Temperature-Dependent Conformation Behavior of Isolated Poly(3-hexylthiopene) Chains

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer-Nanoparticle Hybrid Systems

Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer-nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer-nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer-nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field.

Molecular Dynamics Analysis of Silica/PMMA Interface Shear Behavior

The mechanical properties of cementitious materials injected by epoxy have seldom been modeled quantitatively, and the atomic origin of the shear strength of polymer/concrete interfaces is still unknown. To understand the main parameters that affect crack filling and interface strength in mode II, we simulated polymethylmethacrylate (PMMA) injection and PMMA/silica interface shear deformation with Molecular Dynamics (MD). Injection simulation results indicate that the notch filling ratio increases with injection pressure (100 MPa–500 MPa) and temperature (200 K–400 K) and decreases with the chain length (4–16). Interface shear strength increases with the strain rate (1×108 s−1–1×109 s−1). Smooth interfaces have lower shear strengths than polymer alone, and under similar injection conditions, rough interfaces tend to be stronger than smooth ones. The shear strength of rough interfaces increases with the filling ratio and the length of the polymer chains; it is not significantly affected by temperatures under 400 K, but it drops dramatically when the temperature reaches 400 K, which corresponds to the PMMA melting temperature for the range of pressures tested. For the same injection work input, a higher interface shear strength can be achieved with the entanglement of long molecule chains rather than with asperity filling by short molecule chains. Overall, the mechanical work needed to break silica/PMMA interfaces in mode II is mainly contributed by van der Waals forces, but it is noted that interlocking forces play a critical role in interfaces created with long polymer chains, in which less non-bond energy is required to reach failure in comparison to an interface with the same shear strength created with shorter polymer chains. In general, rough interfaces with low filling ratios and long polymer chains perform better than rough interfaces with high filling ratios and short polymer chains, indicating that for the same injection work input, it is more efficient to use polymers with high polymerization.