arXiv subjects

Riccardo Alessandri

Publications and source records attributed to Riccardo Alessandri.

6 recordsLinked to original sources

arXiv2024

Numerical Study of Cleavable Bond-Modified Polyethylene for Circular Polymer Design

There is considerable interest in designing new polymeric materials with built-in mechanisms for recycling. In this work, we present a systematic exploration of several polyethylene-based polymers that contain strategically incorporated cleavable bonds that are susceptible to chemical degradation. We consider ten distinct telechelic functionalities across varying chain lengths: ester, aromatic ester, anhydride, carbonate, urethane, siloxane, oxalate, acetal, urea, and amide linkages. Our study is focused on the effects of cleavable bonds on density, diffusion, enthalpy, and melting temperatures. We also examine the crystallization process. These are key properties for polymer processing and, by comparing them to those of linear polyethylene, we establish a platform that will help guide future synthetic efforts towards the design of sustainable plastics with performance comparable to that of traditional, high-performance polyolefin-based materials.

cond-mat.soft↗

arXiv2024

Thermodynamic and Transport Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes from Physics-Informed and Machine-Learned Models

The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights.

cond-mat.soft↗

arXiv2023

Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions

The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. Description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally-averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely non-conjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach, and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows, and provides a framework for the development of CG models that retain electronic structure information.

cond-mat.soft↗

arXiv2023

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system's configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this new tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

physics.comp-ph↗

arXiv2021

Polyply: a python suite for facilitating simulations of (bio-)macromolecules and nanomaterials

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that leverages 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating melt simulations of six different polymers using two force-fields with different resolution. We further demonstrate the power of our approach by setting up a multi lamellar microphase-separated block copolymer system for next generation batteries, and by generating a liquid-liquid phase separated polyethylene oxide-dextran system inside a lipid vesicle, featuring both branching and molecular weight distribution of the dextran component.

cond-mat.mtrl-sci↗

arXiv2020

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here we highlight the main applications to date of the Martini model in materials science, and we give a perspective for the future developments in this field, in particular in light of recent developments such as the new version of the model, Martini 3.

cond-mat.mtrl-sci↗