formalization of knowledge in a way that can be understood by both people and computers. Domain‐specific ontologies provide definitive frameworks for structuring knowledge, while open‐source software packages enable the creation, validation, manipulation, and sharing of data. To link ontologies with other resources, articles, and multimedia content, a new web‐based platform called the Battery Knowledge Base, which provides a centralized hub to enhance knowledge sharing and collaboration, is introduced. In this article, how these semantic tools address critical challenges in knowledge and data management, driving progress in the field, are highlighted.

Non‐trivial relationships link individual materials properties to device‐level performance. Device optimization therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. This study demonstrates a Materials Acceleration Platform (MAP) in the field of battery research based on the problem‐agnostic Fast INtention‐Agnostic LEarning Server (FINALES) framework, which integrates simulations and physical experiments while leaving the active control of the hardware and software resources executing experiments or simulations with the partners running the respective units. This decentralization of control is a distinctive feature of MAPs using the FINALES framework. The connected capabilities entail the formulation and characterization of electrolytes, cell assembly and testing, early lifetime prediction, and ontology‐mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimize the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium‐ion coin cells by varying the electrolyte formulation. Trends in ionic conductivity are rediscovered and the effect of the electrolyte formulation on the EOL is investigated. Further, the capability of this MAP to bridge diverse research modalities, scales, and institutions enabling system‐level investigations under asynchronous conditions while handling concurrent workflows on the material‐ and system‐level is shown, demonstrating true intention‐agnosticism.

Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven accelerated electrolyte discovery efforts, we curated and analyzed a large dataset covering a wide range of experimentally recorded ionic conductivities for various combinations of lithium salts, solvents, concentrations, and temperatures. The dataset is named as ’Conductivity Atlas for Lithium salts and Solvents’ (CALiSol-23). Comprehensive datasets are lacking but are critical to building chemistry agnostic machine learning models for conductivity as well as data driven electrolyte optimization tasks. CALiSol-23 was derived from an exhaustive review of literature concerning experimental non-aqueous electrolyte conductivity measurement. The final dataset consists of 13,825 individual data points from 27 different experimental articles, in total covering 38 solvents, a broad temperature range, and 14 lithium salts. CALiSol-23 can help expedite machine learning model development that can help in understanding the complexities of ion transport and streamlining the optimization of non-aqueous electrolyte mixtures.

Non-trivial relationships link individual materials properties to device-level performance. Device optimisation therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. We demonstrate a Materials Acceleration Platform (MAP) in the field of battery research based on our problem-agnostic Fast Intentional Agnostic Learning Server (FINALES) framework, which integrates simulations and physical experiments without centrally controlling them. The connected capabilities entail the formulation and characterisation of electrolytes, cell assembly and testing, early lifetime prediction, and ontology-mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimise the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium-ion coin cells by varying the electrolyte formulation. We rediscover trends in ionic conductivity and investigate the effect of the electrolyte formulation on the EOL. We further demonstrate the capability of our MAP to bridge diverse research modalities, scales, and institutions enabling system-level investigations under asynchronous conditions while handling concurrent workflows on the material- and system-level, demonstrating true intention-agnosticism.