Multiscale modelling: an industrial perspective

Computational modelling of materials and catalysis is now indispensable in an industrial setting, fuelled by powerful algorithms and increasing computational speed. This article explores how multiscale modelling techniques are being used to accelerate product development cycles and innovate the way in which new materials are discovered. We discuss current multiscale approaches starting from the atomic simulation to the continuum methods, illustrating how these are applied to provide insight and understanding to real-world catalysts. We illustrate how understanding reactions at atomic and molecular levels directly affects large-scale industrial processes. A key focus is the essential link between model predictions and experimental validation, often requiring advanced characterization. We also address current methodological limitations and explore solutions offered by emerging techniques such as machine learning (ML). Ultimately, this article highlights the transformative role of multiscale modelling in connecting microscopic phenomena with macroscopic performance, significantly accelerating the design and development of new industrial products. This article is part of the theme issue 'Surfaces, interfaces and heterogeneous catalysis'.

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Publication Details

Journal
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Published
2026-04-16
DOI
https://doi.org/10.1098/rsta.2024.0477
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Multiscale modelling: an industrial perspective

Misbah Sarwar, Alessandro Abena, Jane N. Mugo, Moyahabo Hellen Chuma et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Machine Learning in Materials Science
article

Multiscale modelling: an industrial perspective

Misbah Sarwar, Alessandro Abena, Jane N. Mugo, Moyahabo Hellen Chuma, Rachel N. Kerber, CS Cooper, Carlos Fonte, Christopher Perry
article en

Abstract

Computational modelling of materials and catalysis is now indispensable in an industrial setting, fuelled by powerful algorithms and increasing computational speed. This article explores how multiscale modelling techniques are being used to accelerate product development cycles and innovate the way in which new materials are discovered. We discuss current multiscale approaches starting from the atomic simulation to the continuum methods, illustrating how these are applied to provide insight and understanding to real-world catalysts. We illustrate how understanding reactions at atomic and molecular levels directly affects large-scale industrial processes. A key focus is the essential link between model predictions and experimental validation, often requiring advanced characterization. We also address current methodological limitations and explore solutions offered by emerging techniques such as machine learning (ML). Ultimately, this article highlights the transformative role of multiscale modelling in connecting microscopic phenomena with macroscopic performance, significantly accelerating the design and development of new industrial products. This article is part of the theme issue 'Surfaces, interfaces and heterogeneous catalysis'.

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering SciencesVol. 384(2318)
Johnson Matthey (Germany) (DE), Johnson Matthey (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 18%
Machine Learning in Materials Science
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