Machine Learning-Guided Conductivity Prediction in 2D Organic Metal Chalcogenides for Accelerated Electromagnetic Wave Absorber Design
Ruru Gao, Hongcheng Shang, Qinglin Zhou, Bao-Feng Tan, Xiu-Shen Wei, Jinghui Zhang, Yingzhi Jiao, Weijin Li
Abstract
S/m), significantly outperforming empirical approaches. Notably, it demonstrates robust extrapolation capability by correctly predicting 12 out of 15 novel OMCs beyond the training data set. Guided by conductivity predictions, the EMW absorption performance of specific 2D OMCs can be efficiently ranked, accelerating material design with experimentally validated accuracy. This machine learning-assisted strategy reveals the complex relationship between synthesis parameters and conductivity, expediting the design and synthesis of materials with optimized EMW absorption performance.
Topics & Concepts
Materials scienceExpeditingConductivityExtrapolationAbsorption (acoustics)Nonlinear systemComputer scienceParameter spaceBiological systemComposite materialMathematicsPhysicsEngineeringQuantum mechanicsSystems engineeringBiologyStatisticsMathematical analysisPerovskite Materials and Applications2D Materials and ApplicationsConducting polymers and applications