Many indications suggest that research and industrial R&D are entering a year in which several developments will reinforce one another. On the one hand, pressure is increasing to develop materials faster, more sustainably, and with greater precision. On the other, new scientific and technological capabilities are emerging—from AI-supported analytical methods to biologically active materials.
In this dynamic environment, five trends are particularly visible. They influence different stages of the development process and broaden the scope of what is becoming possible in materials science. These include new material concepts, data-centric methodologies, and advanced characterization techniques that collectively define the direction of material development in 2026.
What becomes clear is that materials research in 2026 will be more data-driven, more interdisciplinary, and more methodologically diverse than in previous years. AI and digital research environments are transforming the way materials are developed, characterized, and evaluated. At the same time, biological and multifunctional material systems are creating entirely new design possibilities.
This trend report highlights the technological developments that will be particularly influential—and the role they already play for R&D teams today.
Trend 1: Bio-Based and Living Materials
A central research focus for 2026 is bio-based and living materials. They differ fundamentally from conventional metallic or polymeric materials because they integrate biological components and can therefore take on active functions. Fraunhofer INT describes them as follows:
“Living materials open up a multitude of new application possibilities, such as self-healing construction materials, intelligent packaging materials, artificial leaves for sustainable energy production, or personalised drug-delivery systems.” [1]
These materials are characterized by their ability to respond actively to environmental stimuli, partially regenerate themselves, or independently carry out chemical processes. They impart to non-living matter properties previously reserved for biological systems—such as growth, self-repair, or adaptive behaviour.
Scientific approaches range from bio-inspired polymers to hybrid materials that deliberately incorporate bacteria, yeasts, or microalgae. A widely researched example is living building materials based on cyanobacteria [2,3]. Here, photosynthetically active bacteria are applied to a scaffold of sand and gelatin. The gelatin provides moisture and nutrients, while the bacteria absorb CO₂ and produce calcium carbonate. This triggers a mineralization process that solidifies the material.
A remarkable feature is the system’s ability to grow: if such a stone is split and supplied again with nutrients, both halves can continue to grow and harden through ongoing biomineralization. This can even be repeated over several cycles. The material forms while consuming CO₂—whereas conventional cement production generates substantial emissions.
Other research also highlights the potential of this material class. In symbiotic cultures, for example, bacteria produce cellulose scaffolds into which genetically modified yeasts are integrated. These yeasts can provide enzymes that detect or degrade pollutants or enable packaging materials that decompose themselves after use. Another approach uses bacterial cellulose as a carrier for microalgae to create artificial leaves capable of generating energy via photosynthesis and potentially serving as fully biodegradable textiles.
For R&D teams, this creates an entirely new design space: material design increasingly incorporates biological mechanisms and their dynamics. The combination of technical structure and cellular activity enables properties difficult or impossible to achieve with conventional materials—such as self-healing surfaces, adaptive responses, or controlled release processes. At the same time, safety considerations remain crucial, especially when working with genetically modified organisms.
Trend 2: Agentic AI
“Agentic AI” refers to a new class of AI systems capable not only of analysing or visualising data but also of autonomously performing steps within longer workflows [4]. These systems can understand the goals of a research project, integrate information from various sources, and derive actions from it.
In contrast to classical machine learning—based on trained models and strongly dependent on existing datasets—agentic AI can operate without task-specific training and is guided primarily by objectives and process logic.
Decision-making authority remains entirely with humans. Instead of replacing expertise, agentic systems act as an active part of the workflow and support specialists where tasks are time-consuming, repetitive, or information-intensive.
For research teams, this means that agentic AI can structure literature searches, prepare hypotheses, consolidate data, or take over documentation steps. A frequently cited example is experimental planning: the AI scans existing protocols, summarizes relevant parameters, and proposes potential experimental variants based on historical data.
At LabV, this approach is described as a co-developer. It is not an autonomous system but an assistive tool that alleviates typical bottlenecks in material development—such as merging test data, drafting initial report versions, or finding relevant information from previous projects. Agentic AI makes existing knowledge usable at a speed that is hardly achievable manually.
For R&D teams, this creates a practical tool that provides orientation and significantly reduces workload. Professional responsibility remains fully with researchers, while the AI automates routines and supplies information in a targeted way. Agentic AI thus becomes a realistic, immediately deployable component of modern material development.
Trend 3: Data-Centric Material Development
Digital transformation is fundamentally reshaping materials science [5]. The European Materials Modelling Council (EMMC), a network of research organisations, industry partners, and political stakeholders, describes this shift in its roadmap On the Digital Transformation of Materials Science as a transition toward research environments built on continuous data infrastructures, FAIR principles, and integrated workflows linking experiment, simulation, and AI [6].
Data is becoming a foundational element of R&D. Laboratory processes are moving away from isolated steps toward integrated, interoperable, and increasingly automated workflows.
Machine learning, material-intelligence approaches, and materials-acceleration platforms rely on integrated data structures that systematically connect experiments, simulations, and other data sources. Material Intelligence refers to the ability to make heterogeneous material data usable and derive actionable insights for R&D. For industry, this represents a paradigm shift toward data-oriented processes that will significantly shorten development cycles.
Trend 4: Data-Intensive and In-Situ Based Material Characterization
Material characterization is evolving from static individual measurements to dynamic, data-rich analytical processes, as evidenced by ML-based high-throughput and in-situ methods [6,7]. Modern laboratories increasingly employ techniques that capture material behaviour in three dimensions and under real operating conditions. These include in-situ analyses during mechanical load, thermal cycling, or chemical reactions, as well as high-resolution and high-frequency imaging.
The resulting data volumes are complex and require advanced evaluation methods, including machine learning. This creates substantial added value for R&D teams, as spatially resolved material changes, such as local crack formation, stresses, or micro-reactions—and behaviour at phase and interface boundaries can be recorded with far greater precision.
The industry is clearly moving toward continuous, data-rich characterization that is expected to enable faster development cycles and more robust material decisions.
Trend 5: Multifunctional Materials
Modern applications increasingly require materials capable of fulfilling multiple functions simultaneously. As a result, research focuses on material systems that deliberately combine various physical, chemical, or structural properties—for example, through laser-structured layers, nanostructured surfaces, or novel coating systems for corrosion protection, friction reduction, or energy conversion [8].
A particularly illustrative example are metal–organic frameworks (MOFs), awarded the 2025 Nobel Prize in Chemistry. Researchers at Otto von Guericke University Magdeburg have been working on this highly porous material class for years. Due to their enormous internal surface area and structural variability, MOFs are often described as “molecular sponges.”
“MOFs can store heat, cold, or gases, control chemical reactions, and filter CO₂ from exhaust streams. This makes them a key material for the energy transition,” says Prof. Franziska Scheffler in a press release by the University of Magdeburg [9]. Their multifunctionality exemplifies how material design is evolving toward integrated functional platforms.
For research teams, this means a shift in perspective: the performance of a material increasingly results from the interplay of different mechanisms and functions. This approach opens new possibilities for designing materials that meet multiple requirements simultaneously.
Conclusion
Material development in 2026 will be shaped by five key trends that will fundamentally influence research and industrial R&D. Bio-based and living materials expand the range of functional materials, while agentic AI supports research teams in data-intensive tasks and accelerates decision-making.
Data-centric methods form the foundation of this transformation. They enable connected workflows, reproducible results, and well-grounded analyses. At the same time, modern in-situ and high-throughput methods provide a far more precise understanding of material behaviour under real operating conditions.
Multifunctional materials—including MOFs, which received the 2025 Nobel Prize—demonstrate how performance, sustainability, and system integration must be considered together.
For R&D teams, the implications are clear: material development is becoming more data-oriented, AI-supported, and interdisciplinary. Those who adopt these trends early will enhance innovation speed, quality, and competitiveness.
References
[1] Fraunhofer INT – Living Materials: Neue Anwendungsmöglichkeiten biologisch aktiver Werkstoffe
https://www.int.fraunhofer.de/de/geschaeftsfelder/corporate-technology-foresight/trend-news/living-materials.html
[2] Nguyen, P.Q. et al. – Engineered Living Materials: Prospects and Challenges.
ScienceDirect (2019).
https://www.sciencedirect.com/science/article/pii/S2590238519303911
[3] Scientific American – Bacteria-Filled Bricks Build Themselves
https://www.scientificamerican.com/article/bacteria-filled-bricks-build-themselves/
[4] Wikipedia – Agentic AI
https://en.wikipedia.org/wiki/Agentic_AI
[5] Springer Professional – „Wir stehen erst am Anfang der Entdeckung neuer Materialien“
https://www.springerprofessional.de/materialentwicklung/metalle/-wir-stehen-erst-am-anfang-der-entdeckung-neuer-materialien-/24631018
[6] European Materials Modelling Council (EMMC) – On the Digital Transformation of Materials Science (Roadmap)
https://emmc.eu/wp-content/uploads/digital-transformation-of-materials-science.pdf
[7] Wiley – Foundations of Data-Driven Materials Characterization
https://onlinelibrary.wiley.com/doi/full/10.1002/gamm.202100003
[8] Nature – Advances in In Situ Characterization
https://www.nature.com/articles/s43246-024-00487-3
[9] Otto-von-Guericke-Universität Magdeburg – Pressemitteilung zu metall-organischen Gerüstverbindungen (MOFs)
https://www.ovgu.de/-p-149270.html
FAQ
1. Which five trends will shape material development in 2026?
The key trends are: bio-based and living materials, agentic AI, data-centric research processes, data-intensive and in-situ characterization, and multifunctional materials such as MOFs. Together, they expand the possibilities of material development—from novel material concepts to data-based analytical methods and more precise evaluation techniques.
2. What role will AI play in materials science in 2026?
AI—especially agentic AI—supports research teams in data-intensive tasks such as analysis, documentation, and experiment evaluation. It does not replace expertise but accelerates workflows and improves the use of existing material data.
3. Why are data-centric methods becoming more important for R&D?
Data-centric approaches enable connected workflows, reproducible results, and stronger decision-making. Through FAIR principles, ontologies, and integrated data infrastructures, experiments, simulations, and AI-based evaluations can be efficiently combined.
4. What does in-situ or data-intensive material characterization mean?
In-situ methods measure material behaviour under real conditions and provide a much more precise understanding of local and transient effects. The large data volumes require AI-supported analysis methods to extract detailed information on spatial and time-dependent material changes. These additional insights lead to more reliable models and stronger predictive power.
5. Why are multifunctional materials considered particularly relevant for the future?
Modern applications require materials that fulfil several functions at once. Multifunctional materials such as MOFs combine high performance with sustainability and system integration, making them key materials for sectors such as energy, mobility, sensing, and environmental technology.
Author: Dr Marc Egelhofer