Predictive Modelling of Electronic Materials: A Review of Deep Learning Techniques in Computer Engineering
Keywords:
Computer Engineering, Deep Learning, Electronic Materials, Graph Neural Networks, Materials Discovery, Predictive ModelingAbstract
This review evaluates the application of deep learning (DL)
for the predictive modeling of electronic materials in
computer engineering. We analyzed peer-reviewed
literature across four major databases, focusing exclusively
on advanced architectures like Graph Neural Networks
(GNNs) and Generative models. Results indicate these
models accurately predict critical properties, such as band
gaps and thermal conductivity, for next-generation
semiconductors, 2D materials, and memristors. These high
accuracies are achieved because architectures like GNNs
effectively capture complex 3D spatial interactions without
requiring manual feature engineering. However, practical
fabrication remains hindered by data scarcity, algorithmic
opacity, and a profound "Sim-to-Real Gap". While DL
accelerates predictive design, sustaining Moore's Law
ultimately requires developing autonomous "Self-Driving
Labs" and Large Material Models to bridge digital predictions
with physical synthesis.
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