Multiscale Computational Approaches toward the Understanding of Materials

Abstract

Herewith, an overview of the group's collaborative research efforts on the development and deployment of computational approaches to understand materials and tools at different length and time scales is presented. The techniques employed range from quantum mechanical approaches based on the density functional theory to classical atomistic and coarse-grained force field methods, targeting molecular systems composed of a few to several million atoms at different levels of detail. These new tools and molecular models are presented to the computational materials science community so they can be used in more realistic molecular modelling studies of the properties of materials and their dependence on subtle modifications of their structures. The review concludes by presenting a selection of recent computational case-studies oriented toward the understanding of the synthesis of materials, the interpretation of unexpected experimental results, the prediction of material properties, and the materials selection based on their characteristics for applications in areas such as gas adsorption/separation, corrosion protection, and catalysis.

A Vision for the Future of Multiscale Modeling

“…19−21 Furthermore, as it continues to evolve and adapt, it crosses the borders of chemistry and becomes an interdisciplinary technique, commonly adopted in fields like biochemistry, 22,23 computer science, 24 nanoscience, 11,25 drug discovery, 26 and materials science. 27,28 The capability to contribute in such diverse yet technologically relevant fields makes multiscale modeling one of the most powerful tool we have to address some of the major challenges of our time, such as global warming, energy supply, and disease control. 29,30 In this vision, we first systematically review the main multiscale modeling approaches employed in physical chemistry to date, namely QM/Continuum, QM/MM, and QM/ QM.…”

“…Thanks to the enormous advances in high-performance computing and to the huge developments of computational algorithms reached in the past decades, multiscale modeling has become the workhorse of physical chemistry, constantly providing insights on countless chemical problems of much scientific interest. − Furthermore, as it continues to evolve and adapt, it crosses the borders of chemistry and becomes an interdisciplinary technique, commonly adopted in fields like biochemistry, , computer science, nanoscience, , drug discovery, and materials science. , The capability to contribute in such diverse yet technologically relevant fields makes multiscale modeling one of the most powerful tool we have to address some of the major challenges of our time, such as global warming, energy supply, and disease control. , …”

Influence of Ethanol Parametrization on Diffusion Coefficients Using OPLS-AA Force Field

“…MD has been used to simulate the dynamics of molecular systems within a given period of time, by numerically integrating Newton’s equations of motion and upon consideration of quantum or classical mechanics to calculate the forces between the particles in the molecular systems under study, leading to the so-called ab initio MD (AIMD) or classical MD approaches, respectively, with the former being much more time consuming than the latter [ 24 ]. Classical MD simulations rely on sets of empirical parameters, a.k.a.…”