AI systems for autonomous discovery in materials and condensed matter physics
AI for Materials Lab @ UAM · Led by Jorge Bravo Abad, Professor of Physics at Universidad Autónoma de Madrid.
Machine learning in science has largely focused on prediction. We work on the next step: systems that close the loop between models, simulations and experiments, choosing what to compute or measure next and improving with every result.
Our research follows three lines:
Autonomous discovery under limited budgets: decision-making methods for cost-aware discovery campaigns, implemented in CADAQUES, our open-source framework.
Generative and inverse design of materials: for example, defect engineering in 2D materials.
Machine learning for quantum many-body systems: neural-network quantum states for strongly correlated and frustrated models.
We collaborate with a number of computational and experimental groups and coordinate the Spanish network on AI for condensed matter and materials science.
Core activities
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We develop closed-loop AI systems that choose what to simulate, design, or measure next, learn from each result, and navigate scientific search spaces efficiently.
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We apply machine learning to the discovery and design of materials and to the study of complex quantum systems, combining data-driven methods with physical knowledge.
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We teach, communicate, and build communities around AI-enabled science through courses, books, talks, workshops, and collaborative initiatives.
