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.

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures.

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.

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.

First report of the root-knot nematode Meloidogyne incognita on Salvia miltiorrhiza Bunge in Henan Province, China.

Salvia miltiorrhiza Bunge is an important Chinese herbal medicine, mainly used to treat cardiovascular disease. At present, the planting area of S. miltiorrhiza is near 20,000 hectares in China, mainly in Shandong, Henan, Shanxi, Shaanxi and Sichuan provinces. Root-knot nematode (Meloidogyne spp.) is one of the most devastating pathogens on S. miltiorrhiza. In November 2020, we observed that some S. miltiorrhiza plants grew poorly with smaller, fewer and chlorotic leaves and even necrosis on some middle and lower ones in a Chinese herbal medicine planting base (34° 4' 11.52'' N; 113° 25' 51.40'' E) in Yuzhou City, Henan Province, China. Furthermore, the galls and egg masses were visible on the roots of S. miltiorrhiza, which were the typical symptoms caused by root-knot nematodes. Ten samples of galled roots and rhizosphere soils were collected, bagged and taken to the lab for tests. Females and J2s were extracted from these samples. White, pear-shaped females were observed in the roots, and the average number of second-stage juveniles (J2s) was 121.5 ± 10.8 per 100 ml of soil. The perineal patterns of females showed a high dorsal arch, which was either square or trapezoid with either smooth or wavy striae and without obvious lateral lines. The main morphometrics of females (n=20, mean ± SE; range) were as follows: body length (L)  = 609.0  ±â€¯ 62.5 µm (492.4 to 716.4 µm); maximum body width (W) = 377.0  ±â€¯ 28.6 µm (329.7 to 436.1 µm); stylet length  =  17.0  ±â€¯ 1.8 µm (14.2 to 20.5 µm); and distance from dorsal esophageal gland orifice to stylet knobs (DGO) =  3.3  ±â€¯ 0.3 µm (2.8 to 3.9 µm). The J2s were in vermiform, and stylet knobs were prominent and rounded. The tail of J2s possessed a transparent area with an obtuse tip. J2s (n  =  20) were measured (mean ± SD; range) as follows: L  =  401.2  ±â€¯ 29.3 µm (358.2 to 456.1 µm); W = 14.1 ± 1.1 µm (12.5 to 16.0 µm); L/W  = 28.6  ±â€¯ 1.0 (26.7 to 30.4); stylet length =  10.3  ±â€¯ 0.6 µm (9.1 to 11.2 µm); DGO  =  2.4  ±â€¯ 0.1 µm (2.1 to 2.6 µm); and tail length  =  49.3  ± 2.8 µm (45.2 to 54.7 µm). All the key morphometrics were similar to those of the M. incognita population described by Song et al. (2019). The PCR amplifications of rDNA-internal transcribed spacer (ITS) fragments generated an amplicon of 544 bp from a single female or/and J2s (n = 22) using the universal primers M18S (5'-AACCTGCTGCTGGATCATTAC-3') and M28S (5'-GTATGCTTAAGTTCAGCG-3') (Feng et al. 2010). The PCR amplifications were repeated five times for each sample, and the products were purified and sequenced. The obtained sequnce was deposited in GenBank with Acc. No. OM304617.1. The amplified ITS region sequence was identical to those of M. incognita from India (KT869139.1) and China (MT490926.1 and MT071559.1). For confirmation, the primers species-specific for M. incognita (Inc-K14-F, 5'- GGGATGTGTAAATGCTCCTG -3' and Inc-K14-R, 5'- CCCGCTACACCCTCAACTTC -3') were further used for amplification. Expected PCR amplicon of 399 bp was acquired, which was consistent with previous report for M. incognita (Randig et al. 2002). Pathogenicity and reproduction of this M. incognita population on S. miltiorrhiza was confirmed and examined. Seeds of S. miltiorrhiza were sown in the pots filled with 200 ml of autoclaved soil mixture (loamy soil/sand, 1:1). Two weeks later, a total of 12 plants were inoculated each with 400 J2s, which were hatched from a field-derived M. incognita population. Four plants without nematode inoculation were used as the control. The plants grew in a chamber at 25/30 °C under 12-h dark/12-h light conditions. The parasitic J2s, J3s, J4s and females in roots were observed under a stereomicroscope at 5, 15 and 30 days post inoculation (dpi). At 35 dpi, an average of 98.3 ± 15.7 galls and 23.8 ± 6.9 egg masses per S. miltiorrhiza plant were counted, and the root gall index reached 6 according to the 0-10 RKN rating scale (Poudyal et al. 2005). Nematodes were re-isolated from the roots and their morphological and molecular characteristics were identical to the nematodes obtained from the original samples. Furthermore, all the inoculated S. miltiorrhiza roots showed typical RKN galls with the same symptoms as those initially observed in the field. No symptoms were developed on the non-inoculated control plants, and from which no nematodes were isolated. The nematode on S. miltiorrhiza was therefore certified as M. incognita. Han et al. (2019) isolated and morphologically identified M. incognita from the roots of S. miltiorrhiza and Trichosanthes kirilowii Maximin in Changqing area of Shandong Province, China, but did not perform the Koch's Rule. To our knowledge, this is the first formal report of M. incognita infecting S. miltiorrhiza in Henan Province, China. With the increase of Chinese herbal medicine planting area, plant parasitic nematodes are becoming more and more serious and have become an limiting factor on medicinal plant production, and the yield losses can be as high as 70%. This finding provides important and solid information for growers of Chinese medicinal plants, based on which suitable management action should be taken.

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.

First record of Aphelenchoides besseyi on foxtail millet (Setaria italica) in Henan Province of China.

Aphelenchoides besseyi is one of the important plant-parasitic nematodes on rice, reducing approximate 10-20% of the rice yield annually (Jones et al. 2013). Foxtail millet (Setaria italica) has been a major cereal crop in Northern China, especially in the semi-arid areas of this region, for thousands of years. In August of 2019 and 2020, a survey of nematodes on autumn grain crops was performed each year. One foxtail millet field (N34° 58' 027″ and E113° 39' 059″) in Yuanyang County of Henan Province caught our attention. Some upper leaves showed chlorosis without or with necrotic tips, and flag leaves presented crinkling and distortion, stalks were colored, earheads were vertical, glumes were brown or light black and open, and grains became thin. A total of ten samples were collected, and the nematodes were isolated from the spike pieces by shallow plate method and counted under a stereomicroscope. The average number of nematodes per earhead of foxtail millet counted up to 1738.75 ± 107.72. Morphologically, females were slender with a short stylet, an oval metacorpus with a distinct valve, a labial region slightly wider than the first body annulus and a conoid tail with a terminus bearing a star-shaped mucro with four pointed processes. The females were characterized as follows (mean ± SD; n=20): body length (L) = 668.92 ± 12.73 µm (647.38 to 689.70 µm); maximum body width (W) = 14.35 ± 1.11 µm (12.12 to 16.88 µm); L/W = 46.83 ± 2.94 (40.44 to 50.03); tail length = 38.93 ± 3.48 µm (33.41 to 45.92 µm); L/tail length = 17.31 ± 1.44 (14.47 to 19.62); and stylet length (ST) = 11.57 ± 0.57 µm (10.77 to 12.34 µm). The males had three pairs of ventrosubmedian papillae with the first one adanal, spicula curved with a slight basal process, terminus bearing four mucrones arranged variably, and the whole worm was in 'J' shape. The males could be described as follows (mean ± SD, n = 20): L = 606.66 ± 10.70 µm (586.49 to 626.37 µm); W = 13.95 ± 0.60 µm (12.71 to 14.94 µm); L/W = 43.55 ± 1.69 (40.73 to 46.43); tail length = 35.54 ± 1.93 µm (31.41 to 38.18 µm); L/tail length = 17.07 ± 0.79 (16.05 to 18.67); ST = 11.53 ± 0.56 µm (1061 to 12.76 µm). All the key morphometrics were consistent with those of A. besseyi reported from Brazil (Favoreto et al. 2018) and China (Lin et al. 2004; Ou et al. 2014). The amplifications of rDNA internal transcribed spacer (ITS) fragments generated a PCR fragment of 830 bp from a single nematode, using the primers set TW81 (5'-GTTTCCGTAGGTGAACCTGC-3') and AB28 (5'-ATATGCTTAAGTTCAGCGGGT-3') (Joyce et al. 1994). Five independent PCR experiments were conducted, and all the PCR products were purified and sequenced. Nucleotide sequence of ITS-rDNA was deposited in GenBank with Accession Number OK090549.1. The obtained ITS region sequence was more than 99% identical to those of A. besseyi reported from China (MW216945.1) and India (JF826518.1, JF826519.1 and JF826517.1). These ITS sequence results further supported that the isolated nematodes were A. besseyi. Subsequently, the species-specific primers of A. besseyi (BSF, 5'-TCGATGAAGAACGCAGTGAATT-3' and BSR, 5'-AGATCAAAAGCCAATCGAATCAT-3') were used for confirmation by PCR (Cui et al. 2010). An expected PCR fragment of 312 bp was obtained, which was consistent with those of A. besseyi reported previously. The pathogenicity of identified A. besseyi was confirmed by infection of foxtail millet (Setaria italica cv. 'Yugu33'). Foxtail millet budding seeds were sown in the pots contained 150 mL of sterile soil mixture. In two weeks, 10 seedlings were inoculated with 100 A. besseyi each, and 4 plants were non-inoculated as the control. The foxtail millet seedlings were grown in a plant-growth chamber at 25/30°C under 12 h dark/12 h light. On the average, 73.3 and 138.2 of A. besseyi were isolated from each plant at 15 and 40 days post inoculation, respectively. Both the morphological and molecular characteristics were identical with those nematodes obtained from the original samples. All the upper leaves of the inoculated plants showed chlorosis and necrosis, symptoms that were similar to those observed in the field, and neither symptom developed on the non-inoculated control plants, nor were nematodes re-isolated from the control plants. To the best of our knowledge, this is the first record of A. besseyi on foxtail millet in Henan Province of North China. Henan is one of the most important grain-producing areas in China, and A. besseyi is an important domestic quarantine nematode, which may become a severe threat to cereal production in Henan Province. Our findings will be very beneficial for A. besseyi management and further research on foxtail millet in Henan Province of North China.