Development of hybrid coarse-grained atomistic models for rapid assessment of local structuring of polymeric semiconductors

Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these relationships require the complex integration of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, these methods pose substantial computational challenges that limit their application to the requisite length scales of soft material morphologies. Here, we demonstrate the bottom-up electronic coarse-graining (ECG) of morphology-dependent electronic structure in the liquid-crystal-forming semiconductor, 2-(4-methoxyphenyl)-7-octyl-benzothienobenzothiophene (BTBT). ECG is applied to construct density functional theory (DFT)-accurate valence band Hamiltonians of the isotropic and smectic liquid crystal (LC) phases using only the CG representation of BTBT. By bypassing the atomistic resolution and its prohibitive computational costs, ECG enables the first calculations of the morphology dependence of the electronic structure of charge carriers across LC phases at the ∼20 nm length scale, with robust statistical sampling. Kinetic Monte Carlo (kMC) simulations reveal a strong morphology dependence on zero-field charge mobility among different LC phases as well as the presence of two-molecule charge carriers that act as traps and hinder charge transport. We leverage these results to further evaluate the feasibility of developing mesoscopic, field-based ECG models in future works. The fully CG approach to electronic property predictions in LC semiconductors opens a new computational direction for designing electronic processes in soft materials at their characteristic length scales.

Microstructural Model of Indacenodithiophene-co-benzothiadiazole Polymer: π-Crossing Interactions and Their Potential Impact on Charge Transport

Morphological and electronic properties of indacenodithiophene-co-benzothiadiazole (IDTBT) copolymer with varying molecular weights are calculated through combined molecular dynamics (MD) and quantum chemical (QC) methods. Our study focuses on the polymer chain arrangements, interchain connectivity pathways, and interplay between morphological and electronic structure properties of IDTBT. Our models, which are verified against GIWAXS measurements, show a considerable number of BT-BT π-π interactions with a (preferential) perpendicular local orientation of polymer chains due to the steric hindrance of bulky side chains around IDT. Although our models predict a noncrystalline structure for IDTBT, the BT-BT (interchain) crossing points show a considerable degree of short-range order in spatial arrangement which most likely result in a mesh-like structure for the polymer and provide efficient pathways for interchain charge transport.

Morphology of conducting polymer blends at the interface of conducting and insulating phases: insight from PEDOT:PSS atomistic simulations

Having phase-separated conductive and less-conductive domains is a common morphology in semiconducting polymer blends as it exists in the case of PEDOT:PSS, which is a representative example with a wide range of applications. In this paper, we constructed atomistic models for the interface between the PEDOT-rich (conductive) grains and the PSS-rich (less-conductive) phase through molecular dynamics simulations. Our models are obtained from experimentally relevant compositions, based on precise force field parameters, and through a robust relaxation procedure. We show that both PEDOT-rich and PSS-rich phases consist of PEDOT lamellae embedded in PSS chains. The size of these lamellae depends on the PEDOT concentration in each phase and our model predictions are in quantitative agreement with the experimental data. Furthermore, our models suggest that neither the phases nor the interfaces are entirely connected by π-π stacking. Thus, inter-lamellae tunnelling is essential for both intra- and inter-grain charge transport. We also show that a small increase (≈8 wt%) in the PEDOT concentration results in rather larger lamellae sizes, considerably more oriented lamellae, and slightly better inter-lamellae connectivity, which result in enhanced intra-grain conductivity. Moreover, we show how enhancing phase separation between PEDOT-rich and PSS-rich domains (similar to the effect of polar co-solvents), i.e., pulling out PEDOT from the PSS-rich phase and adding it in the PEDOT-rich phase, highly enhances the intra-grain connectivity but decreases the inter-grain conduction paths through the interface. Our results explain how the marginal extra degree of phase separation (based on experimentally obtained values) could result in a great enhancement in the overall film conductivity.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.