1. The development of new materials remains one of the major technological bottlenecks. Why is it so complex to discover, synthesise and validate new solutions?

Discovering a new material is not just about identifying a promising chemical composition. It is necessary to understand how it behaves, how it is synthesised, what properties it presents, whether it is stable, reproducible and compatible with a specific application. In the case of energy storage, moreover, small variations in structure, morphology or processing can have a very significant impact on final performance.

The complexity increases because each new material must go through many stages before it can be considered viable: design, synthesis, characterisation, data analysis, experimental validation and, subsequently, scale-up. All this requires time, resources and a strong capacity to make decisions among many possible variables. That is why we need new tools that allow us to accelerate the process without losing scientific rigour.

2. What changes when we incorporate automation and artificial intelligence into the traditional work of a materials laboratory?

What changes is that the laboratory no longer functions only as a sequence of manual experiments, but becomes a much more connected, automated system capable of learning from its own results. Automation makes it possible to carry out repetitive tasks more quickly and reproducibly, while artificial intelligence helps analyse the data generated and guide the next steps in the research.

This does not mean replacing scientific judgement, but expanding it. Researchers can explore a much broader space of possibilities, compare more variables and make better-informed decisions about which compositions, synthesis conditions or experimental routes have the greatest potential. In this way, the work becomes less linear and more iterative: the data from each experiment feed new hypotheses and allow the process to be optimised more rapidly.

The result is more efficient, traceable and impact-oriented research. By combining experimentation, automation and advanced data analysis, it is possible to reduce times, minimise errors, improve reproducibility and accelerate the validation of materials capable of addressing real technological challenges.

3. In the case of materials for energy storage, which phases of the research process can be accelerated the most through this type of platform?

This type of platform can especially accelerate the initial phases of identifying and selecting promising materials. Artificial intelligence makes it possible to explore large compositional spaces and prioritise the chemical families or combinations of elements with the greatest potential before moving on to experimentation.

It can also accelerate synthesis and characterisation, especially when automation and robotics tools are integrated. This makes it possible to prepare samples, run tests and obtain data more quickly, systematically and reproducibly than in a completely manual workflow.

Finally, automated data analysis is key to closing the loop. Based on the results obtained, the platform can help interpret trends, compare experiments and propose new working conditions, reducing the time needed to move from an initial hypothesis to experimental validation.

4. There is a lot of discussion about artificial intelligence applied to science, but what does it really mean to use AI to design experiments or guide research into new materials?

Using artificial intelligence to design experiments means using algorithms capable of learning from the available data and suggesting the next most promising tests. Instead of progressing solely through intuition or trial and error, the system can analyse previous results, identify patterns and help decide which composition, temperature, synthesis time or experimental condition should be explored next.

In practice, this makes it possible to guide research in a much more efficient way. AI does not replace the researcher’s knowledge, but it does help them navigate a space of possibilities that would be impossible to explore manually within a reasonable timeframe. Each experiment generates new data, those data feed the model, and the model proposes new working routes, creating a learning cycle that accelerates the discovery and validation of new materials.

5. How can these tools help reduce time, costs and resource consumption in the development of new chemistries for batteries and other energy applications?

These tools make it possible to optimise the research process from the earliest stages, reducing the number of experiments needed to reach a promising solution. By combining automation, experimental design and artificial intelligence, it is possible to prioritise the routes with the greatest potential, discard the least viable ones earlier, and use materials, equipment and laboratory time more efficiently.

In addition, automation improves reproducibility and reduces errors associated with manual processes, avoiding unnecessary repetitions and accelerating decision-making. In the development of new chemistries for batteries and other energy applications, this can translate into shorter innovation cycles, lower resource consumption and a faster transition from the initial idea to solid experimental validation.

6. Beyond accelerating discovery, what role does automation play in the reproducibility and reliability of scientific results?

Automation enables experiments to be carried out under much more controlled and consistent conditions. This reduces the variability associated with manual intervention and makes it easier to compare results between different samples, batches or experimental campaigns.

In materials science, small differences in preparation, temperature, timing or handling can significantly alter the final result. That is why repeating processes precisely is essential to determine whether a material truly works or whether the result depends on a one-off condition.

In addition, by systematically recording the parameters and data from each experiment, automation improves traceability. This makes it possible to better understand what has happened at each stage and makes it easier to reproduce, verify or adjust the results when necessary.

Ultimately, automation not only enables faster work, but also generates more reliable data. And that reliability is key for research to move towards later stages of validation, scale-up and industrial transfer.

7. How important are advanced characterisation and automated data analysis for making better decisions during material development?

Advanced characterisation is essential because it allows us to understand what we are actually obtaining in each experiment. It is not enough to synthesise a material and measure its final performance; we need to know its structure, composition, morphology, stability and electrochemical behaviour in order to identify which variables are influencing its properties and why one result is better than another.

Automated data analysis makes it possible to transform all this information into useful knowledge for decision-making. By integrating data from different techniques and analysing them systematically, we can detect patterns, correlations and trends that would be difficult to identify manually. This helps prioritise the most promising routes, correct deviations and move forward more quickly and precisely in the development of new materials.

8. Although these platforms are closely linked to the battery field, in what other sectors or applications could they have an impact?

Although these platforms are closely linked to the development of materials for batteries, their potential is much broader. They can be applied to the study of virtually any inorganic material where it is necessary to explore compositions, optimise synthesis processes and validate functional properties quickly and reproducibly.

This opens up opportunities in sectors such as advanced electronics, catalysts, materials for hydrogen, sensors, the chemical industry and technologies linked to energy conversion and storage. In all these areas, there is a common need: to accelerate the development of more efficient, stable and sustainable materials adapted to specific applications.

The key point is that the methodology is transferable. If we are able to combine automation, artificial intelligence, advanced characterisation and data analysis, we can reduce development times in many technological fields and help new solutions reach industry sooner.

9. Europe wants to strengthen its technological autonomy. Why is it important to develop its own capabilities in automation, AI and accelerated materials discovery?

Developing its own capabilities in automation, artificial intelligence and accelerated materials discovery is essential for Europe to compete in an increasingly demanding technological context. The speed at which new solutions are discovered, validated and scaled up will be a decisive factor in strategic sectors such as energy, batteries, advanced electronics and the chemical industry.

In addition, these capabilities make it possible to reduce external dependencies and reinforce European technological autonomy. It is not only a matter of accessing new digital tools, but of building Europe’s own scientific and industrial infrastructure capable of generating knowledge, protecting it and transforming it into technologies with economic and social impact.

In this sense, automation and artificial intelligence platforms can act as a bridge between materials science and industrialisation. By accelerating decision-making, improving reproducibility and optimising resources, Europe can move faster in the development of critical materials for the energy transition and strengthen its position in strategic value chains.

10. How does MAITENA help connect materials science, digitalisation and industrialisation, and what role can CIC energiGUNE play in this new stage of research?

MAITENA helps connect materials science, digitalisation and industrialisation because it brings together, on a single platform, capabilities that have traditionally operated more separately: synthesis, characterisation, data analysis and experimental planning. This allows the development of new materials to progress in a more coordinated, traceable and results-oriented way.

Digitalisation provides a key layer, as it turns each experiment into a useful source of data for learning and making better decisions. By combining automation and artificial intelligence, MAITENA can help identify promising routes, optimise working conditions and accelerate experimental validation, reducing the gap between scientific discovery and its potential industrial application.

In this new stage, CIC energiGUNE can play a highly relevant role as a bridge between advanced research and real technological needs. The centre has expertise in energy storage, materials and experimental validation, and platforms such as MAITENA strengthen its capacity to drive research that is faster, more efficient and more transferable to industry.