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      A Vision for the Future of Multiscale Modeling

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          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.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Quantum mechanical continuum solvation models.

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              Quantum Computing in the NISQ era and beyond

              Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.
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                Author and article information

                Journal
                ACS Phys Chem Au
                ACS Phys Chem Au
                pg
                apcach
                ACS Physical Chemistry Au
                American Chemical Society
                2694-2445
                04 March 2024
                22 May 2024
                : 4
                : 3
                : 202-225
                Affiliations
                []Department of Physical and Chemical Sciences, University of L’Aquila , L’Aquila 67010, Italy
                []Department of Chemical Sciences, University of Padova , Padova 35131, Italy
                [§ ]Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia , Modena 41125, Italy
                []Instituto de Cibernética, Matemática y Física (ICIMAF) , La Habana 10400, Cuba
                []Department of Physics, University of Milano , Milano 20133, Italy
                Author notes
                Author information
                https://orcid.org/0000-0002-0534-4190
                https://orcid.org/0000-0002-4330-9740
                https://orcid.org/0000-0003-0072-3092
                https://orcid.org/0000-0003-3521-8045
                Article
                10.1021/acsphyschemau.3c00080
                11117712
                1b4580ed-b1d7-46cd-921d-b3e0a14ace3e
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 30 December 2023
                : 01 February 2024
                : 31 January 2024
                Funding
                Funded by: Università degli Studi di Milano, doi 10.13039/100012352;
                Award ID: NA
                Funded by: Ministero dell’Istruzione, dell’Università e della Ricerca, doi 10.13039/501100003407;
                Award ID: NA
                Categories
                Perspective
                Custom metadata
                pg3c00080
                pg3c00080

                multiscale modeling,multiscale simulation,qm/qm,qm/mm,qm/continuum,photosynthesis,computational chemistry

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