Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Nanostructured smart materials as functional components of medical devices

The field of medical devices has experienced, more than others, technological advances, developments and innovations, thanks to the rapidly expanding scientific knowledge and collaboration between different disciplines such as biology, engineering and materials science. The design of functional components can be achieved by exploiting composite materials based on nanostructured smart materials, that due to the inherent characteristics of single constituents develop unique properties that make them suitable for different applications preserving excellent mechanical proprieties. For instance, recent developments have focused on the fabrication of piezoelectric devices with multiple biomedical functions, as actuation and sensing functions in one component for monitoring pressure signals. The present Ph.D. Thesis aims at investigating nanostructured smart materials embedded into a polymeric matrix to obtain a composite material that can be used as a functional component for medical devices. (i) Nanostructured piezoelectric material with self-sensing capability was successfully manufactured by using ceramic (i.e. lead zirconate titanate (PZT)) and (ii) polymeric (i.e. poly(vinylidene fluoride-trifluoro ethylene (PVDF-TRFE)) piezoelectric materials. PZT nanofibers were obtained by sol-gel electrospinning starting from synthetized PZT precursor solution. Synthesis, sol-gel electrospinning process, and thermal treatment were accurately controlled to obtain PZT nanofibers dimensionally stable with densely packed grains in the perovskite phase. To guarantee the impact resistance of the laminate, the morphology and size of the hosting filler were accurately designed by increasing the surface area to volume ratio. Moreover, to solve the issue relative to the mechanical discrepancy between rigid electronic materials/soft human tissues/different material of the device (iii) a nanostructured flexible composite material based on a network of Poly-L-lactic acid (PLLA) made of curled nanofibers that present a tuneable mechanical response as a function of the applied stress was successful fabricated.