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

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

Effects of Land-Surface-Vegetation on theboreal summer surface climate of a GCM

A land surface model (LSM) has been included in the ECMWF Hamburg version 4 (ECHAM4) atmospheric general circulation model (AGCM). The LSM is an early version of the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and it replaces the simple land surface scheme previously included in ECHAM4. The purpose of this paper is to document how a more exhaustive consideration of the land surface–vegetation processes affects the simulated boreal summer surface climate. To investigate the impacts on the simulated climate, different sets of Atmospheric Model Intercomparison Project (AMIP)-type simulations have been performed with ECHAM4 alone and with the AGCM coupled with ORCHIDEE. Furthermore, to assess the effects of the increase in horizontal resolution the coupling of ECHAM4 with the LSM has been implemented at different horizontal resolutions. The analysis reveals that the LSM has large effects on the simulated boreal summer surface climate of the atmospheric model. Considerable impacts are found in the surface energy balance due to changes in the surface latent heat fluxes over tropical and midlatitude areas covered with vegetation. Rainfall and atmospheric circulation are substantially affected by these changes. In particular, increased precipitation is found over evergreen and summergreen vegetated areas. Because of the socioeconomical relevance, particular attention has been devoted to the Indian summer monsoon (ISM) region. The results of this study indicate that precipitation over the Indian subcontinent is better simulated with the coupled ECHAM4–ORCHIDEE model compared to the atmospheric model alone

Effects of Land-Surface-Vegetation on the boreal summer surface climate of a GCM

A Land Surface Model (LSM) has been included in the ECHAM4 Atmospheric General Circulation Model (AGCM). The LSM is an early version of ORCHIDEE (Organizing Carbon and Hydrology In Dynamic EcosystEms) and it replaces the simple land surface scheme previously included in ECHAM4. The purpose of this paper is to document how a more exhaustive consideration of the land-surface-vegetation processes affects the simulated boreal summer surface climate. In order to investigate the impacts on the simulated climate, different sets of AMIP-type simulations have been performed with Echam4 alone and with the AGCM coupled with ORCHIDEE. Furthermore, to assess the effects of the increase in horizontal resolution the coupling of Echam4 with the LSM has been implemented at different horizontal resolutions. The analysis reveals that the LSM has large effects on the simulated boreal summer surface climate of the atmospheric model. Considerable impacts are found in the surface energy balance due to changes in the surface latent heat fluxes over tropical and mid-latitude areas covered with vegetation. Rainfall and atmospheric circulation are substantially affected by these changes. In particular, increased precipitation is found over evergreen and summergreen vegetated areas. Due to the socio-economical relevance, particular attention has been devoted to the Indian Summer Monsoon (ISM) region. Our results indicate that precipitation over the Indian subcontinent is better simulated with the coupled Echam4-ORCHIDEE model compared to the atmospheric model alone

Comparing Dimerization Free Energies and Binding Modes of Small Aromatic Molecules with Different Force Fields

Dimerization free energies are fundamental quantities that describe the strength of interaction of different molecules. Obtaining accurate experimental values for small molecules and disentangling the conformations that contribute most to the binding can be extremely difficult, due to the size of the systems and the small energy differences. In many cases, one has to resort to computational methods to calculate such properties. In this work, we used molecular dynamics simulations in conjunction with metadynamics to calculate the free energy of dimerization of small aromatic rings, and compared three models from popular online servers for atomistic force fields, namely G54a7, CHARMM36 and OPLS. We show that, regardless of the force field, the profiles for the dimerization free energy of these compounds are very similar. However, significant care needs to be taken when studying larger molecules, since the deviations from the trends increase with the size of the molecules, resulting in force field dependent preferred stacking modes; for example, in the cases of pyrene and tetracene. Our results provide a useful background study for using topology builders to model systems which rely on stacking of aromatic moieties, and are relevant in areas ranging from drug design to supramolecular assembly

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

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 to reach relevant length scales, retain chemical specificity, mimic 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 for any other solution-processed soft matter device.</p

Polyply:A python suite for facilitating simulations of 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 provides 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 realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle

Protohistoric briquetage at Puntone (Tuscany, Italy):principles and processes of an industry based on the leaching of saline lagoonal sediments

A protohistoric (c.10th-5th c. BC) briquetage site at Puntone (Tuscany, Italy) was studied to unravel the salt production processes and materials involved. Geophysical surveys were used to identify kilns, pits, and dumps. One of these pits and a dump were excavated, followed by detailed chemical and physical analyses of the materials encountered. The pit had been used for holding brine, obtained by leaching of lagoonal sediment over a sieve, that afterwards was discarded to form large dumps. Phases distinguished indicate that the pit filled with fine sediment and was regularly "cleaned." The presence of ferroan-magnesian calcite in the pit fill testifies to the prolonged presence of anoxic brine. The production processes could be reconstructed in detail by confronting the analytical results with known changes in composition of a brine upon evaporation. These pertain in particular to the accumulation of "bitterns" and increased B (boron) concentrations in a residual brine. Both could be traced in the materials studied, and were found to be far more indicative than the ubiquitously studied concentrations of Cl and Na.</p

ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions: Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4–6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data