TY - JOUR
T1 - Multi-Model and Hybrid AI Systems in Materials Research — Architectures and Trade-Offs
AU - Nguyen Thanh Huy
AU - Le Thi Bich
AU - Pham Quang Minh
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2023
VL - 2
IS - 2
DO - 10.68159/h788480315
SP - 27
N2 - The integration of multi-model and hybrid artificial intelligence (AI) systems has revolutionized materials research by enabling the efficient analysis of complex datasets, the prediction of material properties, and the optimization of design processes. This narrative review examines the architectures of these systems, including ensemble methods, multimodal data fusion, and physics-informed neural networks. It evaluates their applications in areas such as alloy design, nanomaterial synthesis, and battery management. Key trade-offs are discussed, encompassing computational efficiency versus predictive accuracy, data scarcity versus model generalizability, and interpretability versus performance in black-box models. Drawing on recent peer-reviewed literature, the review highlights how these AI approaches accelerate materials discovery while addressing challenges such as uncertainty quantification and scalability. By synthesizing current advancements, this work underscores the potential of hybrid AI to drive sustainable innovation in materials science, with implications for future interdisciplinary research.
UR - https://iamrp.net/h788480315
ER -