Review: Simulation Models for Materials and Biomolecules

Taft, Carlton A.; Canchaya, Jose Gabriel Solano; Santos, José; Silva, Junio Cesar Francisco

doi:10.1007/978-3-030-62226-8_2

Cited by 4 publications

(1 citation statement)

References 388 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML is a potential option for identifying acceptable and efficient materials without relying on trial and error. Recently, the use of ML to predict material characteristics such as solubility, band gaps, density, scattering, refractive index, transmittance, absorption, and reflectance has been presented [55]. There are various machine learning algorithms that are employed for this purpose and can be used as forward solver, as indicated in the Table 1.…”

Section: Introductionmentioning

confidence: 99%

AI-driven perovskite solar cells optimization

Faizan,

Ijaz,

Mehmood

et al. 2024

Data Science for Photonics and Biophotonics

View full text Add to dashboard Cite

Perovskite solar cells (PSCs) are renowned for their efficiency, affordability, and mass manufacturing. However, the performance unpredictability, material sensitivity and stability issues, and optimization limit their practicality. This study includes the challenges related to PSCs and the role of Artificial Intelligence (AI) in their advancement. AI has shown that it can accelerate the PSC's designs by finding creative solutions. The design assistance provided through AI-based methods reduces the experimentation time and need for resources, enabling real-time production monitoring and control. These methods identify performance bottlenecks and forecast the device efficiency in various settings. In this paper, we have simulated three perovskite solar cell devices (MASnI3, FASnI3, and MAGeI3) using SCAPS-1D with ETL as ZnO and HTL as Cu2O. Random Forest technique has been used for optimization and prediction of the best PSCs efficiency where the conduction band density of state, thickness of the absorber layer, hole mobility, valence band density of state, and electron mobility have served as design variables. The MSE and R2 scores for performance prediction are 1.37× 10-3 and 0.992 for MASnI3, 4.21 × 10-3 and 0.997 for FASnI3, and 0.79 × 10-3 and 0.993 for MAGeI3 respectively.

show abstract

Section: Introductionmentioning

confidence: 99%