Bulk Heterojunction Morphologies with Atomistic Resolution from Coarse-Grain Solvent Evaporation Simulations

Perpendicular crossing chains enable high mobility in a noncrystalline conjugated polymer

The nature of interchain π-system contacts, and their relationship to hole transport, are elucidated for the high-mobility, noncrystalline conjugated polymer C16-IDTBT by the application of scanning tunneling microscopy, molecular dynamics, and quantum chemical calculations. The microstructure is shown to favor an unusual packing motif in which paired chains cross-over one another at near-perpendicular angles. By linking to mesoscale microstructural features, revealed by coarse-grained molecular dynamics and previous studies, and performing simulations of charge transport, it is demonstrated that the high mobility of C16-IDTBT can be explained by the promotion of a highly interconnected transport network, stemming from the adoption of perpendicular contacts at the nanoscale, in combination with fast intrachain transport.

The surface tension of Martini 3 water mixtures

The Martini model, a coarse-grained forcefield for biomolecular simulations, has experienced a vast increase in popularity in the past decade. Its building-block approach balances computational efficiency with high chemical specificity, enabling the simulation of organic and inorganic molecules. The modeling of coarse-grained beads as Lennard-Jones particles poses challenges for the accurate reproduction of liquid-vapor interfacial properties, which are crucial in various applications, especially in the case of water. The latest version of the forcefield introduces refined interaction parameters for water beads, tackling the well-known artifact of Martini water freezing at room temperature. In addition, multiple sizes of water beads are available for simulating the solvation of small cavities, including the smallest pockets of proteins. This work focuses on studying the interfacial properties of Martini water, including surface tension and surface thickness. Employing the test-area method, we systematically compute the liquid-vapor surface tension across various combinations of water bead sizes and for temperatures from 300 to 350 K. These findings are of interest to the Martini community as they allow users to account for the low interfacial tension of Martini water by properly adjusting observables computed via coarse-grained simulations to allow for accurate matching against all-atom or experimental results. Surface tension data are also interpreted in terms of local enrichment of the various mixture components at the liquid-vapor interface by means of Gibbs' adsorption formalism. Finally, the critical scaling of the Martini surface tension with temperature is reported to be consistent with the critical exponent of the 3D Ising universality class.

Designing a Novel Wide Bandgap Small Molecule Guest for Enhanced Stability and Morphology Mediation in Ternary Organic Solar Cells with over 19.3% Efficiency

In this study, a novel wide-bandgap small molecule guest material, ITOA, designed and synthesized for fabricating efficient ternary organic solar cells (OSCs) ITOA complements the absorbance of the PM6:Y6 binary system, exhibiting strong crystallinity and modest miscibility. ITOA optimizes the morphology by promoting intensive molecular packing, reducing domain size, and establishing a preferred vertical phase distribution. These features contribute to improved and well-balanced charge transport, suppressed carrier recombination, and efficient exciton dissociation. Consequently, a significantly enhanced efficiency of 18.62% for the ternary device is achieved, accompanied by increased short-circuit current density (JSC), fill factor (FF), and open-circuit voltage (VOC). Building on this success, replacing Y6 with BTP-eC9 leads to an outstanding PCE of 19.33% for the ternary OSCs. Notably, the introduction of ITOA expedites the formation of the optimized morphology, resulting in an impressive PCE of 18.04% for the ternary device without any postprocessing. Moreover, the ternary device exhibits enhanced operational stability under maximum power point (MPP) tracking. This comprehensive study demonstrates that a rationally designed guest molecule can optimize morphology, reduce energy loss, and streamline the fabrication process, essential for achieving high efficiency and stability in OSCs, paving the way for practical commercial applications.

Assessing molecular doping efficiency in organic semiconductors with reactive Monte Carlo

The addition of molecular dopants into organic semiconductors (OSCs) is a ubiquitous augmentation strategy to enhance the electrical conductivity of OSCs. Although the importance of optimizing OSC-dopant interactions is well-recognized, chemically generalizable structure-function relationships are difficult to extract due to the sensitivity and dependence of doping efficiency on chemistry, processing conditions, and morphology. Computational modeling for an integrated OSC-dopant design is an attractive approach to systematically isolate fundamental relationships, but requires the challenging simultaneous treatment of molecular reactivity and morphology evolution. We present the first computational study to couple molecular reactivity with morphology evolution in a molecularly doped OSC. Reactive Monte Carlo is employed to examine the evolution of OSC-dopant morphologies and doping efficiency with respect to dielectric, the thermodynamic driving for the doping reaction, and dopant aggregation. We observe that for well-mixed systems with experimentally relevant dielectric constants, doping efficiency is near unity with a very weak dependence on the ionization potential and electron affinity of OSC and dopant, respectively. At experimental dielectric constants, reaction-induced aggregation is observed, corresponding to the well-known insolubility of solution-doped materials. Simulations are qualitatively consistent with a number of experimental studies showing a decrease of doping efficiency with increasing dopant concentration. Finally, we observe that the aggregation of dopants lowers doping efficiency and thus presents a rational design strategy for maximizing doping efficiency in molecularly doped OSCs. This work represents an important first step toward the systematic integration of molecular reactivity and morphology evolution into the characterization of multi-scale structure-function relationships in molecularly doped OSCs.

Inducing molecular orientation in solution-processed thin films of fluorene-bithiophene-based copolymer: thermal annealing vs. solvent additive

A deep understanding of the factors influencing the morphology of thin films based on conjugated polymers is essential to boost their performance in optoelectronic devices. Herein, we investigated the electronic structure and morphology of thin films of the copolymer poly(9,9-dioctyl-fluorenyl-co-bithiophene) (F8T2) in its pristine form as well as samples processed with the solvent additive 1,8-diiodooctane (DIO) or post-processed through thermal annealing treatment. Measurements were carried out using angle-resolved S K-edge NEXAFS (near-edge X-ray absorption fine structure) in total electron yield (TEY) and fluorescence yield (FY) detection modes. Two main transitions were observed at the S 1s NEXAFS spectra: S 1s → π* and S 1s → σ* (S-C). The observed dichroism pointed to a face-on orientation of the conjugated backbone, which was significantly increased for F8T2 films processed with DIO. Resonant Auger decay spectra were obtained and analyzed using the core-hole clock (CHC) method. An enhancement in the charge transfer process was observed for thermally annealed films, especially for samples processed with DIO, corresponding to an increase in film ordering. Furthermore, the investigated films were characterized using X-ray photoelectron spectroscopy, attesting to the presence of the thiophene unit in the samples and demonstrating that some of its sulfur atoms were positively polarized in the F8T2 films. All these experimental findings were compared with molecular dynamics (MD) simulations of film evaporation with and without DIO. The use of MD, together with mathematical modeling, was able to explain the major effects found in the experiments, including the polarization of sulfur atoms. The simultaneous use of powerful spectroscopic techniques and theoretical methods shed light on key aspects linking film morphology with fabrication procedures.

Martinoid: the peptoid martini force field

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log D7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software.

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.

Ab Initio Study of Charge Separation Dynamics and Pump-Probe Spectroscopy in the P3HT/PCBM Blend

We develop a bottom-up computational method for excited-state dynamics and time-resolved spectroscopy signals in molecular aggregates, on the basis of ab initio excited-state calculations. As an application, we consider the charge separation dynamics and pump-probe spectroscopy in the amorphous P3HT/PCBM blend. To simulate quantum dynamics and time-resolved spectroscopy, the model Hamiltonian for single-excitation and double-excitation manifolds was derived on the basis of fragment-based excited-state calculations within the GW approximation and the Bethe-Salpeter equation. After elucidating the energetics of the electron-hole separation and examining linear absorption spectrum, we investigated the quantum dynamics of exciton and charge carriers in comparison with the pump-probe transient absorption spectra. In particular, we introduced the pump-probe excited-state absorption (ESA) anisotropy as a spectroscopic signature of charge carrier dynamics after exciton dissociation. We found that the charge separation dynamics can be probed by the pump-probe ESA anisotropy dynamics after charge-transfer excitations. The present study provides the fundamental information for understanding the experimental spectroscopy signals, by elucidating the relationship between the excited states, the exciton and charge carrier dynamics, and time-resolved spectroscopy.

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.

Modeling the Effect of Disorder in the Two-Dimensional Electronic Spectroscopy of Poly-3-hexyltiophene in an Organic Photovoltaic Blend: A Combined Quantum/Classical Approach

We introduce a first-principles model of the 12-mer poly-3-hexyltiophene (P3HT) polymer system in the realistic description of an organic photovoltaic blend environment. We combine Molecular Dynamics (MD) simulations of a thin-film blend of P3HT and phenyl-C61-butyric acid methyl ester (PCBM) to model the interactions with a fluctuating environment with Time-Dependent Density Functional Theory (TDDFT) calculations to parametrize the effect of the torsional flexibility in the polymer and construct an exciton-type Hamiltonian that describes the photoexcitation of the polymer. This allows us to reveal the presence of different flexibility patterns governed by the torsional angles along the polymer chain which, in the interacting fluctuating environment, control the broadening of the spectral observables. We identify the origin of the homogeneous and inhomogeneous line shape of the simulated optical signals. This is paramount to decipher the spectroscopic nature of the ultrafast electron-transfer process occurring in organic photovoltaic (OPV) materials.

Coarse-Grained Molecular Dynamics of pH-Sensitive Lipids

pH-sensitive lipids represent a class of lipids that can be protonated and destabilized in acidic environments, as they become positively charged in response to low-pH conditions. They can be incorporated into lipidic nanoparticles such as liposomes, which are able to change their properties and allow specific drug delivery at the acidic conditions encountered in some pathological microenvironments. In this work, we used coarse-grained molecular-dynamic simulations to study the stability of neutral and charged lipid bilayers containing POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and various kinds of ISUCA ((F)2-(imidazol-1-yl)succinic acid)-derived lipids, which can act as pH-sensitive molecules. In order to explore such systems, we used a MARTINI-derived forcefield, previously parameterized using all-atom simulation results. We calculated the average area per lipid, the second-rank order parameter and the lipid diffusion coefficient of both lipid bilayers made of pure components and mixtures of lipids in different proportions, under neutral or acidic conditions. The results show that the use of ISUCA-derived lipids disturbs the lipid bilayer structure, with the effect being particularly marked under acidic conditions. Although more-in depth studies on these systems must be carried out, these initial results are encouraging and the lipids designed in this research could be a good basis for developing new pH-sensitive liposomes.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Development of a transferable coarse-grained model of polydimethylsiloxane

Polydimethylsiloxane (PDMS) is a popular silicon-based polymer with advanced applications in microfluidics and nanocomposites. The slow dynamics of polymer chains in such complex systems hinders molecular dynamics investigations based on all atom force fields. This limitation can be overcome by exploiting finely tuned coarse-grained (CG) models. This paper develops a transferable CG model of PDMS, compatible with the recent Martini 3 force field, using structural and thermodynamic properties as targets in the parametrization, including a vast set of experimental free energies of transfer. We validate the model transferability by reproducing the correct scaling laws for the PDMS gyration radius in the melt and good and bad solvents. We successfully test the model by reproducing the wetting behavior of water and acetonitrile on PDMS and the phase behavior of a PDMS-peptide triblock copolymer system. This work sets the stage for computational studies involving the interaction between PDMS and many synthetic and biological molecules modeled within the Martini framework.

Ionic liquid-assisted synthesis of mesoporous polymers and carbon materials: the self-assembly mechanism

Soft-templating synthesis has been widely employed to fabricate ordered mesoporous polymer and carbon materials with effectively tuneable pore sizes. However, the commonly used templating agents, block copolymers, are normally decomposed during the process, thus are barely recyclable; this increases the costs and hampers the scale-up feasibility. Therefore, it becomes imperative to seek promising alternatives; amphiphilic ionic liquids (ILs) are excellent candidates due to their good recyclability. This study explored the templating behaviour of IL templates for preparing mesoporous polymers and carbons. In details, the self-assembly of ternary systems (comprising of IL templates, precursors and solvent) were investigated by a combination of coarse-grained molecular dynamics (CGMD) simulations, density function theory (DFT) calculations and experimental techniques. The results indicate that the morphologies of IL templates are tuneable not only by the adjustment of water content in the mixture but also by the selection of suitable precursors. Material precursors containing increasing numbers of hydroxyl moieties also induce various precursor-template spatial correlations, resulting in different topological structures of nanomaterials. This work presents a fundamental investigation into the mechanisms of templating synthesis with amphiphilic ILs as recyclable templates and gives insight into the effective design of coveted carbon nanomaterials for targeted applications.

Modelling structural properties of cyanine dye nanotubes at coarse-grained level

Self-assembly is a ubiquitous process spanning from biomolecular aggregates to nanomaterials. Even though the resulting aggregates can be studied through experimental techniques, the dynamic pathways of the process and the molecular details of the final structures are not necessarily easy to resolve. Consequently, rational design of self-assembling aggregates and their properties remains extremely challenging. At the same time, modelling the self-assembly with computational methods is not trivial, because its spatio-temporal scales are usually beyond the limits of all-atom based simulations. The use of coarse-grained (CG) models can alleviate this limitation, but usually suffers from the lack of optimised parameters for the molecular constituents. In this work, we describe the procedure of parametrizing a CG Martini model for a cyanine dye (C8S3) that self-assembles into hollow double-walled nanotubes. First, we optimised the model based on quantum mechanics calculations and all-atom reference simulations, in combination with available experimental data. Then, we conducted random self-assembly simulations, and the performance of our model was tested on preformed assemblies. Our simulations provide information on the time-dependent local arrangement of this cyanine dye, when aggregates are being formed. Furthermore, we provide guidelines for designing and optimising parameters for similar self-assembling nanomaterials.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Coarse-grained molecular dynamics simulations of immobilized micelle systems and their interactions with hydrophobic molecules

Micelles immobilized in polymer materials are of emerging interest in drug delivery, water treatment and other applications. Immobilization removes the need for membrane-based separation to eliminate micelles from the medium, enabling facile extraction and delivery in diverse industries. This work lays out a coarse-grained molecular dynamics simulations framework for the rapid identification of surfactants for use in immobilized micelle systems. Micelles are immobilized by constraining one end of the constituent surfactants in space, mimicking what would occur in a copolymer system. We demonstrate that constraints affect how the micelles interact with small hydrophobic molecules, making it important to account for their effects in various drug-micelle and pollutant-micelle simulations. Our results show that in several systems there is stronger interaction between hydrophobic small molecules and micelles in immobilized systems compared to unconstrained systems. These strengthened interactions can have important implications for the design of new micelle-based extraction and delivery processes.

Understanding the performance differences between solution and vacuum deposited OLEDs: A computational approach

Solution-processing of organic light-emitting diode films has potential advantages in terms of cost and scalability over vacuum-deposition for large area applications. However, solution processed small molecule films can have lower overall device performance. Here, novel molecular dynamics techniques are developed to enable faster simulation of solvent evaporation that occurs during solution processing and give films of thicknesses relevant to real devices. All-atom molecular dynamics simulations are then used in combination with kinetic Monte Carlo transport modeling to examine how differences in morphology stemming from solution or vacuum film deposition affect charge transport and exciton dynamics in films consisting of light-emitting bis(2-phenylpyridine)(acetylacetonate)iridium(III) [Ir(ppy)2(acac)] guest molecules in a 4,4′-bis( N-carbazolyl)biphenyl host. While the structures of the films deposited from vacuum and solution were found to differ, critically, only minor variations in the transport properties were predicted by the simulations even if trapped solvent was present.

Martini 3 coarse-grained force field for poly(para-phenylene ethynylene)s

Poly(para-phenylene ethynylene)s, or short PPEs, are a class of conjugated and semi-flexible polymers with a strongly delocalized π electron system and increased chain stiffness. Due to this, PPEs have a wide range of technological applications. Although the material properties of single-chains or mixtures of few PPE chains have been studied in detail, the properties of large assemblies remain to be fully explored. Here, we developed a coarse-grained model for PPEs with the Martini 3 force field to enable computational studies of PPEs in large-scale assembly. We used an optimization geometrical approach to take the shape of the π conjugated backbone into account and also applied an additional angular potential to tune the mechanical bending stiffness of the polymer. Our Martini 3 model reproduces key structural and thermodynamic observables of single PPE chains and mixtures, such as persistence length, density, packing and stacking. We show that chain entanglement increases with the expense of nematic ordering with growing PPE chain length. With the Martini 3 PPE model at hand, we are now able to cover large spatio-temporal scales and thereby to uncover key aspects for the structural organization of PPE bulk systems. The model is also predicted to be of high applicability to investigate out-of-equilibrium behavior of PPEs under mechanical force.

Collective Variables for Free Energy Surface Tailoring: Understanding and Modifying Functionality in Systems Dominated by Rare Events

We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the semiautomated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using harmonic linear discriminant analysis (HLDA), a machine-learning-based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in a manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.

Mesoscopic Modeling of a Highly-Ordered Sanidic Polymer Mesophase and Comparison With Experimental Data

Board-shaped polymers form sanidic mesophases: assemblies of parallel lamellae of stacked polymer backbones separated by disordered side chains. Sanidics vary significantly with respect to polymer order inside their lamellae, making them “stepping stones” toward the crystalline state. Therefore, they are potentially interesting for studying crystallization and technological applications. Building on earlier mesoscopic models of the most disordered sanidics Σ_d, we focus on the other extreme, near-crystalline order, and develop a generic model that captures a highly ordered Σ_r mesophase. Polymers are described by generic hindered-rotation chains. Anisotropic nonbonded potentials, with strengths comparable to the thermal energy, mimic board-like monomer shapes. Lamellae equilibrated with Monte Carlo simulations, for a broad range of model parameters, have intralamellar order typical for Σ_r mesophases: periodically stacked polymers that are mutually registered along their backbones. Our mesophase shows registration on both monomer and chain levels. We calculate scattering patterns and compare with data published for highly ordered sanidic mesophases of two different polymers: polyesters and polypeptoids. Most of the generic structural features that were identified in these experiments are present in our model. However, our mesophase has correlations between chains located in different lamellae and is therefore closer to the crystalline state than the experimental samples.

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

Decades of work in the field of computational study of semiconducting polymers using atomistic models illustrate the challenges of generating equilibrated models for this class of materials. While adopting a coarse-grained model can be helpful, the process of developing a suitable model is particularly non-trivial and time-consuming for semiconducting polymers due to a large number of different interactions with some having an anisotropic nature. This work introduces a procedure for the rapid generation of a hybrid model for semiconducting polymers where atoms of secondary importance (those in the alkyl side chains) are transformed into coarse-grained beads to reduce the computational cost of generating an equilibrated structure. The parameters are determined from easy-to-equilibrate simulations of very short oligomers and the model is constructed to enable a very simple back-mapping procedure to reconstruct geometries with atomistic resolution. The model is illustrated for three related polymers containing DPP (diketopyrrolopyrrole) to evaluate the transferability of the potential across different families of polymers. The accuracy of the model, determined by comparison with the results of fully equilibrated simulations of the same material before and after back-mapping, is fully satisfactory for two out of the three cases considered. We noticed that accuracy can be determined very early in the workflow so that it is easy to assess when the deployment of this method is advantageous. The hybrid representation can be used to evaluate directly the electronic properties of structures sampled by the simulations.

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

We use atomistic as well as coarse-grained molecular dynamics simulations to study the conformation of a single poly(3-hexylthiopene) chain as a function of temperature. We find that mainly bundle and toroid structures appear with bundles becoming more abundant for decreasing temperatures. We compare an atomistic and a Martini-based coarse-grained model which we find in very good agreement. We further illustrate how the temperature dependence of P3HT can be connected to that of simple Lennard–Jones model polymers in a vacuum. Upon adding solvent (THF) we observe the occurrence of a prominent swelling of the molecular size at a temperature of about 220 K. This swelling is in close agreement with the interpretation of recent spectroscopic experiments which allows us to explain the experimental observations by an increased frequency of bundle structures.

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 10⁷ speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

Why does solvent treatment increase conductivity of PEDOT:PSS? Insight from molecular dynamics simulations

Poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) is one of the most important conducting polymers. In its pristine form its electrical conductivity is low, but it can be enhanced by several orders of magnitude by...

Thermoplastic Elastomer Tunes Phase Structure and Promotes Stretchability of High-Efficiency Organic Solar Cells

Top-performance organic solar cells (OSCs) consisting of conjugated polymer donors and nonfullerene small molecule acceptors (NF-SMAs) deliver rapid increases in efficiencies. Nevertheless, many of the polymer donors exhibit high stiffness and small molecule acceptors are very brittle, which limit their applications in wearable devices. Here, a simple and effective strategy is reported to improve the stretchability and reduce the stiffness of high-efficiency polymer:NF-SMA blends and simultaneously maintain the high efficiency by incorporating a low-cost commercial thermoplastic elastomer, polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS). The microstructure, mechanical properties, and photovoltaic performance of PM6:N3 with varied SEBS contents and the molecular weight dependence of SEBS on microstructure and mechanical properties are thoroughly characterized. This strategy for mechanical performance improvement exhibits excellent applicability in some other OSC blend systems, e.g., PBQx-TF:eC9-2Cl and PBDB-T:ITIC. More crucially, the elastic modulus of such complex ternary blends can be nicely predicted by a mechanical model. Therefore, incorporating thermoplastic elastomers is a widely applicable and cost-effective strategy to improve mechanical properties of nonfullerene OSCs and beyond.

Local structuring of diketopyrrolopyrrole (DPP)-based oligomers from molecular dynamics simulations

The microscopic structure of high mobility semiconducting polymers is known to be essential for their performance but it cannot be easily deduced from the available experimental data. A series of short oligomers of diketopyrrolopyrrole (DPP)-based materials that display high charge mobility are studied by molecular dynamics simulations to understand their local structuring at an atomic level. Different analyses are proposed to compare the ability of different oligomers to form large aggregates and their driving force. The simulations show that the tendency for this class of materials to form aggregates is driven by the interaction between DPP fragments, but this is modulated by the other conjugated fragments of the materials which affect the rigidity of the polymer and, ultimately, the size of the aggregates that are formed. The main structural features and the electronic structure of the oligomers are fairly similar above the glass transition temperature and at room temperature.

Nonconverged Constraints Cause Artificial Temperature Gradients in Lipid Bilayer Simulations

Molecular dynamics (MD) simulations have become an indispensable tool to investigate phase separation in model membrane systems. In particular, simulations based on coarse-grained (CG) models have found widespread use due to their increased computational efficiency, allowing for simulations of multicomponent lipid bilayers undergoing phase separation into liquid-ordered and liquid-disordered domains. Here, we show that a significant temperature difference between molecule types can artificially arise in CG MD membrane simulations with the standard Martini simulation parameters in GROMACS. In particular, the linear constraint solver (LINCS) algorithm does not converge with its default settings, resulting in serious temperature differences between molecules in a time step-dependent manner. We demonstrate that the underlying reason for this behavior is the presence of highly constrained moieties, such as cholesterol. Their presence can critically impact numerous structural and dynamic membrane properties obtained from such simulations. Furthermore, any preference of these molecules toward a certain membrane phase can lead to spatial temperature gradients, which can amplify the degree of phase separation or even induce it in compositions that would otherwise mix well. We systematically investigated the effect of the integration time step and LINCS settings on membrane properties. Our data show that for cholesterol-containing membranes, a time step of 20 fs should be combined with at least lincs_iter = 2 and lincs_order = 12, while using a time step of 30 fs requires at least lincs_iter = 3 and lincs_order = 12 to bring the temperature differences to a level where they do not perturb central membrane properties. Moreover, we show that in cases where stricter LINCS settings are computationally too demanding, coupling the lipids in multiple groups to the temperature bath offers a practical workaround to the problem, although the validity of this approach should be further verified. Finally, we show that similar temperature gradients can also emerge in atomistic simulations using the CHARMM force field in combination with settings that allow for a 5 fs integration step.

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, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

CHARMM-GUI Polymer Builder for Modeling and Simulation of Synthetic Polymers

Molecular modeling and simulations are invaluable tools for polymer science and engineering, which predict physicochemical properties of polymers and provide molecular-level insight into the underlying mechanisms. However, building realistic polymer systems is challenging and requires considerable experience because of great variations in structures as well as length and time scales. This work describes Polymer Builder in CHARMM-GUI (http://www.charmm-gui.org/input/polymer), a web-based infrastructure that provides a generalized and automated process to build a relaxed polymer system. Polymer Builder not only provides versatile modeling methods to build complex polymer structures, but also generates realistic polymer melt and solution systems through the built-in coarse-grained model and all-atom replacement. The coarse-grained model parametrization is generalized and extensively validated with various experimental data and all-atom simulations. In addition, the capability of Polymer Builder for generating relaxed polymer systems is demonstrated by density calculations of 34 homopolymer melt systems, characteristic ratio calculations of 170 homopolymer melt systems, a morphology diagram of poly(styrene-b-methyl methacrylate) block copolymers, and self-assembly behavior of amphiphilic poly(ethylene oxide-b-ethylethane) block copolymers in water. We hope that Polymer Builder is useful to carry out innovative and novel polymer modeling and simulation research to acquire insight into structures, dynamics, and underlying mechanisms of complex polymer-containing systems.

Martini 3: a general purpose force field for coarse-grained molecular dynamics

The coarse-grained Martini force field is widely used in biomolecular simulations. Here we present the refined model, Martini 3 ( http://cgmartini.nl ), with an improved interaction balance, new bead types and expanded ability to include specific interactions representing, for example, hydrogen bonding and electronic polarizability. The updated model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.

Nanotechnology for catalysis and solar energy conversion

This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure-property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here, we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for the excited-state energy of oligothiophenes against time-dependent density functional theory (TDDFT) calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiophene) in its single-crystal and solution phases using the transfer learning models trained with the data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.

Transfer Learned Designer Polymers For Organic Solar Cells

Organic photovoltaic (OPV) materials have been examined extensively over the past two decades for solar cell applications because of the potential for device flexibility, low-temperature solution processability, and negligible environmental impact. However, discovery of new candidate OPV materials, especially polymer-based electron donors, that demonstrate notable power conversion efficiencies (PCEs), is nontrivial and time-intensive exercise given the extensive set of possible chemistries. Recent progress in machine learning accelerated materials discovery has facilitated to address this challenge, with molecular line representations, such as Simplified Molecular-Input Line-Entry Systems (SMILES), gaining popularity as molecular fingerprints describing the donor chemical structures. Here, we employ a transfer learning based recurrent neural (LSTM) model, which harnesses the SMILES molecular fingerprints as an input to generate novel designer chemistries for OPV devices. The generative model, perfected on a small focused OPV data set, predicts new polymer repeat units with potentially high PCE. Calculations of the similarity coefficient between the known and the generated polymers corroborate the accuracy of the model predictability as a function of the underlying chemical specificity. The data-enabled framework is sufficiently generic for use in accelerated machine learned materials discovery for various chemistries and applications, mining the hitherto available experimental and computational data.

Protocol for Simulations of PEGylated Proteins with Martini 3

Enhancement of proteins by PEGylation is an active area of research. However, the interactions between polymer and protein are far from fully understood. To gain a better insight into these interactions or even make predictions, molecular dynamics (MD) simulations can be applied to study specific protein-polymer systems at molecular level detail. Here we present instructions on how to simulate PEGylated proteins using the latest iteration of the Martini coarse-grained (CG) force-field. CG MD simulations offer near atomistic information and at the same time allow to study complex biological systems over longer time and length scales than fully atomistic-level simulations.

Charge-transfer electronic states in organic solar cells: a TDDFT study

The prediction of new organic photovoltaic materials in organic solar cells (OSCs) must include a precise description of charge-transfer states because they are involved in electron-transfer processes such as charge separation and charge recombination which govern the device efficiency. Also, as the experimental performance of an optoelectronic device is measured for nonequilibrium nanostructures, computational approaches need models that can incorporate morphology effects. Usually, this aspect is treated by molecular dynamics simulation (MDS) methodologies; however, methodologies and formalisms to calculate the electron-transfer processes are still controversial and sometimes do not connect their information with the phase morphologies. In this work we propose a simple and fast characterization of electron-transfer processes to find the rate constants by analysing the distribution of vertical excitation energies of both local excitation (LE) and charge-transfer (CT) states using TD-DFT calculations in the donor-acceptor pair structures which were extracted from MDS. This proposal assumes that conformational changes are prevented and equilibria are not achieved while the electron-transfer events take effect, and thus the only pathway that connects the LE and CT states is their surface crossing point where an ideal distribution might exist. Different density functionals and dialectric models were tested. The results indicate a close relationship between the proposal and experimental data for electron-transfer events, suggesting the application of this method in the rational design of new photovoltaic materials.