Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics
Y Shang, Z Xiong, K An, JA Hauch… - Materials Genome …, 2024 - Wiley Online Library
The emerging photovoltaic (PV) technologies, such as organic and perovskite PVs, have the
characteristics of complex compositions and processing, resulting in a large …
characteristics of complex compositions and processing, resulting in a large …
[HTML][HTML] Multiclass blood cancer classification using deep CNN with optimized features
Breast cancer, lung cancer, skin cancer, and blood malignancies such as leukemia and
lymphoma are just a few instances of cancer, which is a collection of cells that proliferate …
lymphoma are just a few instances of cancer, which is a collection of cells that proliferate …
Strain Engineering on the Optoelectronic Properties of CsPbI3 Halide Perovskites: Ab-Initio Investigation
We have studied the effects of uniaxial strain on the electronic and optical properties in
CsPbI3 perovskite using density functional theory. The unstrained CsPbI3 has a band gap …
CsPbI3 perovskite using density functional theory. The unstrained CsPbI3 has a band gap …
[HTML][HTML] Predicting the Hall-Petch slope of magnesium alloys by machine learning
B Guan, C Chen, Y Xin, J Xu, B Feng, X Huang… - Journal of Magnesium …, 2023 - Elsevier
Hall-Petch slope (k) is an important material parameter, while there is a great challenge to
accurately predict the k value of magnesium alloys due to a high dependence of k on the …
accurately predict the k value of magnesium alloys due to a high dependence of k on the …
Rapidly predicting Kohn–Sham total energy using data-centric AI
Predicting material properties by solving the Kohn-Sham (KS) equation, which is the basis of
modern computational approaches to electronic structures, has provided significant …
modern computational approaches to electronic structures, has provided significant …
A machine learning framework for predicting device performance in 2D metal halide perovskite photodetector
Abstract Two-dimensional (2D) Metal halide Perovskite Photodetectors (MHP-PDs) have
attracted significant attention owing to their promising performance with high responsivity …
attracted significant attention owing to their promising performance with high responsivity …
Swarm smart meta-estimator for 2D/2D heterostructure design
Two-dimensional (2D) semiconductors are central to many scientific fields. The combination
of two semiconductors (heterostructure) is a good way to lift many technological deadlocks …
of two semiconductors (heterostructure) is a good way to lift many technological deadlocks …
An interpretable hybrid machine learning prediction of dielectric constant of alkali halide crystals
J Deng, G Jia - Chemical Physics, 2022 - Elsevier
Exploring the data-driven prediction strategy of physical and chemical properties is attractive
for the rational design of crystal dielectrics with target characteristics, especially for the …
for the rational design of crystal dielectrics with target characteristics, especially for the …
Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data
Abstract The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion
battery characterization is an extensively recognized non-destructive procedure for both in …
battery characterization is an extensively recognized non-destructive procedure for both in …
Deep learning-driven QSPR models for accurate properties estimation in organic solar cells using extended connectivity fingerprints
Bulk heterojunction solar cell (BHJ) materials represent a promising avenue for enhancing
environmental stability and practicality in solar cell technology. However, the vast array of …
environmental stability and practicality in solar cell technology. However, the vast array of …