Soft Matter Molecular Simulations

15/08/2026

A new paper from our group is now out in JACS

This work continues a long-standing interest of ours in solid–liquid transitions and molecular memory from triglyceride crystallization with Alejandro G Marangoni to polymer melt memory with Alejandro Müller.

A key part of this study is the analysis through density fields: instead of looking only at individual molecular configurations, we follow how collective spatial organization survives melting and evolves in time. This field-based view helps reveal structural information that is difficult to see from molecular snapshots alone, and connects microscopic conformations with emerging mesoscale order.

Different systems, same broad question: what information does matter retain when it crosses a phase transition?

And apparently, having an Alejandro among the experimental collaborators is becoming part of the method. 🙂

How Polymer Chains Remember Their Crystalline Past: Simulations Uncover the Molecular Origin of Melt Memory

Abstract. Melt memory in semicrystalline polymers is the remarkable ability of polymer chains to retain structural information from a prior crystalline sta

28/05/2026

Pleased to share that an invited Perspective article is now available online in *Polymer Science & Technology* on the ACS website
as part of the special issue Industry-Relevant Polymer Science and Technology

The title is:

Sustainable Polyolefin Science: A Two-Voice Perspective Toward a Shared Academic–Industrial Vision

This article originated from an invitation by the journal, but even more from a series of discussions, exchanges of ideas, and shared reflections developed over the past years during collaborative research activities between academia and industry.

Many of the concepts discussed in the Perspective emerged gradually through interactions with Alexandra Albunia and Markus Gahleitner at Borealis, and with Veronica Ambrogi at the University of Naples Federico II, while working at the interface between polymer science, industrial materials development, modelling, and sustainability.

The Perspective addresses the future of polyolefin science from a broad lifecycle viewpoint: from catalyst and material design to formulation, processing, recycling, environmental fate, and the education of the next generation of scientists.

Within this broader framework, multiscale molecular simulations, hybrid particle-field models, and AI/data-driven approaches are discussed as important tools to support a more predictive and physically grounded polymer science.

Polyolefins have shaped modern society, but their future requires a fundamental shift in how they are designed, produced, used, and reused/recycled. This perspective, written as a two-voice contribution from academia and industry, examines how decades of scientific understanding enabled the rapid ex...

04/04/2026

29/03/2026

Hybrid Particle-Field MD goes neural! A new cover on Macromolecules.

Neural networks can be efficiently trained using local density fields as physical order parameter. The multiscale workflow integrates coarse graining from atoms to beads and voxelization into a 3D density field as the physical order parameter for deep learning in block copolymer/carbon nanotube nanocomposites.

Link to the paper in the first comment

27/02/2026

“Designing New Polymers by Combining High-Throughput and Artificial Intelligence Tools” (EUPOC 2026) to be held in Bertinoro (Italy), May 18-21, 2026.

With a worldwide production of nearly 450 million tons per year, polymer materials play a central role in our modern society. They are used in the manufacture of innumerable daily-life products, or as more sophisticated compounds in medicine, diagnostics, and fine chemistry. However, economical and new societal constraints require a more rational design and alternative synthesis, formulation and processing methods for polymer manufacturing to meet the need for greater sustainability, more virtuous end-of-life management, while maintaining optimal performances in application. Polymer-based materials of the future will be one of the pillars of the circular economy. Thus, the discovery of new polymers will lead to a paradigm shift and new methodologies for the design, processing and analysis of polymer-based materials. The recent development of high-throughput (HTP) and artificial intelligence (AI) methods has opened up enormous opportunities to tackle these challenges. While such methods are emerging in chemistry, they have not yet been implemented in Polymer Science.

Thus, the EUPOC2026 proposes the state of art in the fields related to :

1 - High-throughput methods for synthesis and characterization from macromolecular architectures to physical properties, i.e. considering molecular, macromolecular, and materials scales. Inputs of polymer modelling

2 - Data management using Artificial Intelligence tools from the data collection, analyses (deep learning, neuronal networks, data mining) to the specific issues related to polymers, i.e. polymer fingerprint/digital standard

3 - Combination of HTP approaches with AI tools in order to take profit of machine-learning approaches for designing optimized materials.

List of topics to be addressed:

-Last developments of high-throughput methods for polymer synthesis (synthesis in flux, production of gradient-based polymer materials, X-Y generation, etc)

-Last developments of high-throughput methods for polymer characterization: NMR, IR, Raman, SEC, scattering methods including Tg determination, mechanical properties, gas barrier, etc

-Data collection and management such as data mining basics.

-Artificial Intelligence tools for polymer scientists. Basis on machine-methods – Basis on neuronal networks, deep learning, etc.
-Last developments of AI methods to polymer discovery (polymers, composites, processing)
-Last developments of combining AI tools and high-throughput methods for polymers or related materials
-How to train polymer scientists to AI tools ?
-(Flash/short) presentations for HT and AI tools suppliers

The European Polymer Federation (EPF) in collaboration with Italian Association of Macromolecular Science and Technology (AIM) is honored and pleased to announce the organization of the EUropean POlymer Conference “Designing New Polymers by Combining High-Throughput and Artificial Intelligence Too...

27/02/2026

Physics-Informed Graph Learning for Field-Based Molecular Models

In two of our recent works, we introduced a new paradigm that integrates hybrid particle–field molecular dynamics (hPF-MD) with graph neural networks, moving beyond black-box machine learning toward physically grounded representation learning.

Links to the two papers are in the first two comments.

24/02/2026

New Journal Cover on Macromolecules

A schematic illustration of the hydrogen-bonding interactions involved in methanol sorption in poly(ether-imide) as approached by molecular dynamics. This is part of a multiscale theoretical approach spanning from quantum mechanical to macroscopic thermodynamic scales combined with infrared vibrational spectroscopy measurements.

Methanol Sorption in Poly(ether imide): Molecular Insights from a Multiscale Study Combining Experiments, Theory, and Simulations

Reference in first comment

07/02/2026

Università di Salerno 20 Febbraio 2026

04/11/2025

A new paper out for Athanassios Z. Panagiotopoulos Festschrift: Simulations of Phase Transitions and Self-Assembly in Complex Fluids

Viscosity Calculations with Hybrid Particle-Field Molecular Dynamics Simulations

https://pubs.acs.org/doi/10.1021/acs.jpcb.5c03244?ref=vi_panagiotopoulos-festschrift

18/07/2025

Quest'anno festeggio il mio 11° anniversario su Facebook. Grazie per il continuo supporto. Non ce l'avrei mai fatta senza di voi. 🙏🤗🎉