Capone Matteo, Romanelli Marco, Castaldo Davide, Parolin Giovanni, Bello Alessandro, Gil Gabriel, Vanzan Mirko
Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila 67010, Italy.
Department of Chemical Sciences, University of Padova, Padova 35131, Italy.
ACS Phys Chem Au. 2024 Mar 4;4(3):202-225. doi: 10.1021/acsphyschemau.3c00080. eCollection 2024 May 22.
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.
现代计算机科学的兴起使物理化学在理解和利用自然及人工现象方面取得了巨大进展。然而,尽管在过去几十年中取得了进步,但计算资源仍然不足以从第一原理彻底模拟扩展系统。事实上,无数的生物、催化和光物理过程需要从头算处理才能得到恰当描述,但所涉及的长度和时间尺度范围使得这在实际中几乎不可行。解决这些问题的一种方法是通过将系统划分为在不同近似水平下处理的域,将在不同尺度上工作的理论和算法结合起来,范围从量子力学到经典分子动力学,甚至包括连续介质电动力学。这种方法被称为多尺度建模,在过去60年中的应用已经取得了显著成果。考虑到理论、算法设计和计算能力的快速进步,我们相信多尺度建模在未来几年将大规模发展成为一种主导的研究方法。在此,我们描述了在其领域内开发的主要方法,突出了它们的成就和当前的缺点,最终考虑到机器学习和量子计算等新计算技术的出现,为未来发展提出了一个合理的方向。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,重点是复杂的光捕获过程的模拟,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个域在相互正确交互的同时移动和扩展。尽管这一愿景非常宏大,但我们相信计算机科学的快速发展将导致这些技术的大幅改进和广泛应用,从而在物理化学乃至我们的社会中取得巨大进步。