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.

Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

Lithium‐ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time‐consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real‐time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set‐up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state‐of‐the‐art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.

In a comprehensive study, we demonstrate the performance and typical application scenarios for laboratory-based nano-computed tomography in materials research on various samples. Specifically, we focus on a projection magnification system with a nano focus source. The imaging resolution is quantified with common 2D test structures and validated in 3D applications by means of the Fourier Shell Correlation. As representative application examples from nowadays material research, we show metallization processes in multilayer integrated circuits, aging in lithium battery electrodes, and volumetric of metallic sub-micrometer fillers of composites. Thus, the laboratory system provides the unique possibility to image non-destructively structures in the range of 170–190 nanometers, even for high-density materials.

One of the key challenges of data management for smart manufacturing is dealing with data originating from both physical processes and virtual digital technologies. As image and sensor‐based production monitoring deliver a wealth of data along the process chain, artificial intelligence (AI) enables enhanced data analysis and new insight regarding relevance of observed process deviations. With constantly increased availability of data from manifold and specific sources, the complexity and heterogeneity of information structures are also growing rapidly. This is especially true for highly variable research, scale‐up, and pilot production, which poses new demands on data acquisition, data management, and data preprocessing. Herein, a unified framework for integrating an ontology and graph‐based data space with data acquisition and data analytics to improve data consistency, documentation of workflows, as well as the reproducibility of observations and results is presented. The framework consists of several open‐source web services that form an ontology‐based data space where physical and virtual process chains are represented by a semantic data fabric built from findable, accessible, interoperable, and reusable resource descriptions framework self‐descriptions. The feasibility of the proposed framework is demonstrated for a laboratory‐scale Li‐ion battery cell production facility with AI applied to two data analytics use cases.

The development of new glasses is often hampered by inefficient trial‐and‐error approaches. The traditional glass manufacturing process is not only time‐consuming, but also difficult to reproduce with inevitable variations in process parameters. These challenges are addressed by implementing an ontology‐based digital infrastructure coupled with a robotic melting system. This system facilitates high‐throughput glass synthesis and ensures the collection of consistent process data. In addition, the digital infrastructure includes machine learning models for predicting glass properties and a tool for extracting patent information. Current glass databases have significant gaps in the relationships between compositions, process parameters, and properties due to inconsistent studies and nonconforming units. In addition, process parameters are often omitted, and even original literature references provide limited information. By continuously expanding the database with consistent, high‐quality data, it is aimed to fill these gaps and accelerate the glass development process.