  In this work, the dynamic realms of Materials Science and Computer Science
advancements meet the critical challenge of identifying efficient descriptors
capable of capturing the essential features of physical systems. Such task has
remained formidable, with solutions often involving ad-hoc scalar and vectorial
sets of materials properties, making optimization and transferability
challenging. We extract representations directly from ab-initio differential
electron charge density profiles using Neural Networks, highlighting the
pivotal role of transfer learning in such task. Firstly, we demonstrate
significant improvements in regression of a specific defected-materials
property with respect to training a deep network from scratch, both in terms of
predictions and their reproducibilities, by considering various pre-trained
models and selecting the optimal one after fine-tuning. The remarkable
performances obtained confirmed the transferability of the existent pre-trained
Convolutional Neural Networks (CNNs) on physics domain data, very different
from the original training data. Secondly, we demonstrate a saturation in the
regression capabilities of computer vision models towards properties of an
extensive variety of undefected systems, and how it can be overcome with the
help of large language model (LLM) transformers, with as little text
information as composition names. Finally, we prove the insufficiency of
open-models, like GPT-4, in achieving the analogous tasks and performances as
the proposed domain-specific ones. The work offers a promising avenue for
enhancing the effectiveness of descriptor identification in complex physical
systems, shedding light over the power of transfer learning to easily adapt and
combine available models, with different modalities, to the physics domain, at
the same time opening space to a benchmark for LLMs capabilities in such
domain.
