Machine Learning and Polymer Modelling
A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Artificial Intelligence in Polymer Science".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 36
Special Issue Editors
2. Institute of Applied and Computational Mathematics, FORTH, GR-70013 Heraklion, Crete, Greece
3. Computation-Based Science and Technology Research Center, The Cyprus Institute, Nicosia, Cyprus
Interests: multiscale modeling; soft condensed matter; physics-based data driven simulation methods; polymer nanocomposites; nanostructured materials
Special Issues, Collections and Topics in MDPI journals
Interests: polymer melts, blends and nanocomposites; association of biomolecules in membrane environments; reaction-diffusion phenomena in patterning applications; development of novel algorithms for multiscale modeling; computational characterization of materials coupled to scattering experiments
Interests: molecular simulation; multiscale modelling; machine learning-aided molecular simulation; macromolecules; polymer-based materials; complex fluids; soft materials; ionic liquids; multicomponent and composite materials
Special Issue Information
Dear Colleagues,
Machine Learning (ML) and advanced polymer modelling synergies are increasingly being exploited to redefine the ways that polymeric materials are understood, designed, and engineered. Polymer modelling has long relied en large on multiscale approaches that bridge molecular architecture, mesoscale structure, and macroscopic properties.
Today, the emergence of data-driven methods, ranging from supervised learning and surrogate models to generative architectures and physics-based ML, provides an opportunity to transform this landscape. ML-empowered computational methods are currently being explored with the aim of developing faster and often more accurate modelling protocols, which will also enable interpretable representations of complex polymer properties.
This Special Issue brings together research that advances the intersection of ML with computational polymer science, including atomistic simulations, coarse-graining strategies, mesoscale modelling, and continuum representations. Emphasis is placed on ML-aided force-field development, accelerated sampling, uncertainty quantification, active learning, polymer informatics and inverse design, hybrid physics–ML schemes, and the fusion of computational and experimental datasets. Contributions also highlight applications to functional and sustainable polymers, soft matter, energy materials, polymer-based composites and biomolecular systems.
By showcasing these innovations, this Special Issue aims to illuminate the potential of ML to transform polymer modelling, improving accuracy, reducing computational costs, and enabling the exploration of previously inaccessible chemical and morphological spaces. Collectively, these works aim to highlight open key issues and persisting challenges and outline future opportunities for data-driven, simulation-informed polymer science towards the advancement of fundamental understanding and thus of knowledge-driven scientific discovery and technological applications.
Prof. Dr. Vagelis Harmandaris
Dr. Manolis Doxastakis
Dr. Niki Vergadou
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning and AI in polymer science
- multi-scale polymer modelling
- physics-based machine learning
- data-driven polymer modelling
- scientific ML
- molecular simulation and ML integration
- polymer structure–property relationships
- polymer-based composites
- hierarchical and bottom-up modelling
- ML-assisted atomistic and coarse-grained models
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