From the 1 of 12 linked papers with an AI index.
12 papers
Thermodynamics-Informed Machine Learning for Energy Materials Discovery
Pol BenÃtez, Cibrán López, Claudio Cazorla
The paper discusses the need for machine learning models that incorporate thermodynamic effects, such as entropy and anharmonicity, to predict free‑energy landscapes of energy mate…
Carrier Localization in Pnictogen-Based Chalcohalides from Defect-Bound Hot Polarons
Xiaoyu Guo, Junzhi Ye, Cibrán Lopez Alvarez +19
Pnictogen-based solar absorbers have gained prominence as promising nontoxic and stable alternatives to lead-halide perovskites (LHPs), but are severely limited by carrier localiza…
Machine Learning Modeling of Temperature-Dependent Optoelectronic Properties of Anharmonic Solid Solutions
Pol BenÃtez, Cibrán López, Edgardo Saucedo +1
Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including…
Defect-Limited Efficiency of Pnictogen Chalcohalide Solar Cells
Cibrán López, Seán R. Kavanagh, Pol BenÃtez +4
Pnictogen chalcohalides (MChX) have recently emerged as promising nontoxic and environmentally friendly photovoltaic absorbers, combining strong light absorption coefficients with…
Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
Pol BenÃtez, Cibrán López, Edgardo Saucedo +2
Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the…
Molecular ink-based synthesis of Bi(SzSe1-z)(IxBr1-x) solid solutions as tuneable materials for sustainable energy applications
David Rovira, Ivan Caño, Cibran Lopez +16
Quasi-one-dimensional (Q-1D) van der Waals chalcohalides have emerged as promising materials for advanced energy applications, combining tunable optoelectronic properties and compo…