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FullProfAPP is a software tool for data processing, refinement and visualization of large collections of powder diffraction patterns. Featuring an intuitive graphical user interface, it seamlessly facilitates a variety of tasks. These include conducting full‐profile phase searches, sequential and high‐throughput Rietveld refinements, and managing background (and peak) detection. FullProfAPP also provides convenient interaction with crystallographic databases and supports the visualization and export of high‐quality pixel and vector graphics depicting the refinement results, among other functionalities. FullProfAPP wraps around the refinement program FullProf [Rodríguez‐Carvajal (1993), Physica B, 192, 55–69] and offers the flexibility of user‐defined workflows by accessing and editing FullProf's input files and triggering its execution as necessary. FullProfAPP is distributed as open‐source software and is presently available for Windows and Linux operating systems. [ABSTRACT FROM AUTHOR]

Battery research initiatives and giga‐scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high‐performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery‐related activities across different fields, accelerate the flow of knowledge in both human‐ and machine‐readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need. [ABSTRACT FROM AUTHOR]

BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high‐performance batteries. Here, a description is given of how the AI‐assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low‐technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI‐assisted toolkit developed within BIG‐MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time‐ and length‐scale simulations of multi‐species systems, dynamic processes at battery interfaces, deep learned multi‐scaling and explainable AI, as well as AI‐assisted materials characterization, self‐driving labs, closed‐loop optimization, and AI for advanced sensing and self‐healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low‐TRL battery chemistries and concepts covering multivalent anodes, metal‐sulfur/oxygen systems, non‐crystalline, nano‐structured and disordered systems, organic battery materials, and bulk vs. interface‐limited batteries. [ABSTRACT FROM AUTHOR]

Review: 214 refs. [ABSTRACT FROM AUTHOR]