Evidence map›Paper›PMID 38800726›Full record

ReviewACS physical chemistry Au2024

A Vision for the Future of Multiscale Modeling.

Matteo Capone, Marco Romanelli, Davide Castaldo, Giovanni Parolin, Alessandro Bello, Gabriel Gil, Mirko Vanzan

Abstract readReview
In one paragraph

Review in ACS physical chemistry Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Strong light-matter interactions in hybrid polaritonic systems.Chemical communications (Cambridge, England) · 2026
    Review
  2. Article
  3. Utility-Scale Quantum Computational Chemistry.The journal of physical chemistry letters · 2026
    Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Emerging Advanced Electronic Packaging Materials for Thermal Management in Power Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Unravelling MnPhysical chemistry chemical physics : PCCP · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Matteo CaponeDepartment of Physical and Chemical Sciences, University of L'Aquila, L'Aquila 67010, Italy.ORCID https://orcid.org/0000-0002-0534-4190
Marco RomanelliDepartment of Chemical Sciences, University of Padova, Padova 35131, Italy.ORCID https://orcid.org/0000-0002-4330-9740
Davide CastaldoDepartment of Chemical Sciences, University of Padova, Padova 35131, Italy.
Giovanni ParolinDepartment of Chemical Sciences, University of Padova, Padova 35131, Italy.ORCID https://orcid.org/0000-0003-0072-3092
Alessandro BelloDepartment of Chemical Sciences, University of Padova, Padova 35131, Italy.
Gabriel GilDepartment of Chemical Sciences, University of Padova, Padova 35131, Italy.
Mirko VanzanDepartment of Physics, University of Milano, Milano 20133, Italy.ORCID https://orcid.org/0000-0003-3521-8045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID38800726
PMCPMC11117712

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.