A multiscale approach to uncover the self-assembly of ligand-covered palladium nanocubes.

Ligand-mediated superlattice assemblies of metallic nanocrystals represent a new type of mesoscale materials whose structural ordering directly influence emergent collective properties. However, universal control over the spatial and orientational ordering of their constitutive components remains an open challenge. One major barrier contributing to the lack of programmability in these nanoscale building blocks revolves around a gap in fundamental understanding of how ligand-mediated interactions at the particle level propagate to macroscopic and mesoscale behaviors. Here, we employ a combination of scaling theory and coarse-grained simulations to develop a multiscale modeling framework capable of bridging across hierarchical assembly length scales for a model system of ligand-functionalized nanocubes (here, Pd). We first employ atomistic simulations to characterize how specific ligand-ligand interactions influence the local behaviors between neighboring Pd nanocubes. We then utilize a mean-field scaling theory to both rationalize the observed behaviors as well as compute a coarse-grained effective pairwise potential between nanocubes capable of reproducing atomistic behaviors at the mesoscale. Furthermore, our simulations reveal that a complex interplay between ligand-ligand interactions is directly responsible for a shift in macroscopic ordering between neighboring nanocubes. Our results, therefore, provides a critical step forward in establishing a multiscale understanding of ligand-functionalized nanocrystalline assemblies that can be subsequently leveraged to design targeted structures exhibiting novel, emergent collective properties.

Blended Conjugated Host and Unconjugated Dopant Polymers Towards N-type All-Polymer Conductors and High-ZT Thermoelectrics.

N-Type thermoelectrics typically consist of small molecule dopant+polymer host. Only a few polymer dopant+polymer host systems have been reported, and these have lower thermoelectric parameters. N-type polymers with high crystallinity and order are generally used for high-conductivity ( σ ${\sigma }$ ) organic conductors. Few n-type polymers with only short-range lamellar stacking for high-conductivity materials have been reported. Here, we describe an n-type short-range lamellar-stacked all-polymer thermoelectric system with highest σ ${\sigma }$ of 78 S-1 , power factor (PF) of 163 µW m-1 K-2 , and maximum Figure of merit (ZT) of 0.53 at room temperature with a dopant/host ratio of 75 wt%. The minor effect of polymer dopant on the molecular arrangement of conjugated polymer PDPIN at high ratios, high doping capability, high Seebeck coefficient (S) absolute values relative to σ ${\sigma }$ , and atypical decreased thermal conductivity ( κ ${\kappa }$ ) with increased doping ratio contribute to the promising performance.

Ab Initiomodeling of Near-Edge EELS spectra for chemisorbed molecules.

Electron energy loss spectroscopy (EELS) has recently been applied to probe chemisorbed molecules on metal nanostructures, but a fundamental understanding of the correlation between these spectra and the electronic structures of the adsorbates has been limited. We report here on the insights afforded by time-dependent density functional theory to decipher the energy loss near edge structure (ELNES) of EELS spectra associated with chemisorption. These first-principles calculations simulate the ELNES-EELS spectra for chemisorbed CO on various facets of Au and Pt. Computational predictions of key signatures such as the 'red shift' and reductions in the peak intensity of the 2π* and 6σ* peaks, as compared to free CO in the gas phase, are validated in comparison to experimentally collected EELS spectra. These signatures are revealed to arise from changes in the electronic structure in terms of unoccupied density of states associated with the chemisorption process. They are consistent with a Blyholder model that incorporates donation and back-donation of electrons. They are also characteristic of the chemisorption process, such as the choice of metal, site of adsorption and the coverage and distribution of adsorbates. Our simulations thus provide guidelines for the use of ELNES-EELS to characterize the atomic structure and adsorption property of nanostructured surfaces and facilitate the development of advanced nanomaterials for catalytic applications.

3,4,5-Trimethoxy Substitution on an N-DMBI Dopant with New N-Type Polymers: Polymer-Dopant Matching for Improved Conductivity-Seebeck Coefficient Relationship.

Achieving high electrical conductivity and thermoelectric power factor simultaneously for n-type organic thermoelectrics is still challenging. By constructing two new acceptor-acceptor n-type conjugated polymers with different backbones and introducing the 3,4,5-trimethoxyphenyl group to form the new n-type dopant 1,3-dimethyl-2-(3,4,5-trimethoxyphenyl)-2,3-dihydro-1H-benzo[d]imidazole (TP-DMBI), high electrical conductivity of 11 S cm-1 and power factor of 32 µW m-1 K-2 are achieved. Calculations using Density Functional Theory show that TP-DMBI presents a higher singly occupied molecular orbital (SOMO) energy level of -1.94 eV than that of the common dopant 4-(1, 3-dimethyl-2, 3-dihydro-1H-benzoimidazol-2-yl) phenyl) dimethylamine (N-DMBI) (-2.36 eV), which can result in a larger offset between the SOMO of dopant and lowest unoccupied molecular orbital (LUMO) of n-type polymers, though that effect may not be dominant in the present work. The doped polymer films exhibit higher Seebeck coefficient and power factor than films using N-DMBI at the same doping levels or similar electrical conductivity levels. Moreover, TP-DMBI doped polymer films offer much higher electron mobility of up to 0.53 cm2 V-1 s-1 than films with N-DMBI doping, demonstrating the potential of TP-DMBI, and 3,4,5-trialkoxy DMBIs more broadly, for high performance n-type organic thermoelectrics.

Decomplexation as a rate limitation in the thiol-Michael addition of N-acrylamides.

The thiol-Michael addition is a popular, selective, high-yield "click" reaction utilized for applications ranging from small-molecule synthesis to polymer or surface modification. Here, we combined experimental and quantum mechanical modeling approaches using density functional theory (DFT) to examine the thiol-Michael reaction of N-allyl-N-acrylamide monomers used to prepare sequence-defined oligothioetheramides (oligoTEAs). Experimentally, the reaction was evaluated with two fluorous tagged thiols and several monomers at room temperature (22 °C and 40 °C). Using the Eyring equation, the activation energies (enthalpies) were calculated, observing a wide range of energy barriers ranging from 28 kJ mol-1 to 108 kJ mol-1 within the same alkene class. Computationally, DFT coupled with the Nudged Elastic Band method was used to calculate the entire reaction coordinate of each monomer reaction using the B97-D3 functional and a hybrid implicit-explicit methanol solvation approach. The thiol-Michael reaction is traditionally rate-limited by the propagation or chain-transfer steps. However, our test case with N-acrylamides and fluorous thiols revealed experimental and computational data produced satisfactory agreement only when we considered a previously unconsidered step that we termed "product decomplexation", which occurs as the product physically dissociates from other co-reactants after chain transfer. Five monomers were investigated to support this finding, capturing a range of functional groups varying in alkyl chain length (methyl to hexyl) and aromaticity (benzyl and ethylenephenyl). Increased substrate alkyl chain length increased activation energy, explained by the inductive effect. Aromatic ring-stacking configurations significantly impacted the activation energy and contributed to improved molecular packing density. Hydrogen-bonding between reactants increased the activation energy emphasizing the rate-limitation of the product decomplexation. Our findings begin to describe a new structure-kinetic relationship for thiol-Michael acceptors to enable further design of reactive monomers for synthetic polymers and biomaterials.

Ab Initio Studies of the Diffusion of Intrinsic Defects and Silicon Dopants in Bulk InAs.

We expose the predominant diffusional pathways for In and As in InAs, as well as dopant Si atoms in InAs, using Nudged Elastic Band calculations in conjunction with accurate Density Functional Theory calculations of the energy of defective systems. Our results show that As is a very fast diffuser compared to In and Si for both vacancy-assisted and interstitially mediated mechanisms. Larger indium atoms, on the other hand, are very slow diffusers and strongly prefer to remain on the In sublattice. Silicon also prefers to stay in substitutional sites in the In sublattice, in agreement with the fact that Si is used to create n-doped InAs. We find that the mechanism by which Si diffuses within the InAs lattice is very unlikely to proceed via vacancy-assisted jumps, since these routes encounter energy barriers above 2 eV. In contrast, silicon can readily make interstitial jumps since they occur with energy barriers as small as 0.23 eV. This suggests that an interstitial diffusion mechanism is strongly preferred for Si diffusion in InAs which challenges the common presumption made for another similar III-V compound, namely GaAs, that Si diffusion takes place via a vacancy-assisted mechanism.

Multiscale Simulation and Modeling of Multilayer Heteroepitactic Growth of C60 on Pentacene.

We apply multiscale methods to describe the strained growth of multiple layers of C60 on a thin film of pentacene. We study this growth in the presence of a monolayer pentacene step to compare our simulations to recent experimental studies by Breuer and Witte of submonolayer growth in the presence of monolayer steps. The molecular-level details of this organic semiconductor interface have ramifications on the macroscale structural and electronic behavior of this system and allow us to describe several unexplained experimental observations for this system. The growth of a C60 thin film on a pentacene surface is complicated by the differing crystal habits of the two component species, leading to heteroepitactical growth. In order to probe this growth, we use three computational methods that offer different approaches to coarse-graining the system and differing degrees of computational efficiency. We present a new, efficient reaction-diffusion continuum model for 2D systems whose results compare well with mesoscale kinetic Monte Carlo (KMC) results for submonolayer growth. KMC extends our ability to simulate multiple layers but requires a library of predefined rates for event transitions. Coarse-grained molecular dynamics (CGMD) circumvents KMC's need for predefined lattices, allowing defects and grain boundaries to provide a more realistic thin film morphology. For multilayer growth, in this particularly suitable candidate for coarse-graining, CGMD is a preferable approach to KMC. Combining the results from these three methods, we show that the lattice strain induced by heteroepitactical growth promotes 3D growth and the creation of defects in the first monolayer. The CGMD results are consistent with experimental results on the same system by Conrad et al. and by Breuer and Witte in which C60 aggregates change from a 2D structure at low temperature to 3D clusters along the pentacene step edges at higher temperatures.

A feasibility study of unconventional planar ligand spacers in chalcogenide nanocrystals.

The solar cell efficiency of chalcogenide nanocrystals (quantum dots) has been limited in the past by the insulation between neighboring quantum dots caused by intervening, often long-chain, aliphatic ligands. We have conducted a computationally based feasibility study to investigate the use of ultra-thin, planar, charge-conducting ligands as an alternative to traditional long passive ligands. Not only might these radically unconventional ligands decrease the mean distance between adjacent quantum dots, but, since they are charge-conducting, they have the potential to actively enhance charge migration. Our ab initio studies compare the binding energies, electronic energy gaps, and absorption characteristics for both conventional and unconventional ligands, such as phthalocyanines, porphyrins and coronene. This comparison identified these unconventional ligands with the exception of titanyl phthalocyanine, that bind to themselves more strongly than to the surface of the quantum dot, which is likely to be less desirable for enhancing charge transport. The distribution of finite energy levels of the bound system is sensitive to the ligand's binding site and the levels correspond to delocalized states. We also observed a trap state localized on a single Pb atom when a sulfur-containing phenyldithiocarbamate (PTC) ligand is attached to a slightly off-stoichiometric dot in a manner that the sulfur of the ligand completes stoichiometry of the bound system. Hence, this is indicative of the source of trap state when thio-based ligands are bound to chalcogenide nanocrystals. We also predict that titanyl phthalocyanine in a mix with chalcogenide dots of diameter ∼1.5 Šcan form a donor-acceptor system.

Stochastic fusion simulations and experiments suggest passive and active roles of hemagglutinin during membrane fusion.

Influenza enters the host cell cytoplasm by fusing the viral and host membrane together. Fusion is mediated by hemagglutinin (HA) trimers that undergo conformational change when acidified in the endosome. It is currently debated how many HA trimers, w, and how many conformationally changed HA trimers, q, are minimally required for fusion. Conclusions vary because there are three common approaches for determining w and q from fusion data. One approach correlates the fusion rate with the fraction of fusogenic HA trimers and leads to the conclusion that one HA trimer is required for fusion. A second approach correlates the fusion rate with the total concentration of fusogenic HA trimers and indicates that more than one HA trimer is required. A third approach applies statistical models to fusion rate data obtained at a single HA density to establish w or q and suggests that more than one HA trimer is required. In this work, all three approaches are investigated through stochastic fusion simulations and experiments to elucidate the roles of HA and its ability to bend the target membrane during fusion. We find that the apparent discrepancies among the results from the various approaches may be resolved if nonfusogenic HA participates in fusion through interactions with a fusogenic HA. Our results, based on H3 and H1 serotypes, suggest that three adjacent HA trimers and one conformationally changed HA trimer are minimally required to induce membrane fusion (w = 3 and q = 1).

Understanding polymorphism in organic semiconductor thin films through nanoconfinement.

Understanding crystal polymorphism is a long-standing challenge relevant to many fields, such as pharmaceuticals, organic semiconductors, pigments, food, and explosives. Controlling polymorphism of organic semiconductors (OSCs) in thin films is particularly important given that such films form the active layer in most organic electronics devices and that dramatic changes in the electronic properties can be induced even by small changes in the molecular packing. However, there are very few polymorphic OSCs for which the structure-property relationships have been elucidated so far. The major challenges lie in the transient nature of metastable forms and the preparation of phase-pure, highly crystalline thin films for resolving the crystal structures and evaluating the charge transport properties. Here we demonstrate that the nanoconfinement effect combined with the flow-enhanced crystal engineering technique is a powerful and likely material-agnostic method to identify existing polymorphs in OSC materials and to prepare the individual pure forms in thin films at ambient conditions. With this method we prepared high quality crystal polymorphs and resolved crystal structures of 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-pentacene), including a new polymorph discovered via in situ grazing incidence X-ray diffraction and confirmed by molecular mechanic simulations. We further correlated molecular packing with charge transport properties using quantum chemical calculations and charge carrier mobility measurements. In addition, we applied our methodology to a [1]benzothieno[3,2-b][1]1benzothiophene (BTBT) derivative and successfully stabilized its metastable form.

PAL 2.0: a physics-driven bayesian optimization framework for material discovery.

The lack of efficient discovery tools for advanced functional materials remains a major bottleneck to enabling advances in the next-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials (with respect to material compositions and processing conditions) that is typically redolent of such materials-centric applications. Searches of this large combinatorial space are often influenced by expert knowledge and clustered close to material configurations that are known to perform well, thus ignoring potentially high-performing candidates in unanticipated regions of the composition-space or processing protocol. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible material candidates can be prohibitively expensive, making exhaustive approaches to determine the best candidates infeasible. As a result, there remains a need for the development of computational algorithms that can efficiently search a large parameter space for a given material application. Here, we introduce PAL 2.0, a method that combines a physics-based surrogate model with Bayesian optimization. The key contributing factor of our proposed framework is the ability to create a physics-based hypothesis using XGBoost and Neural Networks. This hypothesis provides a physics-based "prior" (or initial beliefs) to a Gaussian process model, which is then used to perform a search of the material design space. In this paper, we demonstrate the usefulness of our approach on three material test cases: (1) discovery of metal halide perovskites with desired photovoltaic properties, (2) design of metal halide perovskite-solvent pairs that produce the best solution-processed films and (3) design of organic thermoelectric semiconductors. Our results indicate that the novel PAL 2.0 approach outperforms other state-of-the-art methods in its efficiency to search the material design space for the optimal candidate. We also demonstrate the physics-based surrogate models constructed in PAL 2.0 have lower prediction errors for material compositions not seen by the model. To the best of our knowledge, there is no competing algorithm capable of this useful combination for materials discovery, especially those for which data are scarce.

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning.

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.

Effects of odd-even side chain length of alkyl-substituted diphenylbithiophenes on first monolayer thin film packing structure.

Because of their preferential two-dimensional layer-by-layer growth in thin films, 5,5'bis(4-alkylphenyl)-2,2'-bithiophenes (P2TPs) are model compounds for studying the effects of systematic chemical structure variations on thin-film structure and morphology, which in turn, impact the charge transport in organic field-effect transistors. For the first time, we observed, by grazing incidence X-ray diffraction (GIXD), a strong change in molecular tilt angle in a monolayer of P2TP, depending on whether the alkyl chain on the P2TP molecules was of odd or even length. The monolayers were deposited on densely packed ultrasmooth self-assembled alkane silane modified SiO2 surfaces. Our work shows that a subtle change in molecular structure can have a significant impact on the molecular packing structure in thin film, which in turn, will have a strong impact on charge transport of organic semiconductors. This was verified by quantum-chemical calculations that predict a corresponding odd-even effect in the strength of the intermolecular electronic coupling.

Solvent-driven symmetry of self-assembled nanocrystal superlattices--a computational study.

The preference of experimentally realistic sized 4-nm facetted nanocrystals (NCs), emulating Pb chalcogenide quantum dots, to spontaneously choose a crystal habit for NC superlattices (Face Centered Cubic (FCC) vs. Body Centered Cubic (BCC)) is investigated using molecular simulation approaches. Molecular dynamics simulations, using united atom force fields, are conducted to simulate systems comprised of cube-octahedral-shaped NCs covered by alkyl ligands, in the absence and presence of experimentally used solvents, toluene and hexane. System sizes in the 400,000-500,000-atom scale followed for nanoseconds are required for this computationally intensive study. The key questions addressed here concern the thermodynamic stability of the superlattice and its preference of symmetry, as we vary the ligand length of the chains, from 9 to 24 -CH(2) groups, and the choice of solvent. We find that hexane and toluene are "good" solvents for the NCs, which penetrate the ligand corona all the way to the NC surfaces. We determine the free energy difference between FCC and BCC NC superlattice symmetries to determine the system's preference for either geometry, as the ratio of the length of the ligand to the diameter of the NC is varied. We explain these preferences in terms of different mechanisms in play, whose relative strength determines the overall choice of geometry.

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning.

Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid-liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

Machine Learning Approach to Enable Spectral Imaging Analysis for Particularly Complex Nanomaterial Systems.

Scanning transmission electron microscopy-based electron energy loss spectroscopy spectral imaging (STEM-EELS-SI) has been widely used in material research to capture a wealth of information, including elemental, electron density, and bonding state distributions. However, its exploitation still faces many challenges due to the difficulty of extracting information from noisy and overlapping edges in the convoluted spatial and spectroscopic data set. A traditional EELS spectral imaging analysis lacks the capability to isolate noise and deconvolute such overlapping edges, which either limits the resolution or the signal-to-noise ratio of the maps generated by EELS-SI. Existing machine learning (ML) algorithms can achieve denoising and deconvolution to a certain extent, but the extracted spectra lack physical meaning. To address these challenges, we have developed a ML method tailored to a spectral imaging analysis system and based on a non-negative robust principal component analysis. This approach offers an effective way to analyze EELS spectral images with improved space-time resolution, signal-to-noise ratio, and the capability to separate subtle differences in the spectrum. We apply this algorithm to 13 nanomaterial systems to show that ML can greatly improve image quality compared to a traditional approach, especially for more challenging systems. This will expand the type of nanomaterial systems that can be characterized by EELS-SI, and aid the analysis of structural, chemical, and electronic properties that are otherwise difficult to obtain.

Explicit all-atom modeling of realistically sized ligand-capped nanocrystals.

We present a study of an explicit all-atom representation of nanocrystals of experimentally relevant sizes (up to 6 nm), "capped" with alkyl chain ligands, in vacuum. We employ all-atom molecular dynamics simulation methods in concert with a well-tested intermolecular potential model, MM3 (molecular mechanics 3), for the studies presented here. These studies include determining the preferred conformation of an isolated single nanocrystal (NC), pairs of isolated NCs, and (presaging studies of superlattice arrays) unit cells of NC superlattices. We observe that very small NCs (3 nm) behave differently in a superlattice as compared to larger NCs (6 nm and above) due to the conformations adopted by the capping ligands on the NC surface. Short ligands adopt a uniform distribution of orientational preferences, including some that lie against the face of the nanocrystal. In contrast, longer ligands prefer to interdigitate. We also study the effect of changing ligand length and ligand coverage on the NCs on the preferred ligand configurations. Since explicit all-atom modeling constrains the maximum system size that can be studied, we discuss issues related to coarse-graining the representation of the ligands, including a comparison of two commonly used coarse-grained models. We find that care has to be exercised in the choice of coarse-grained model. The data provided by these realistically sized ligand-capped NCs, determined using explicit all-atom models, should serve as a reference standard for future models of coarse-graining ligands using united atom models, especially for self-assembly processes.

Protocol for Directing Nudged Elastic Band Calculations to the Minimum Energy Pathway: Nurturing Errant Calculations Back to Convergence.

The combination of density functional theory (DFT) and the nudged elastic band (NEB) method offers a practical tool for the discovery of underlying reaction mechanisms related to the synthesis of functional materials. However, in practice, the lack of a standardized protocol for minimum energy pathway determination too often leads to an inefficient and computationally intensive design process. To that end, we define a verifiable DFT+NEB protocol for efficiently locating and confirming the transition state of a reaction. To test this assertion, we curate 226 unique reactions within 14 classes of reactions and investigate their performance in terms of the number of NEB iterations they require to locate the transition state and an estimate of the associated mean absolute error. Leveraging this protocol, we demonstrate its application for an initial set of parameters: number of frames, Nframes = 11; maximum step size, Smax = 0.04 Å; optimizer = LBFGS; and spring constant, kspr = 0.1 eV/Å2. We report a convergence rate of 73% and find that a root-mean-square force (FRMS) of 0.01 eV/Å provides a "rule of thumb" below which NEB simulations are likely to converge. Venturing beyond this baseline enquiry, we delineate the effect on performance of altering the number of frames, maximum step size, choice of optimizer and spring constant. We find improvements in performance with increasing Nframes and Smax, ostensibly approaching some asymptotic limit. We also see substantial improvement in efficiency with the LBFGS optimizer and a clear minimum in performance for the spring constant value of 0.1 eV/Å2. Finally, we provide five case studies that demonstrate typical convergence issues for NEB simulations and suggest methods to overcome them. Our results provide specific and transferable recommendations, offering a transparent and practical tool for beginner and expert researchers alike toward a more rational NEB simulation design.

A comprehensive picture of roughness evolution in organic crystalline growth: the role of molecular aspect ratio.

Exploiting the capabilities of organic semiconductors for applications ranging from light-emitting diodes to photovoltaics to lasers relies on the creation of ordered, smooth layers for optimal charge carrier mobilities and exciton diffusion. This, in turn, creates a demand for organic small molecules that can form smooth thin film crystals via homoepitaxy. We have studied a set of small-molecule organic semiconductors that serve as templates for homoepitaxy. The surface roughness of these materials is measured as a function of adlayer film thickness from which the growth exponent (ß) is extracted. Notably, we find that three-dimensional molecules that have low molecular aspect ratios (AR) tend to remain smooth as thickness increases (small ß). This is in contrast to planar or rod-like molecules with high AR that quickly roughen (large ß). Molecular dynamics simulations find that the Ehrlich-Schwöbel barrier (EES) alone is unable to fully explain this trend. We further investigated the mobility of ad-molecules on the crystalline surface to categorize their diffusion behaviors and the effects of aggregation to account for the different degrees of roughness that we observed. Our results suggest that low AR molecules have low molecular mobility and moderate EES which creates a downward funneling effect leading to smooth crystal growth.

Untying the Cesium "Not": Cesium-Iodoplumbate Complexation in Perovskite Solution-Processing Inks Has Implications for Crystallization.

We illustrate the critical importance of the energetics of cation-solvent versus cation-iodoplumbate interactions in determining the stability of ABX3 perovskite precursors in a dimethylformamide (DMF) solvent medium. We have shown, through a complementary suite of nuclear magnetic resonance (NMR) and computational studies, that Cs+ exhibits significantly different solvent vs iodoplumbate interactions compared to organic A+-site cations such as CH3NH3+ (MA+). Two NMR studies were conducted: 133Cs NMR analysis shows that Cs+ and MA+ compete for coordination with PbI3- in DMF. 207Pb NMR studies of PbI2 with cationic iodides show that perovskite-forming Cs+ (and, somewhat, Rb+) do not comport with the 207Pb chemical shift trend found for Li+, Na+, and K+. Three independent computational approaches (density functional theory (DFT), ab initio Molecular Dynamics (AIMD), and a polarizable force field within Molecular Dynamics) yielded strikingly similar results: Cs+ interacts more strongly with the PbI3- iodoplumbate than does MA+ in a polar solvent environment like DMF. The stronger energy preference for PbI3- coordination of Cs+ vs MA+ in DMF demonstrates that Cs+ is not simply a postcrystallization cation "fit" for the perovskite A+-site. Instead, it may facilitate preorganization of the framework precursor that eventually transforms into the crystalline perovskite structure.