Jesper Friis

Materials science is at the crossroad between fundamental and applied sciences. Whether enabling clean energy, next-generation computing, or advanced manufacturing, it shapes the tools we use and the systems we build. As our societies undergo rapid digital, environmental, and technological transitions, materials science becomes even more central. It's a space where innovation can respond to practical challenges while aligning with broader social values. In the European context, that means supporting sustainability, openness, and solidarity -while also strengthening competitiveness.This roadmap explores how digital tools-especially simulation, data science, and AI-are transforming materials research, in connection with the twin digital and energy transition. The digital transition refers to the widespread adoption of digital technologies and data-driven methods across sectors, while the green (or energy) transition focuses on shifting toward sustainable, lowcarbon energy systems-together forming what is often called the twin transition, a joint effort to make economies both smarter and more sustainable.The Roadmap outlines both the technical directions and the cultural shifts needed to make this transformation inclusive and effective. Chapters span from atomic-scale simulations to advanced experimentation, from reproducibility to intelligent optimization, and from institutional reform to education for the next generation.Importantly, this isn't a single viewpoint. The document brings together a wide range of voices: researchers from different disciplines, working across length and time scales. It includes early-career scientists and senior experts. Enabled by activities supported by European Cooperation in Science and Technology (COST), it reflects a commitment to gender and geographic diversity. This plurality doesn't just enrich the content-it makes the vision more robust and relevant.We hope this collection serves not just as a guide, but as an invitation to collaborate-across fields, sectors, and borders-as we reimagine the future of materials science in a digital age.

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to mesoscale evolution, microstructural characterisation and continuum-level property prediction. Its impact, however, differs substantially between scales because the data, descriptors, governing physics and validation requirements change from one regime to another. Nanoscale problems require models that can treat local atomic environments, defects, interfaces, long-range interactions and uncertainty in atomically resolved data. Mesoscale problems often involve evolving microstructures, phase fields, coarse-grained descriptions and expensive simulations that motivate surrogate, reduced-order and operator-learning approaches. At the micro-to-continuum scale, the emphasis shifts towards extracting reliable features from microscopy and related characterisation data, and using them for segmentation, classification and structure–property prediction. This Review examines these data-driven methods across the three scales, while distinguishing capabilities that are already reliable from limitations that still restrict broader use, including data scarcity, inconsistent metadata, weak uncertainty quantification, limited interpretability and incomplete cross-scale consistency.

Data drives battery development. Despite the unprecedented volume of data being generated across both industry and research today, the battery data landscape remains a patchwork of heterogeneous sources with inconsistent metadata needed to correctly interpret its meaning. A new approach is needed to ensure that reported battery data is interoperable, reusable, and machine-readable. Furthermore, there is a growing need to share knowledge about batteries among groups from different backgrounds and organizations (e.g., experimentalists, modellers, engineers, technicians, etc.). The Battery Interface Ontology (BattINFO) is designed to meet these needs [1]. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. This allows data to be mapped to common vocabulary terms, such that any two pieces of data mapped to the same term are also mapped to each other. Additionally, the network of concepts and relations allow expert knowledge to be expressed as graphs in a machine-readable form, which can then be queried to identify links between individual pieces of data and quickly share knowledge across organizations. BattINFO is a free, open-source domain ontology for electrochemistry and batteries that facilitates battery data interoperability and enables machine-readable semantic knowledge representation. A conceptual overview is shown in Figure 1. BattINFO was developed within the scope of the EU H2020 project BIG-MAP to facilitate data interoperability between more than 30 battery research institutes and companies in Europe. This contribution introduces BattINFO, demonstrates specific use cases for how it can be applied to accelerate battery data handling and development, and presents plans for future development and integration into the global battery data landscape. [1] S. Clark et al., “Toward a Unified Description of Battery Data,” Adv. Energy Mater., vol. 2102702, 2021. Figure 1

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.

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.