Modeling the Self-Assembly of Nano Objects: Applications to Supramolecular Organic Monolayers Adsorbed on Metal Surfaces

Physical Prior Mean Function-Driven Gaussian Processes Search for Minimum-Energy Reaction Paths with a Climbing-Image Nudged Elastic Band: A General Method for Gas-Phase, Interfacial, and Bulk-Phase Reactions

The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by delineating both transition states (TSs) and intermediate nonstationary structures along reaction coordinates. However, executing CI-NEB calculations for reactions with extensive reaction coordinate spans necessitates a large number of images to ensure a reliable convergence of the MEPs and TS structures, presenting a computationally demanding optimization challenge, even with mildly costly electronic-structure methods. In this study, we advocate for the utilization of physically inspired prior mean function-based Gaussian processes (GPs) to expedite MEP exploration and TS optimization via the CI-NEB method. By incorporating reliable prior physical approximations into potential energy surface (PES) modeling, we demonstrate enhanced efficiency in multidimensional CI-NEB optimization with surrogate-based optimizers. Our physically informed GP approach not only outperforms traditional nonsurrogate-based optimizers in optimization efficiency but also on-the-fly learns the reaction path valley during optimization, culminating in significant advancements. The surrogate PES derived from our optimization exhibits high accuracy compared to true PES references, aligning with our emphasis on leveraging reliable physical priors for robust and efficient posterior mean learning in GPs. Through a systematic benchmark study encompassing various reaction pathways, including gas-phase, bulk-phase, and interfacial/surface reactions, our physical GPs consistently demonstrate superior efficiency and reliability. For instance, they outperform the popular fast inertial relaxation engine optimizer by approximately a factor of 10, showcasing their versatility and efficacy in exploring reaction mechanisms and surface reaction PESs.

Equilibrium structure of a dense trimesic acid monolayer on a homogeneous solid surface: from atomistic simulation to thermodynamics

A general methodology for determining the thermodynamic characteristics of rigid organic crystals on the atomistic level is presented. The proposed approach is based on a combination of grid interpolation of the precalculated intermolecular potential and kinetic Monte Carlo simulation of the gas-crystal system with an explicit interphase. The two-phase system is stabilized in a wide range of external parameters with an imposed external potential and damping field. The damping field reduces the intermolecular potential at the edges of the crystals and turns it off in the gas phase. To determine the thermodynamic characteristics of a crystal the conditions of equality of chemical potentials in coexisting phases are used. The intermolecular pairwise potential can be calculated on the atomistic or quantum level. In the kinetic Monte Carlo simulations, a grid interpolation of the precalculated potential is performed on each iteration of the algorithm. We have applied the approach to the thermodynamic analysis of a dense monolayer of trimesic acid on a homogeneous surface. The calculated free energy and entropy for the dense "superflower" and filled chicken-wire phases obey the Gibbs-Duhem equation, which confirms the thermodynamic consistency of our approach. Using the proposed approach, we have revealed that the dense "superflower" phase becomes metastable at zero pressure and 470-500 K. Under these conditions, the filled chicken-wire structure with partially released hexagonal cages is thermodynamically favourable. The proposed approach is a potentially universal tool for the thermodynamic analysis of crystals formed by "rigid" organic molecules of any complexity on the atomistic level.

Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation

Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks (MOFs) as adsorbents for radon separation from indoor air. MOFs from a specific database were initially screened using a pore-limiting diameter filter. Subsequently, random forest classification and grand canonical Monte Carlo simulations were implemented to identify MOFs with a high adsorbent performance score (APS) and high regenerability (R %). Interpretability and trustworthiness were determined by variable importance analysis , and adsorption mechanisms were elucidated by calculating the adsorption sites using Materials Studio. Notably, two MOF candidates were discovered with higher APS values in both the radon/N₂ and radon/O₂ systems compared with that of ZrSQU which is the best-performing MOF thus far, with R % values exceeding 85%. Furthermore, the proposed framework can be flexibly applied to multiple data sets due to good performance in model transfer. Therefore, the proposed framework has the potential to provide guidelines for the strategic design of MOFs for radon separation.

On-surface self-assembly of tetratopic molecular building blocks

Self-assembly of functional molecules on solid substrates has recently attracted special attention as a versatile method for the fabrication of low dimensional nanostructures with tailorable properties. In this contribution, using theoretical modeling, we demonstrate how the architecture of 2D molecular assemblies can be predicted based on the individual properties of elementary building blocks at play. To that end a model star-shaped tetratopic molecule is used and its self-assembly on a (111) surface is simulated using the lattice Monte Carlo method. Several test cases are studied in which the molecule bears terminal arm centers providing interactions with differently encoded directionality. Our theoretical results show that manipulation of the interaction directions can be an effective way to direct the self-assembly towards extended periodic superstructures (2D crystals) as well as to create assemblies characterized by a lower degree of order, including glassy overlayers and quasi one-dimensional molecular connections. The obtained structures are described and classified with respect to their main geometric parameters. A small library of the tetratopic molecules and the corresponding superstructures is provided to categorize the structure-property relationship in the modeled systems. The results of our simulations can be helpful to 2D crystal engineering and surface-confined polymerization techniques as they give hints on how to functionalize tetrapod organic building blocks which would be able to create superstructures with predefined spatial organization and range of order.

Cross-impact of surface and interaction anisotropy in the self-assembly of organic adsorption monolayers: a Monte Carlo and transfer-matrix study

We have theoretically studied the features of self-assembly in organic adsorption layers where both “molecule–surface” and “molecule–molecule” interactions are anisotropic.

Phase behaviour of self-assembled monolayers controlled by tuning physisorbed and chemisorbed states: A lattice-model view

The self-assembly of molecules on surfaces into 2D structures is important for the bottom-up fabrication of functional nanomaterials, and the self-assembled structure depends on the interplay between molecule-molecule interactions and molecule-surface interactions. Halogenated benzene derivatives on platinum have been shown to have two distinct adsorption states: a physisorbed state and a chemisorbed state, and the interplay between the two can be expected to have a profound effect on the self-assembly and phase behaviour of these systems. We developed a lattice model that explicitly includes both adsorption states, with representative interactions parameterised using density functional theory calculations. This model was used in Monte Carlo simulations to investigate pattern formation of hexahalogenated benzene molecules on the platinum surface. Molecules that prefer the physisorbed state were found to self-assemble with ease, depending on the interactions between physisorbed molecules. In contrast, molecules that preferentially chemisorb tend to get arrested in disordered phases. However, changing the interactions between chemisorbed and physisorbed molecules affects the phase behaviour. We propose functionalising molecules in order to tune their adsorption states, as an innovative way to control monolayer structure, leading to a promising avenue for directed assembly of novel 2D structures.

Observation and modeling of conformational molecular structures driving the self-assembly of tri-adamantyl benzene on Ag(111)

A STM image of the hexagonal network of tri-adamantyl benzene molecules on Ag(111).

Theoretical Modeling of Surface Confined Chiral Nanoporous Networks: Cruciform Molecules as Versatile Building Blocks

Patterning of solid surfaces with functional organic molecules has been a convenient route to fabricate two-dimensional materials with programmed architecture and activities. One example is the chiral nanoporous networks that can be created via controlled self-assembly of star-shaped molecules under 2D confinement. In this contribution we use computer modeling to predict the formation of molecular networks in adsorbed overlayers comprising cruciform molecular building blocks equipped with discrete interaction centers. To that end, we employ the Monte Carlo simulation method combined with a coarse-grained representation of the adsorbed molecules which are treated as collections of interconnected segments. The interaction centers within the molecules are represented by active segments whose number and distribution are adjusted. Our particular focus is on those distributions that produce prochiral molecules able to occur in adsorbed configurations being mirror images of each other (surface enantiomers). We demonstrate that, depending on size, aspect ratio, and intramolecular distribution of active sites, the surface enantiomers can co-crystallize or segregate into extended homochiral domains with largely diversified nanosized cavities. The insights from our theoretical studies can be helpful in designing 2D chiral porous networks with potential applications in enantioselective adsorption and asymmetric heterogeneous catalysis.

Predicting supramolecular self-assembly on reconstructed metal surfaces

The prediction of supramolecular self-assembly onto solid surfaces is still challenging in many situations of interest for nanoscience. In particular, no previous simulation approach has been capable to simulate large self-assembly patterns of organic molecules over reconstructed surfaces (which have periodicities over large distances) due to the large number of surface atoms and adsorbing molecules involved. Using a novel simulation technique, we report here large scale simulations of the self-assembly patterns of an organic molecule (DIP) over different reconstructions of the Au(111) surface. We show that on particular reconstructions, the molecule-molecule interactions are enhanced in a way that long-range order is promoted. Also, the presence of a distortion in a reconstructed surface pattern not only induces the presence of long-range order but also is able to drive the organization of DIP into two coexisting homochiral domains, in quantitative agreement with STM experiments. On the other hand, only short range order is obtained in other reconstructions of the Au(111) surface. The simulation strategy opens interesting perspectives to tune the supramolecular structure by simulation design and surface engineering if choosing the right molecular building blocks and stabilising the chosen reconstruction pattern.