From the 1 of 3 linked papers with an AI index.
3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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…