Transformers in Material Science: Roles, Challenges, and Future Scope

Abstract

This study explores the diverse applications, challenges, and future prospects of employing vision transformers in various material science domains, including biomaterials, ceramic materials, composite materials, energy materials, magnetic materials, electronics and photonic materials, materials synthesis, polymers, and nanomaterials. In the realm of biomaterials, the application of vision transformers has significantly improved our understanding of biological interactions, leading to the development of innovative medical implants and drug delivery systems. In ceramic materials, these transformers have revolutionized design and production processes, ensuring higher durability and efficiency. Likewise, in composite materials, they have enabled the creation of lightweight yet robust structures, transforming industries from aerospace to automotive. Energy materials research has greatly benefited from vision transformers, facilitating the discovery of novel materials for energy storage and conversion. Additionally, the study of magnetic materials has been transformed by their ability to analyze intricate magnetic patterns, aiding in the development of advanced data storage technologies. In electronics and photonic materials, vision transformers have accelerated the evolution of compact and high-performance devices. Integrating vision transformers poses challenges in managing vast and intricate datasets, ensuring model interpretability, and addressing ethical concerns related to data privacy and bias. As vision transformers continue to advance, their application in materials synthesis, polymers, and nanomaterials is anticipated to yield groundbreaking discoveries. This study highlights the way forward, underscoring the importance of collaborative efforts between computer scientists and materials researchers to unlock the full potential of vision transformers in reshaping the landscape of material science.

Keywords: Transformer, Deep Learning, Convolutional Neural Network, Features Extraction, Materials