Automated Scientific Discovery - Materials Science (RAISE pilot) (RIA)

Expected Outcome:

• Development of closed-loop scientific experimentation systems that integrate automation with AI-driven, trustworthy decision-making processes in existing laboratory environments;
• Accelerated scientific discovery with increased efficiency and reproducibility;
• Improved scientific productivity;
• Advancement of laboratory automation, including development of best practices, challenges, and opportunities for accelerating R&D; and
• Prototype functional demonstrators that showcase the integration of automation with AI-driven decision-making, enabling the development of closed-loop scientific experimentation systems.

Scope:

This topic addresses the development of safe and trustworthy closed loop scientific experimentation systems through the integration of laboratory automation with AI-driven decision-making processes and robust data infrastructures. Funded projects will help scientific labs with an already advanced level of automation and digitalisation to design, develop, and test the intelligence layer that enables scientific instrumentation to semi- or fully autonomously plan, run, and analyse experiments, ideally in coordination/network with other labs and without requiring a complete redesign of existing laboratory outfitting. Proposals will incorporate comprehensive data management systems capable of handling the collection, storage, processing, and sharing of experimental data. This includes developing scalable and secure data storage solutions, efficient data processing and analysis tools, and mechanisms to facilitate data sharing and collaboration across labs, while ensuring data security and privacy. Systems could incorporate AI-driven resource optimisation modules, actively minimising energy, reagent, and material consumption during automated experimentation cycles. Systems should incorporate appropriate level of security and robustness by design. Proposals should demonstrate how an existing lab can be retrofitted with AI-driven systems to plan, execute, and analyse experiments in a closed-loop fashion, incorporating human oversight and interaction to ensure accuracy, safety, and ethical compliance. Possible research targets include (non-exhaustively):

• Autonomous/semi-autonomous and adaptive AI systems (including agentic AI) that connect with laboratory instruments and robotics and can autonomously plan, act, learn and adapt within a scientific environment, within a validated safe pipeline,
• Assistive and interactive safe AI-managed robotic systems that automate diverse experiments and can be applied to a diverse hardware setup.
• Scalable automation solutions and networked AI systems that enable collaborative experimentation across multiple labs and networks of labs (including different geographic locations), supporting the simultaneous execution of large volumes of experiments,
• Systems that provide real-time data processing and analytics, enabling immediate feedback and dynamic adjustments during experiments
• Standards and protocols to ensure interoperability between different laboratory instruments, robotics, and AI systems
• Intuitive user interfaces for enhanced human-machine interaction
• AI-driven predictive maintenance systems to optimize equipment uptime and resource utilization.
• Exploration of how scientific automation technologies can be adapted for use in various scientific disciplines beyond those in the scope of this call

Proposals should demonstrate close interdisciplinary collaboration of computer/AI scientists and domain scientists. While the scope of this call prioritises software development, it does not exclude the justified purchase of complementary equipment necessary to implement the research targets of the project. The focus of this topic is on any aspect of materials science. Impact areas of automated experimentation in this field could include, non-exhaustively, battery technologies, photovoltaics, carbon capture/storage, water purification, soil remediation, drug discovery or sustainable fabrics. International collaboration is encouraged. Proposals are expected to develop synergies with running Horizon Europe projects in the same field, for example with HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61.

Submissions open on 13 October 2026.

Part of call HORIZON-RAISE-2027-01

Type of action: HORIZON Research and Innovation Actions

Programme period: 2021 - 2027

Priorities: DigitalAgenda, AI

"> General conditions

1. Admissibility Conditions: Proposal page limit and layout

described in Annex A and Annex E of the Horizon Europe Work Programme General Annexes.

Proposal page limits and layout: described in Part B of the Application Form available in the Submission System.

2. Eligible Countries

described in Annex B of the Work Programme General Annexes. A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon Europe projects. See the information in the Horizon Europe Programme Guide .

3. Other Eligible Conditions

described in Annex B of the Work Programme General Annexes.

4. Financial and operational capacity and exclusion

described in Annex C of the Work Programme General Annexes.

5a. Evaluation and award: Award criteria, scoring and thresholds

are described in Annex D of the Work Programme General Annexes.

5b. Evaluation and award: Submission and evaluation processes

are described in Annex F of the Work Programme General Annexes and the Online Manual .

5c. Evaluation and award: Indicative timeline for evaluation and grant agreement

described in Annex F of the Work Programme General Annexes.

6. Legal and financial set-up of the grants

described in Annex G of the Work Programme General Annexes.

Specific conditions described in the [specific topic of the Work Programme]

Application and evaluation forms and mo