arXiv subjects

Chadawan Khamdang

Publications and source records attributed to Chadawan Khamdang.

3 recordsLinked to original sources

arXiv2026

Machine learning insights into band gap properties in halide-based perovskites

Halide perovskites show great promise for applications in optoelectronic devices. The lead-free perovskites are attracting increasing interest due to their low toxicity and motivate the exploration of alternative compositions and structures, including A$_2$BX$_6$, A$_2$BB$^\prime$X$_6$, A$_3$B$_2$X$_9$, and A$_4$BX$_6$. Accurate predictions of a wide range of band gap energies are important for designing new materials. It is also desired to generate a direct relationship between the structural and elemental descriptors and the band gap energies. In this work, we develop machine learning models to predict band gap energies across various types of halide perovskites based on atomic and structural properties. Algorithms including ensemble tree-based methods, random forest regression (RFR), gradient boosted regression trees (GBRT), and extreme gradient boosting (XGB) showed strong predictive accuracy. We also analyzed feature importance to identify key descriptors, including B-site and X-site elemental properties, as well as the number of A- and B-site atoms, as primary factors influencing band gap energies. These results improve our understanding of the ML models and provide guidance for designing new halide perovskite materials.

cond-mat.mtrl-sci↗

arXiv2024

Defect formation in CsSnI$_3$ from Density Functional Theory and Machine Learning

Sn-based perovskites as low-toxic materials are actively studied for optoelectronic applications. However, their performance is limited by $p$-type self-doping, which can be suppressed by substitutional doping on the cation sites. In this study, we combine density functional theory (DFT) calculations with machine learning (ML) to develop a predictive model and identify the key descriptors affecting formation energy and charge transition levels of the substitutional dopants in CsSnI$_{3}$. Our DFT calculations create a dataset of formation energies and charge transition levels and show that Y, Sc, Al, Nb, Ba, and Sr are effective dopants that pin the fermi level higher in the band gap, suppressing the $p$-type self-doping. We explore ML algorithms and propose training the random forest regression model to predict the defect formation properties. This work shows the predictive capability of combining DFT with machine learning and provides insights into the important features that determine the defect formation energetics.

cond-mat.mtrl-sci↗

arXiv2024

Surface phase diagram of CsSnI$_3$ from first-principles calculations

CsSnI$_3$ is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI$_3$. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI$_3$ (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.

cond-mat.mtrl-sci↗