An atomistic–continuum multiscale analysis for heterogeneous nanomaterials and its application in nanoporous gold foams

Nikravesh, Y.; Sameti, A. Rezaei; Khoei, A.R.

doi:10.1016/j.apm.2022.02.029

Cited by 13 publications

(1 citation statement)

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“…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. We quickly go through the description of QM/MM/Continuum methods, which combine different aspects of the more traditional mentioned methods, while intentionally omitting any specific reference to MM/Continumm approaches since they are less employed within physical chemistry and akin fields of research, even though there are notable exceptions (see e.g., ref ). We then conclude this section with an overview of the state of the art of new techniques such as Machine Learning (ML) and Quantum Computing (QC), showing their potentialities and highlighting how they are already impacting the field. , The following part of this work is devoted to illustrate our vision about the evolution of multiscale modeling in the next decades.…”

Section: Introductionmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

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The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

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Section: Introductionmentioning

confidence: 99%