arXiv subjects

Ali Barooni

Publications and source records attributed to Ali Barooni.

2 recordsLinked to original sources

arXiv2026

Understanding insulating ferromagnetism in LaCoO3 films under tensile strain

LaCoO3 thin films grown under epitaxial tensile strain exhibit a robust ferromagnetic insulating state that is absent in the bulk. Despite many studies, both experimental and computational, the microscopic origin of this phenomenon is not well understood. In this work, density functional theory calculations are used to systematically investigate the magnetic ground state of stoichiometric LaCoO3 under epitaxial strain equivalent to that imposed by a SrTiO3 substrate. The results identify a ferromagnetic insulating ground state characterized by a unique ordered array of high-spin (HS) and low-spin (LS) Co3+ ions. The spin state ordering is best described as 2 x 2 columns that consist of alternating HS and LS Co3+ ions, separated by planes of LS Co3+ ions. This leads to HS-LS-LS repeating sequence of Co3+ ions in both pseudocubic [100] and [010] directions. Analysis of the electronic structure confirms the presence of an insulating gap. Evaluation of the superexchange interactions reveal ferromagnetic interactions between HS Co3+ ions via 90 degree paths, and antiferromagnetic interactions via 180 degree paths, both of which are facilitated by empty sigma* (eg) orbitals on the diamagnetic LS Co3+ ions. The strength and number of 90 degree ferromagnetic interactions are sufficient to overcome the competing 180 degree antiferromagnetic interactions stabilizing a ferromagnetic insulating state.

cond-mat.mtrl-sci↗

arXiv2024

Frost Prediction Using Machine Learning Methods in Fars Province

One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to the country, especially Fars province. Solving this problem requires that, in addition to predicting the minimum temperature, we can provide enough time to implement the necessary measures. Empirical methods have been provided by the Food and Agriculture Organization (FAO), which can predict the minimum temperature, but not in time. In addition to this, we can use machine learning methods to model the minimum temperature. In this study, we have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A customized loss function designed for methods based on deep learning, which can be effective in reducing prediction errors. With methods based on deep learning models, not only do we observe a reduction in RMSE error compared to empirical methods but also have more time to predict minimum temperature. Thus, we can model the minimum temperature for the next 24 hours by having the current 24 hours. With the gradient boosting model (XGBoost) we can keep the prediction time as deep learning and RMSE error reduced. Finally, we experimentally concluded that machine learning methods work better than empirical methods and XGBoost model can have better performance in this problem among other implemented.

cs.LG↗