Marek Grzesiak
Independent researcher, England
Publications
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Research Article
Transfer Learning for Novel Material Property Prediction Using Pretrained AI Models
Author(s): Marek Grzesiak*
Accurate prediction of material properties is essential for accelerating the discovery of innovative materials across applications such as electronics, energy, and structural engineering. However, traditional machine learning approaches require large, labeled datasets, which are often unavailable for novel or hypothetical compounds. This limitation significantly restricts their practical utility. To address this challenge, we propose a transfer learning framework that leverages pretrained deep learning models trained on large-scale materials datasets to predict physical, thermal, and electronic properties of new materials with minimal labeled data. Our method uses feature reuse and fine-tuning strategies to adapt neural networks to new prediction tasks across diverse material classes. We evaluate the framework on benchmark datasets from the Materials Project, OQMD, and AFLOW, focusing.. Read More»