Clustering feature of metal atoms in pentacene molecular solids: first-principles study

Metal-atom contamination often induces the degradation of organic molecular devices. In this work, we studied clustering feature of Au and Al impurity metal atoms in pentacene solids by the first-principles calculations. We found that Au atoms prefer to produce clusters in a molecule-edge space due to the strong bonding among Au atoms, and such clusters can increase their sizes by producing molecule vacancies. On the other hand, Al atom prefers to locate separately around the center of pentacene molecules due to the strong bonding between Al atom and surrounding molecules, which produces the scattering distribution of Al atoms in pentacene solids.

Modelling the Melting of Gallium Clusters: A Path to Understanding Molecular Solids

<p>Gallium is a molecular solid with many unique properties. Comprised of Ga2 dimers but exhibiting metal-like electronic characteristics, gallium may be deemed a molecular metal. The role of this dual covalent-metallic nature may explain gallium’s fascinating thermodynamic behaviour. While bulk gallium melts at 303 K, clusters with only 10’s of atoms melt at temperatures between 500 and 800 K, according to experiment. The measured specific heat curves exhibit a strong size-sensitivity, where the addition of a single atom can alter the melting temperature by up to 100 K. This research addresses the relationship of electronic structure to the melting behaviour in small gallium clusters through a parallel tempering implementation of first-principles molecular dynamics simulations. These simulations cover 11 cluster sizes and two charge states, including neutral clusters sized 7-12 atoms and cationic clusters sized 32-35 atoms. The modelling of small clusters sets a lower size limit for melting and illustrates that greater-than-bulk melting is not universal for small gallium clusters. The larger cluster sizes allow for a direct comparison to experimental data. Each simulation reveals that the clusters have a non-covalent nature more similar to the metallic surface structure of bulk gallium than its covalently bonded interior. The dramatic lowering of melting temperatures and cluster stabilities with single atom additions supports the conclusion that the difference in the nature of bonding between bulk and clusters accounts for the melting temperature discrepancy. Finally, in order to gain additional insight into the nature of bonding in molecular solids, the cohesive energies of the solid halogens are calculated by the method of increments. These calculations investigate the relative N-body correlation energy contributions to the total cohesive energy for solid Cl2, Br2 and I2.</p>

Modelling the Melting of Gallium Clusters: A Path to Understanding Molecular Solids

<p>Gallium is a molecular solid with many unique properties. Comprised of Ga2 dimers but exhibiting metal-like electronic characteristics, gallium may be deemed a molecular metal. The role of this dual covalent-metallic nature may explain gallium’s fascinating thermodynamic behaviour. While bulk gallium melts at 303 K, clusters with only 10’s of atoms melt at temperatures between 500 and 800 K, according to experiment. The measured specific heat curves exhibit a strong size-sensitivity, where the addition of a single atom can alter the melting temperature by up to 100 K. This research addresses the relationship of electronic structure to the melting behaviour in small gallium clusters through a parallel tempering implementation of first-principles molecular dynamics simulations. These simulations cover 11 cluster sizes and two charge states, including neutral clusters sized 7-12 atoms and cationic clusters sized 32-35 atoms. The modelling of small clusters sets a lower size limit for melting and illustrates that greater-than-bulk melting is not universal for small gallium clusters. The larger cluster sizes allow for a direct comparison to experimental data. Each simulation reveals that the clusters have a non-covalent nature more similar to the metallic surface structure of bulk gallium than its covalently bonded interior. The dramatic lowering of melting temperatures and cluster stabilities with single atom additions supports the conclusion that the difference in the nature of bonding between bulk and clusters accounts for the melting temperature discrepancy. Finally, in order to gain additional insight into the nature of bonding in molecular solids, the cohesive energies of the solid halogens are calculated by the method of increments. These calculations investigate the relative N-body correlation energy contributions to the total cohesive energy for solid Cl2, Br2 and I2.</p>

Glass transition temperature prediction of disordered molecular solids

Glass transition temperature, Tg, is the key quantity for assessing morphological stability and molecular ordering of films of organic semiconductors. A reliable prediction of Tg from the chemical structure is, however, challenging, as it is sensitive to both molecular interactions and analysis of the heating or cooling process. By combining a fitting protocol with an automated workflow for forcefield parameterization, we predict Tg with a mean absolute error of ~20 °C for a set of organic compounds with Tg in the 50–230 °C range. Our study establishes a reliable and automated prescreening procedure for the design of amorphous organic semiconductors, essential for the optimization and development of organic light-emitting diodes.

Fast and Accurate Electric Field Gradient Calculations in Molecular Solids With Density Functional Theory

Modern approaches for calculating electric field gradient (EFF) tensors in molecular solids rely upon plane-wave calculations employing periodic boundary conditions (PBC). In practice, models employing PBCs are limited to generalized gradient approximation (GGA) density functionals. Hybrid density functionals applied in the context of gauge-including atomic orbital (GIAO) calculations have been shown to substantially improve the accuracy of predicted NMR parameters. Here we propose an efficient method that effectively combines the benefits of both periodic calculations and single-molecule techniques for predicting electric field gradient tensors in molecular solids. Periodic calculations using plane-wave basis sets were used to model the crystalline environment. We then introduce a molecular correction to the periodic result obtained from a single-molecule calculation performed with a hybrid density functional. Single-molecule calculations performed using hybrid density functionals were found to significantly improve the agreement of predicted 17O quadrupolar coupling constants (Cq) with experiment. We demonstrate a 31% reduction in the RMS error for the predicted 17O Cq values relative to standard plane-wave methods using a carefully constructed test set comprised of 22 oxygen-containing molecular crystals. We show comparable improvements in accuracy using five different hybrid density functionals and find predicted Cq values to be relatively insensitive to the choice of basis set used in the single molecule calculation. Finally, the utility of high-accuracy 17O Cq predictions is demonstrated by examining the disordered 4-Nitrobenzaldehyde crystal structure.

A study of real-world micrograph data quality and machine learning model robustness

Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.