One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity**

Non-aqueous aprotic battery electrolytes need to perform well over a wide range of temperatures in practical applications. Herein we present a one-shot active learning study to find all conductivity optima, confidence bounds, and relating formulation trends in the temperature range from −30 °C to 60 °C. This optimization is enabled by a high-throughput formulation and characterization setup guided by one-shot active learning utilizing robust and heavily regularized polynomial regression. Whilst there is an initially good agreement for intermediate and low temperatures, there is a need for the active learning step to improve the model for high temperatures. Optimized electrolyte formulations likely correspond to the highest physically possible conductivities within this formulation system when compared to literature data. A thorough error propagation analysis yields a fidelity assessment of conductivity measurements and electrolyte formulation

Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein

Data Management Plans: the Importance of Data Management in the BIG‐MAP Project[]**

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU−H2020 BIG-MAP project—a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan

Auto-MISCHBARES

Auto-MISCHBARES, building upon our HELAO framework, is designed for high-throughput electrochemical research. It automates the study different electrolyte and/or electrode materials, different electrochemcial protocols in order to characterize the interphase formations at a millimeter scale, enhancing the efficiency of material discovery. This system's significant feature is its ability to autonoumussly asynchronously orchestrate sequential or parallel experiments, integrated with advanced Quality Control assessments and MADAP for advanced data analysis using AI algorithms. The web interface of Auto-MISCHBARES offers streamlined user control, and its database design adheres to FAIR principles, promoting robust and transparent research in battery material science.If you use this software, please cite it using the metadata from this file

Attention towards chemistry agnostic and explainable battery lifetime prediction

<p>Here are the pre-trained model weights for a pre-print publication named "Attention towards chemistry agnostic and explainable battery lifetime prediction".</p><p>This zip file contains all the pre-trained model weights from the ARCANA framework, including:</p><ul><li><strong>public_multihead </strong>- the original model trained on Li-ion batteries for cell types.</li><li><strong>finetune_public_multihead_on_coin </strong>- the model fine-tuned on Li-ion coin cells data.</li><li><strong>finetune_public_multihead_on_sodium </strong>- the model fine-tuned on Na-ion coin cells.</li></ul><p>Further details regarding model architecture can be found here: <a href="https://github.com/basf/ARCANA">https://github.com/basf/ARCANA </a></p><p> </p&gt

Auto-MISCHBARES: Tutorial & Demonstration

<p>Tutorial and demonstration of Auto-MISCHBARES.</p> <p> </p> <p>Additional links:</p> <p>MISCHBARES: <a title="MISCHBARES" href="https://github.com/fuzhanrahmanian/MISCHBARES">https://github.com/fuzhanrahmanian/MISCHBARES</a></p> <p>MADAP:  <a title="MADAP" href="https://github.com/fuzhanrahmanian/MADAP">https://github.com/fuzhanrahmanian/MADAP</a></p> <p>HELAO: <a title="HELAO" href="https://github.com/helgestein/helao-pub">https://github.com/helgestein/helao-pub</a></p> <p> </p&gt

Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries

Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive\u27s amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes’ area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript