Recent Advances in the Design and Molecular Dynamics Simulations of Polymeric Materials

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2602

Special Issue Editors


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Guest Editor
Department of Chemical Engineering (EQ), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
Interests: computation chemistry; molecular dynamics; Monte Carlo; complex materials; nanomaterials; self-assembly and aggregation;

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Guest Editor
DC Technology & Corporate Venturing, Agustín de Betancourt s/n, 28935 Móstoles, Madrid, Spain
Interests: computational chemistry; machine learning; sequential learning; active learning; physics-based simulation; kinetic modelling

Special Issue Information

Dear colleagues,

The structural determination of polymeric materials is often hampered by its inner complexity, featuring large segments of amorphous organization, interspersed by areas of crystallized materials of variable sizes. Such inner disorganization makes it extremely complicated to comprehend their molecular structure and hinders the design of new polymeric materials of specific and advanced applications. Molecular simulations, specifically Molecular Dynamics, arise as a powerful alternative tool to understand, at the atomistic level, the physic-chemical basis that determine those properties that will encompass their practical use. In this Special Issue, we present the latest advances on the use of molecular dynamics to characterize the organization of such materials, from which most key properties stem. The main aim is to demonstrate how the combination of experimental information with the simulation of polymeric systems allows one to increase the understanding and development of new advanced materials of better applicability in fields, such as materials science nanotechnology, biomedicine, molecular biology, or pharmaceutics. Not only does this Special Issue focus on what Molecular Dynamics can contribute but also in the combination of the technique with others that belong to the field of Computational Chemistry in order to show how the in silico approach boosts the progress in the discipline.

Dr. David Zanuy
Dr. Guillem Revilla-López
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • molecular dynamics
  • ultrastructure of polymers
  • synergic development
  • relation structure, dynamics, and properties
  • new algorithms for polymers
  • new force fields for polymers

Published Papers (1 paper)

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Research

19 pages, 1319 KiB  
Article
Design of New Dispersants Using Machine Learning and Visual Analytics
by María Jimena Martínez, Roi Naveiro, Axel J. Soto, Pablo Talavante, Shin-Ho Kim Lee, Ramón Gómez Arrayas, Mario Franco, Pablo Mauleón, Héctor Lozano Ordóñez, Guillermo Revilla López, Marco Bernabei, Nuria E. Campillo and Ignacio Ponzoni
Polymers 2023, 15(5), 1324; https://doi.org/10.3390/polym15051324 - 06 Mar 2023
Cited by 3 | Viewed by 2164
Abstract
Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models [...] Read more.
Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models to predict the dispersancy efficiency of oil and lubricant additives, a critical property in their design that can be estimated through a quantity named blotter spot. We propose a comprehensive approach that combines machine learning techniques with visual analytics strategies in an interactive tool that supports domain experts’ decision-making. We evaluated the proposed models quantitatively and illustrated their benefits through a case study. Specifically, we analyzed a series of virtual polyisobutylene succinimide (PIBSI) molecules derived from a known reference substrate. Our best-performing probabilistic model was Bayesian Additive Regression Trees (BART), which achieved a mean absolute error of 5.50±0.34 and a root mean square error of 7.56±0.47, as estimated through 5-fold cross-validation. To facilitate future research, we have made the dataset, including the potential dispersants used for modeling, publicly available. Our approach can help accelerate the discovery of new oil and lubricant additives, and our interactive tool can aid domain experts in making informed decisions based on blotter spot and other key properties. Full article
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