TY - JOUR
T1 - Multi-Modal Transformer Architectures for Correlating Processing Conditions to Functional Performance in Perovskite Solar Cells
AU - Ravi Kumar
AU - Neha Sharma
AU - Aniket Deshmukh
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2024
VL - 3
IS - 1
DO - 10.68159/r181465594
SP - 37
N2 - Due to their high power-conversion efficiency and low fabrication cost, Perovskite solar cells (PSCs) have been introduced as a promising technology in photovoltaics. However, optimizing their functional performance remains challenging due to the complex interplay among processing conditions, material structure, and resultant properties. This paper proposes a new conceptual framework that uses a multi-modal transformer architecture to correlate processing parameters with functional outcomes in PSCs, grounded in the processing-structure-performance (PSP) paradigm. By integrating different data modalities—such as textual descriptions of processing recipes, graphical representations of microstructures, and numerical performance metrics—the framework provides a unified theoretical model for understanding and predicting PSP relationships. Recent advances in artificial intelligence have enabled the architecture to use transformer-based mechanisms for cross-modal attention and fusion, facilitating the extraction of latent correlations without empirical data. This approach addresses limitations in traditional modeling by providing a scalable, interpretable means to conceptualize how variations in processing influence structural evolution and, ultimately, device efficiency and stability. The innovation of this framework lies in its modality-agnostic design, which theorizes emergent patterns in high-dimensional PSP spaces. Potential implications include accelerated theoretical insights for materials design, fostering advancements in sustainable energy technologies. This purely conceptual work synthesizes literature to establish a foundation for future theoretical explorations in applied artificial intelligence for materials science.
UR - https://iamrp.net/r181465594
ER -