Machine Learning‐Assisted Development in Insulating Materials for High Voltage Applications

ABSTRACT The advancement of power systems imposes increasingly stringent performance requirements on insulating materials, making the development of high‐performance insulating materials a critical challenge in the field of electrical engineering. Traditional material development relies heavily on empirical trial‐and‐error approaches, which are time‐consuming and cost‐intensive and thus cannot keep pace with the rapid iteration of modern power systems. Machine learning (ML) offers a powerful data‐driven paradigm to overcome these limitations. By extracting complex structure–property relationships from experimental and computational datasets, ML enables efficient performance prediction, formulation optimisation and inverse molecular design. This review first systematically presents the fundamental workflow of ML‐assisted materials research and summarises representative ML algorithms and their applicability to specific material science problems. Subsequently, it reviews key advances in ML applications for gaseous, liquid and solid insulating materials, highlighting the value of ML in supporting insulating materials research. Finally, it discusses major challenges in ML‐assisted insulating materials research and proposes potential solutions. By outlining the workflow of ML‐assisted materials research, summarising landmark advancements and exploring critical challenges in ML‐assisted insulating material research, this review aims to provide guidance for researchers seeking to leverage ML to elucidate structure–property relationships and accelerate the discovery of novel high‐performance insulating materials.