A Vision for the Future of Multiscale Modeling

IF 3.7 Q2 CHEMISTRY, PHYSICAL
Matteo Capone, Marco Romanelli, Davide Castaldo, Giovanni Parolin, Alessandro Bello, Gabriel Gil and Mirko Vanzan*, 
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引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

多尺度建模的未来愿景
现代计算机科学的兴起使物理化学在理解和利用自然与人工现象方面取得了巨大进步。然而,尽管过去几十年来取得了巨大进步,计算资源仍然不足以从第一原理出发对扩展系统进行彻底模拟。事实上,无数的生物、催化和光物理过程都需要进行ab initio处理才能得到正确的描述,但由于所涉及的长度和时间尺度范围很广,这实际上是不可行的。解决这些问题的方法是将不同尺度的理论和算法结合起来,将系统划分为不同近似程度的领域,从量子力学到经典分子动力学,甚至包括连续电动力学。这种方法被称为多尺度建模,在过去 60 年的应用中取得了显著的成果。考虑到理论、算法设计和计算能力的飞速发展,我们相信多尺度建模将在未来几年内迅速成长为一种主流研究方法。在此,我们将介绍在多尺度建模领域开发的主要方法,强调这些方法的成就和目前存在的缺陷,并最终提出未来发展的合理方向,同时考虑到机器学习和量子计算等新计算技术的出现。然后,我们讨论了如何利用先进的多尺度建模方法来应对关键的科学挑战,重点是模拟复杂的采光过程,如自然光合作用。在此过程中,我们提出了一种前沿的计算范式,即在一个系统上同时进行多尺度计算,允许以适当精度处理的各种域移动和扩展,同时它们之间适当地相互作用。尽管这一愿景雄心勃勃,但我们相信,计算机科学的快速发展将带来这些技术的巨大进步和广泛应用,从而推动物理化学的巨大进步,并最终推动我们社会的巨大进步。
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来源期刊
CiteScore
3.70
自引率
0.00%
发文量
0
期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
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