How Quantum Physics and Artificial Intelligence Help Us Create the Energy Materials of the Future
Jun 30, 2026
Cibrán López defended his thesis co-directed by professors Claudio Cazorla and Edgardo Saucedo on June 29, 2026 at the Diagonal-Besòs Campus. Titled “Unveiling Correlated Charge Dynamics and Recombination Pathways in Energy Materials via Quantum Simulations and Machine Learning”, the thesis combines first-principles simulations and machine learning to model, at the atomic scale, the mechanisms that govern ionic transport and electronic recombination in solid-state electrolytes and emerging photovoltaic absorbers.
In the field of solid-state electrolytes, research revealed that ionic diffusion is governed by cooperative movements of multiple ions, closely linked to the vibrations of the crystal lattice. Characteristic correlation lengths, remarkably independent of temperature, were identified, opening the door to new descriptors for the design of fast ion conductors.
Regarding pnictogen chalcogen halides, first-principles calculations combined with deep learning and device modeling allowed the identification of MChX solid solutions with tunable band gaps (1.2–2.1 eV) and high absorption coefficients, results that were experimentally validated and have direct implications for photovoltaic and photocatalytic applications. Furthermore, ab initio calculations identified chalcogen vacancies as the dominant non-radiative recombination centers in these materials, and demonstrated that targeted anion substitution and control of synthesis conditions allow the suppression of their deleterious effect.
Overall, the work establishes generalizable methodological frameworks that connect atomic-scale mechanisms with macroscopic device performance, offering transferable tools for the rational design of high-performance sustainable energy technologies.
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