Substituent effects on packing entropy and film morphologies in the nucleation of functionalized pentacenes on SiO2 substrate: Molecular dynamics simulations

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

Pentacene Crystal Growth on Silica and Layer-Dependent Step-Edge Barrier from Atomistic Simulations

Understanding and controlling the growth of organic crystals deposited from the vapor phase is important for fundamental materials science and necessary for applications in pharmaceutical and organic electronics industries. Here, this process is studied for the paradigmatic case of pentacene on silica by means of a specifically tailored computational approach inspired by the experimental vapor deposition process. This scheme is able to reproduce the early stages of the thin-film formation, characterized by a quasi layer-by-layer growth, thus showcasing its potential as a tool complementary to experimental techniques for investigating organic crystals. Crystalline islands of standing molecules are formed at a critical coverage, as a result of a collective reorientation of disordered aggregates of flat-lying molecules. The growth then proceeds by sequential attachment of molecules at the cluster and then terrace edges. Free-energy calculations allowed us to characterize the step-edge barrier for descending the terraces, a fundamental parameter for growth models for which only indirect experimental measurements are available. The barrier is found to be layer-dependent (approximately 1 kcal/mol for the first monolayer on silica, 2 kcal/mol for the second monolayer) and to extend over a distance comparable with the molecular length.

Charge-Storage Aromatic Amino Compounds for Nonvolatile Organic Transistor Memory Devices

Here, charge-storage nonvolatile organic field-effect transistor (OFET) memory devices based on interfacial self-assembled molecules are proposed. The functional molecules contain various aromatic amino moieties (N-phenyl-N-pyridyl amino- (PyPN), N-phenyl amino- (PN), and N,N-diphenyl amino- (DPN)) which are linked by a propyl chain to a triethoxysilyl anchor group and act as the interface modifiers and the charge-storage elements. The PyPN-containing pentacene-based memory device (denoted as PyPN device) presents the memory window of 48.43 V, while PN and DPN devices show the memory windows of 24.88 and 8.34 V, respectively. The memory characteristic of the PyPN device can remain stable along with 150 continuous write-read-erase-read cycles. The morphology analysis confirms that three interfacial layers show aggregation due to the N atomic self-catalysis and hydrogen bonding effects. The large aggregate-covered PyPN layer has the full contact area with the pentacene molecules, leading to the high memory performance. In addition, the energy level matching between PyPN molecules and pentacene creates the smallest tunneling barrier and facilitates the injection of the hole carriers from pentacene to the PyPN layer. The experimental memory characteristics are well in agreement with the computational calculation.

Molecular Dynamics Simulations and their Application to Thin-film Devices

The performance of devices based on organic semiconductors strongly depends on the molecular organisation in thin films. Due to the intrinsic complexity of these systems, a combination of theoretical modelling and experimental techniques is often the key to achieve a full understanding of their inner working. Here, we introduce the modelling of organic semiconductors by means of molecular dynamics simulations. We describe the basic theoretical framework of the technique and review the most popular class of force fields used to model organic materials, paying particular attention to the peculiarities of confined systems like nano-thick films. Representative studies of the organisation of organic functional materials in thin film phases are also reviewed.

Simulations of molecular self-assembled monolayers on surfaces: packing structures, formation processes and functions tuned by intermolecular and interfacial interactions

Simulations using QM and MM methods guide the rational design of functionalized SAMs on surfaces.

Effects of different self-assembled monolayers on thin-film morphology: a combined DFT/MD simulation protocol

Organic thin film transistors (OTFTs) are multilayer field-effect transistors that employ an organic conjugated material as semiconductor. Several experimental groups have recently demonstrated that the insertion of an organic self-assembled monolayer (SAM) between the dielectric and the semiconductive layer is responsible for a sensible improvement of the OTFT performances in terms of an increased charge carrier mobility caused by a higher degree of order in the organic semiconductor layer. Here, we describe a combined periodic density functional theory (DFT) and classical molecular dynamics (MD) protocol applied to four different SAMs and a pentacene monolayer deposited onto their surfaces. In particular, we investigate the morphology and the surface of the four SAMs and the translational, orientational, and nematic order of the monolayer through the calculation of several distribution functions and order parameters pointing out the differences among the systems and relating them to known experimental results. Our calculations also suggest that small differences in the SAM molecular design will produce remarkable differences in the SAM surface and monolayer order. In particular, our simulations explain how a SAM with a bulky terminal group results in an irregular and rough surface that determines the deposition of a disordered semiconductive monolayer. On the contrary, SAMs with a small terminal group generate smooth surfaces with uninterrupted periodicity, thus favoring the formation of an ordered pentacene monolayer that increases the mobility of charge carriers and improves the overall performances of the OTFT devices. Our results clearly point out that the in silico procedure presented here might be of help in tuning the design of SAMs in order to improve the quality of OTFT devices.

Computational investigation of hole mobilities in organic semiconductors: comparison of single crystal structures and surface adsorbed clusters

We report hole mobilities obtained computationally based on both single crystal geometries and those obtained from crystal fragments optimised on a model surface. Such computational estimates can differ considerably from experimentally measured thin film mobilities. One source of this discrepancy is due to a difference in the morphology of the thin film compared with that of the crystal. Here, predictions of thin film hole mobilities based on optimised structures are given. A model surface is used to provide an inert geometric platform for the formation of an organic monolayer. The model is tested on pentacene and TIPS-pentacene for which experimental information of the surface morphology exists. The model has also been applied to four previously uninvestigated structures. Two of the compounds studied had fairly low predicted mobilities in their single crystal structures, which were vastly improved post-optimisation. This is in accord with experiment.