Center for Research in Multiscale Science and Engineering

PhD Thesis Defense - Cibrán López Álvarez

Unveiling correlated charge dynamics and recombination pathways in energy materials via quantum simulations and machine learning

When

Jun 29, 2026 from 10:00 AM to 01:00 PM (Europe/Madrid / UTC200)

Where

Sala Polivalent - Edifici A - Campus Diagonal-Besòs (UPC)

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As part of the Doctoral Programme in Computational and Applied Physics, Cibrán López Álvarez will be defending his PhD thesis on Monday, 29th June 2026, at 10:00 AM, at the Escola d’Enginyeria de Barcelona Est (EEBE)

  • Name of the author: Cibrán López Álvarez

  • Thesis title: Unveiling correlated charge dynamics and recombination pathways in energy materials via quantum simulations and machine learning

  • Thesis director: Prof. Claudio Cazorla Silva

  • Thesis co-director: Prof. Edgardo Ademar Saucedo Silva

This PhD thesis focuses on understanding how atomic-scale mechanisms govern ionic and electronic transport, which is essential for designing next-generation energy materials. By combining first-principles simulations with machine learning techniques, this research provides predictive frameworks to model and understand solid-state electrolytes and photovoltaic materials at an atomistic level.

The study centers on two main frontiers in energy technology:

  • Solid-State Electrolytes: Using first-principles calculations and unsupervised learning, the research reveals that ionic diffusion is driven by the correlated movement of multiple ions. This cooperative behavior is strongly linked to lattice vibrations and elastic properties, offering new descriptors for designing fast ionic conductors.

  • Photovoltaic Materials (MChX pnictogen chalcohalides): Combining deep learning with device-level modeling, the study identifies and experimentally validates material solutions with tunable bandgaps and high absorption coefficients. It also addresses efficiency bottlenecks, demonstrating how targeted anionic substitution can suppress detrimental recombination centers to optimize solar cell performance.

Ultimately, this work establishes generalizable frameworks that bridge atomistic mechanisms with macroscopic device performance, providing a clear roadmap for the rational design of sustainable, high-performance energy technologies.