AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials is presented.
The optical and photocatalytic activities show that bismuth-based double perovskites have comparatively better absorption in the visible region than indium-based double perovskites, which may facilitate the rapid screening and future discovery of new double-perovskite materials.
Evaluated classical machine-learning models trained on different descriptor sets including DFT band gap, non-orbital compositional descriptors, orbital-related descriptors, SOAP structural fingerprints, and a reduced mixed descriptor set to predict DFT-derived transport conductivity demonstrate that accurate prediction...
The results demonstrate that machine learning can efficiently identify promising dopant configurations while providing reliable predictions of dopant-dependent material properties, and offers a practical approach for the computational design of doped 2D oxide semiconductors for future optoelectronic and sensing applica...
An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.
This integrated approach offers a practical pathway for designing high-performance, stable, and environmentally sustainable perovskite solar cells (PSCs) by identifying absorber layer thickness as the dominant factor influencing efficiency.
The proposed hybrid framework demonstrates a robust and computationally efficient pathway for designing high-efficiency perovskite solar cells, while highlighting the critical role of MXene-assisted interface engineering in enhancing device performance and defect tolerance.
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