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, e.g. hydrogen bonding and electronic polarizability. The new 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.

In this contribution, for the first time, the molecular n-doping of a donor-acceptor (D-A) copolymer achieving 200-fold enhancement of electrical conductivity by rationally tailoring the side chains without changing its D-A backbone is successfully improved. Instead of the traditional alkyl side chains for poly{[N,N'-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl](NDI)-alt-5,5'-(2,2'-bithiophene)} (N2200), polar triethylene glycol type side chains is utilized and a high electrical conductivity of 0.17 S cm after doping with (4-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)phenyl)dimethylamine is achieved, which is the highest reported value for n-type D-A copolymers. Coarse-grained molecular dynamics simulations indicate that the polar side chains can significantly reduce the clustering of dopant molecules and favor the dispersion of the dopant in the host matrix as compared to the traditional alkyl side chains. Accordingly, intimate contact between the host and dopant molecules in the NDI-based copolymer with polar side chains facilitates molecular doping with increased doping efficiency and electrical conductivity. For the first time, a heterogeneous thermoelectric transport model for such a material is proposed, that is the percolation of charge carriers from conducting ordered regions through poorly conductive disordered regions, which provides pointers for further increase in the themoelectric properties of n-type D-A copolymers.

Control over the morphology of the active layer of bulk heterojunction (BHJ) organic solar cells is paramount to achieve high-efficiency devices. However, no method currently available can predict morphologies for a novel donor–acceptor blend. An approach which allows reaching relevant length scales, retaining chemical specificity, and mimicking experimental fabrication conditions, and which is suited for high-throughput schemes has been proven challenging to find. Here, we propose a method to generate atom-resolved morphologies of BHJs which conforms to these requirements. Coarse-grain (CG) molecular dynamics simulations are employed to simulate the large-scale morphological organization during solution-processing. The use of CG models which retain chemical specificity translates into a direct path to the rational design of donor and acceptor compounds which differ only slightly in chemical nature. Finally, the direct retrieval of fully atomistic detail is possible through backmapping, opening the way for improved quantum mechanical calculations addressing the charge separation mechanism. The method is illustrated for the poly(3-hexyl-thiophene) (P3HT)–phenyl-C61-butyric acid methyl ester (PCBM) mixture, and found to predict morphologies in agreement with experimental data. The effect of drying rate, P3HT molecular weight, and thermal annealing are investigated extensively, resulting in trends mimicking experimental findings. The proposed methodology can help reduce the parameter space which has to be explored before obtaining optimal morphologies not only for BHJ solar cells but also for any other solution-processed soft matter device.

The computational and conceptual simplifications realized by coarse-grain (CG) models make them a ubiquitous tool in the current computational modeling landscape. Building block based CG models, such as the Martini model, possess the key advantage of allowing for a broad range of applications without the need to reparametrize the force field each time. However, there are certain inherent limitations to this approach, which we investigate in detail in this work. We first study the consequences of the absence of specific cross Lennard-Jones parameters between different particle sizes. We show that this lack may lead to artificially high free energy barriers in dimerization profiles. We then look at the effect of deviating too far from the standard bonded parameters, both in terms of solute partitioning behavior and solvent properties. Moreover, we show that too weak bonded force constants entail the risk of artificially inducing clustering, which has to be taken into account when designing elastic network models for proteins. These results have implications for the current use of the Martini CG model and provide clear directions for the reparametrization of the Martini model. Moreover, our findings are generally relevant for the parametrization of any other building block based force field.

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein-ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the wellcharacterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

The ‘phonon-glass electron-crystal’ concept has triggered most of the progress that has been achieved in inorganic thermoelectrics in the past two decades. Organic thermoelectric materials, unlike their inorganic counterparts, exhibit molecular diversity, flexible mechanical properties and easy fabrication, and are mostly ‘phonon glasses’. However, the thermoelectric performances of these organic materials are largely limited by low molecular order and they are therefore far from being ‘electron crystals’. Here, we report a molecularly n-doped fullerene derivative with meticulous design of the side chain that approaches an organic ‘PGEC’ thermoelectric material. This thermoelectric material exhibits an excellent electrical conductivity of >10 S cm−1 and an ultralow thermal conductivity of <0.1 Wm−1K−1, leading to the best figure of merit ZT = 0.34 (at 120 °C) among all reported single-host n-type organic thermoelectric materials. The key factor to achieving the record performance is to use ‘arm-shaped’ double-triethylene-glycol-type side chains, which not only offer excellent doping efficiency (~60%) but also induce a disorder-to-order transition upon thermal annealing. This study illustrates the vast potential of organic semiconductors as thermoelectric materials.

Improved miscibility of the blend could be obtained by controlling the structural similarity between the dopant and host materials, which accounts for the high doping efficiency and good thermoelectric performance.

The recent re‐parametrization of the Martini coarse‐grained force field, Martini 3, improved the accuracy of the model in predicting molecular packing and interactions in molecular dynamics simulations. Here, we describe how small molecules can be accurately parametrized within the Martini 3 framework and present a database of validated small molecule models. We pay particular attention to the description of aliphatic and aromatic ring‐like structures, which are ubiquitous in small molecules such as solvents and drugs or in building blocks constituting macromolecules such as proteins and synthetic polymers. In Martini 3, ring‐like structures are described by models that use higher resolution coarse‐grained particles (small and tiny particles). As such, the present database constitutes one of the cornerstones of the calibration of the new Martini 3 small and tiny particle sizes. The models show excellent partitioning behavior and solvent properties. Miscibility trends between different bulk phases are also captured, completing the set of thermodynamic properties considered during the parametrization. We also show how the new bead sizes allow for a good representation of molecular volume, which translates into better structural properties such as stacking distances. We further present design strategies to build Martini 3 models for small molecules of increased complexity.