The structures of materials play a critical role in defining their unique properties and applications. In particular, the engineering of materials to enhance their performance or understand their defect, dislocation, impurity, or catalytic activity requires knowledge of atomistic-level structures. Atomistic modeling techniques utilize atomic-level information to simulate the macroscopic chemical and physical properties of materials. These modeling techniques range from simulating systems with a few hundred atoms to employing statistical models for systems with thousands or millions of atoms.
In this talk, the first part will focus on presenting a selection of my previous works that utilize atomistic modeling and simulation techniques to investigate the structures of MOFs (MetalOrganic Frameworks) membranes and explore the behavior of ionic liquids in supercapacitors.
This part will highlight the connection between atomistic structures and the macroscopic properties and applications of these materials.
The second part of the talk will delve into my ongoing project here at IMDEA, which aims to employ a multi-scale simulation model and machine learning techniques to study the relationship between the structures of MOF/polymer mixed matrix materials and their mechanical properties. This project will showcase how atomistic modeling, combined with advanced computational approaches, can provide valuable insights into the mechanical behavior of complex materials.
By presenting these works and discussing the ongoing project, the talk will demonstrate the importance of atomistic modeling in understanding the structures of materials and their impact on properties and applications. It will highlight the potential of using advanced simulation techniques and machine learning approaches to gain deeper insights into the behavior of materials at different scales, fostering advancements in materials science and engineering