Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models.

The development of scanning probe microscopy (SPM) has enabled unprecedented scientific discoveries through high-resolution imaging. Simulations and theoretical analysis of SPM images are equally important as obtaining experimental images since their comparisons provide fruitful understandings of the structures and physical properties of the investigated systems. So far, SPM image simulations are conventionally based on quantum mechanical theories, which can take several days in tasks of large-scale systems. Here, we have developed a scanning tunneling microscopy (STM) molecular image simulation and analysis framework based on a generative adversarial model, CycleGAN. It allows efficient translations between STM data and molecular models. Our CycleGAN-based framework introduces an approach for high-fidelity STM image simulation, outperforming traditional quantum mechanical methods in efficiency and accuracy. We envision that the integration of generative networks and high-resolution molecular imaging opens avenues in materials discovery relying on SPM technologies.

Unraveling the Mechanisms of On-Surface Photoinduced Reaction with Polarized Light Excitations.

On-surface reaction has been shown as a powerful strategy to achieve atomically precise nanostructures. Numerous reactions have been realized on surfaces with thermal annealing as the primary excitation. In contrast, far fewer reactions have been triggered by light on surfaces despite its advantages due to the nonthermal process. This is possibly ascribed to our limited understanding on the excitation mechanisms of on-surface photoinduced reactions. In this work, we have studied the photoinduced debrominated coupling by using a linearly polarized light. We successfully achieved the reaction with no annealing process and obtained oligomers as the primary reaction products, which is in contrast with the formation of polymers with traditional thermal treatments. By exploring the dependence of reaction yield on the angle of incidence, we demonstrate an experimental method that can provide fundamental insights. The comparison with the theoretical approximation suggests indirect hot carrier excitation as the leading excitation mechanism. Our results not only provide fundamental insight into the surface photochemical reactions but also set the basis for harnessing light to construct unconventional nanomaterials.

Order-Disorder Transition of Two-Dimensional Molecular Networks through a Stoichiometric Design.

Materials with disordered structures may exhibit interesting properties. Metal-organic frameworks (MOFs) are a class of hybrid materials composed of metal nodes and coordinating organic linkers. Recently, there has been growing interest in MOFs with structural disorder and the investigations of amorphous structures on surfaces. Herein, we demonstrate a bottom-up method to construct disordered molecular networks on metal surfaces by selecting two organic molecule linkers with the same symmetry but different sizes for preparing two-component samples with different stoichiometric ratios. The amorphous networks are directly imaged by scanning tunneling microscopy under ultrahigh vacuum with a submolecular resolution, allowing us to quantify its degree of disorder and other structural properties. Furthermore, we resort to molecular dynamics simulations to understand the formation of the amorphous metal-organic networks. The results may advance our understanding of the mechanism of formation of monolayer molecular networks with structural disorders, facilitating the design and exploration of amorphous MOF materials with intriguing properties.

Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets.

The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules.

Steering on-surface polymerization through coordination with a bidentate ligand.

A double-chain structure was fabricated on Au (111) with a bromine-functionalized phenanthroline precursor. We reveal the competition between the on-surface metal-ligand coordination and C-C coupling of the precursor by scanning tunneling microscopy (STM) imaging and density functional theory (DFT) calculations at the molecular level. Our work provides an additional strategy for controlling the on-surface polymerization, which is of great relevance to the construction of novel nanostructures.

Triangle Counting Rule: An Approach to Forecast the Magnetic Properties of Benzenoid Polycyclic Hydrocarbons.

Open-shell benzenoid polycyclic hydrocarbons (BPHs) are promising materials for future quantum applications. However, the search for and realization of open-shell BPHs with desired properties is a challenging task due to the gigantic chemical space of BPHs, requiring new strategies for both theoretical understanding and experimental advancement. In this work, by building a structure database of BPHs through graphical enumeration, performing data-driven analysis, and combining tight-binding and mean-field Hubbard calculations, we discovered that the number of the internal vertices of the BPH graphs is closely correlated to their open-shell characters. We further established a simple rule, the triangle counting rule, to predict the magnetic ground states of BPHs. These findings not only provide a database of open-shell BPHs, but also extend the well-known Lieb's theorem and Ovchinnikov's rule and provide a straightforward method for designing open-shell carbon nanostructures. These insights may aid in the exploration of emerging quantum phases and the development of magnetic carbon materials for technology applications.

A Deep-Learning Framework for the Automated Recognition of Molecules in Scanning-Probe-Microscopy Images.

Computer vision as a subcategory of deep learning tackles complex vision tasks by dealing with data of images. Molecular images with exceptionally high resolution have been achieved thanks to the development of techniques like scanning probe microscopy (SPM). However, extracting useful information from SPM image data requires careful analysis which heavily relies on human supervision. In this work, we develop a deep learning framework using an advanced computer vision algorithm, Mask R-CNN, to address the challenge of molecule detection, classification and instance segmentation in binary molecular nanostructures. We employ the framework to determine two triangular-shaped molecules of similar STM appearance. Our framework could accurately differentiate two molecules and label their positions. We foresee that the application of computer vision in SPM images will become an indispensable part in the field, accelerating data mining and the discovery of new materials.

On-surface synthesis of ethers through dehydrative coupling of hydroxymethyl substituents.

On-surface synthesis has been a subject of intensive research during the last decade. Various chemical reactions have been developed on surfaces to prepare compounds and carbon nanostructures, most of which are centered on the carbon-carbon bond formation. Despite the vast progress so far, the diversity of functional groups in organic chemistry has been far less explored in on-surface synthesis. Herein, we study the surface-assisted synthesis of ethers through the homocoupling of hydroxymethyl substituents on Ag(111). By using two hydroxymethyl substituent functionalized molecular precursors with different symmetries, we have achieved the formation of ether chains and rings. High-resolution scanning tunneling microscopy complemented with density functional theory calculations are used to support our findings and offer mechanistic insights into the reaction. This work expands the toolbox of on-surface reactions for the bottom-up fabrication of more sophisticated functional nanostructures.

Heteroatom-Edged [4]Triangulene: Facile Synthesis and Two-Dimensional On-Surface Self-Assemblies.

Triangulenes have attracted enormous interest in organic chemistry and materials science, but suffer from their high instability towards oxygen. Embedding heteroatoms into triangulenes provides a new class of ambient stable materials for various applications. However, [3]heterotriangulenes have dominated the chemistry of heteroatom-doped triangulenes, while their higher homologues have been rarely explored. In this work, we synthesize a new [4]heterotriangulene with three oxygen-boron-oxygen (OBO) segments incorporated into the zigzag edges. The planar geometry of the OBO-doped [4]triangulene is demonstrated by single-crystal X-ray diffraction. Self-assembly on metal surfaces reveals substrate-dependent nanostructures, leading to different long-range ordered 2D patterns on Ag and Cu substrates with negligible defects.

High-Throughput Preparation of Supramolecular Nanostructures on Metal Surfaces.

One of the contemporary challenges in materials science lies in the rapid materials screening and discovery. Experimental sample libraries can be generated by high-throughput parallel synthesis to map the composition space for rapid material discoveries. Molecular self-assembly on surfaces has proved a useful way to construct nanostructures with interesting topologies or properties. Despite the strong dependence of molecular stoichiometry on the structures, high-throughput preparations of supramolecular surface nanostructures have been far less explored. Here, by integrating a physical mask into the standard ultra-high-vacuum (UHV) molecular preparation system we show a high-throughput approach for preparing supramolecular nanostructures of continuous composition spreads on metal surfaces. The spatially addressable sample libraries of supramolecular self-assemblies are characterized by high-resolution scanning probe microscopy. We could explore different binary nanostructures of varying molecular ratios on one single substrate. Moreover, we use the minimum spanning tree approach to qualitatively and quantitatively study the structural properties of the formed nanostructures. This high-throughput approach may accelerate the screening and exploration of surface-supported, low-dimensional nanostructures not limited to supramolecular interactions.

Evolution of the Topological Energy Band in Graphene Nanoribbons.

Topological theory has been recently applied in graphene nanoribbons (GNRs) and predicts the existence of topological quantum states in junctions connecting GNRs of different topological classes. Through the periodic alignment of the topological states along a GNR backbone, frontier GNR electronic bands with tunable band gaps and band widths could be generated. In this work, we demonstrate the evolution of the topological band by fabricating GNR structures hosting a single topological junction, dimerized junctions, and multiple coupled junctions with on-surface synthesis, which guarantees the atomic precision of these nanostructures. Their structural and electronic properties are investigated by scanning tunneling microscopy and spectroscopy supported by tight-binding theory. The 1D superlattice of the topological junction states can be described by an effective two-band tight-binding Su-Schrieffer-Heeger (SSH) type model considering two alternating coupling motifs.

Pulse-regulated single-photon generation via quantum interference in a χ(2) nonlinear nanocavity.

A scalable on-chip single-photon source at telecommunications wavelengths is an essential component of quantum communication networks. In this work, we numerically construct a pulse-regulated single-photon source based on an optical parametric amplifier in a nanocavity. Under the condition of pulsed excitation, we study the photon statistics of the source using the Monte Carlo wave-function method. The results show that there exists an optimum excitation pulse width for generating high-purity single photons, while the source brightness increases monotonically with increasing excitation pulse width. More importantly, our system can be operated resonantly, and we show that in this case the oscillations in g(2)(0) are completely suppressed.