1 citations · 1 across the 4 of their papers we have counts for
4 papers
Thermodynamics-Informed Machine Learning for Energy Materials Discovery
Pol Benítez, Cibrán López, Claudio Cazorla
Machine learning (ML) is transforming materials discovery by enabling rapid prediction of properties that previously required computationally expensive first-principles calculation…
Band-Gap Tunability in Anharmonic Perovskite-like Semiconductors Driven by Polar Electron-Phonon Coupling
Pol Benítez, Ruoshi Jiang, Siyu Chen +5
The ability to finely tune optoelectronic properties in semiconductors is crucial for the development of advanced technologies, ranging from photodetectors to photovoltaics. In thi…
Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Cibrán López, Joshua Ojih, Ming Hu +3
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical…
Chalcogen Vacancies Rule Charge Recombination in Pnictogen Chalcohalide Solar-Cell Absorbers
Cibrán López, Seán R. Kavanagh, Pol Benítez +4
Pnictogen chalcohalides (MChX, M = Bi, Sb; Ch = S, Se; X = I, Br) represent an emerging class of nontoxic photovoltaic absorbers, valued for their favorable synthesis conditions an…