Time-resolved THz Stark spectroscopy of molecules in solution.

For decades, it was considered all but impossible to perform Stark spectroscopy on molecules in a liquid solution, because their concomitant orientation to the applied electric field results in overwhelming background signals. A way out was to immobilize the solute molecules by freezing the solvent. While mitigating solute orientation, freezing removes the possibility to study molecules in liquid environments at ambient conditions. Here we demonstrate time-resolved THz Stark spectroscopy, utilizing intense single-cycle terahertz pulses as electric field source. At THz frequencies, solute molecules have no time to orient their dipole moments. Hence, dynamic Stark spectroscopy on the time scales of molecular vibrations or rotations in both non-polar and polar solvents at arbitrary temperatures is now possible. We verify THz Stark spectroscopy for two judiciously selected molecular systems and compare the results to conventional Stark spectroscopy and first principle calculations.

Enhancing Single-Frame Supervision for Better Temporal Action Localization.

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

Visual Analytics for Machine Learning: A Data Perspective Survey.

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this survey. Since data quality greatly impacts the performance of ML models, our survey focuses specifically on summarizing VIS4ML works from the data perspective. First, we categorize the common data handled by ML models into five types, explain the unique features of each type, and highlight the corresponding ML models that are good at learning from them. Second, from the large number of VIS4ML works, we tease out six tasks that operate on these types of data (i.e., data-centric tasks) at different stages of the ML pipeline to understand, diagnose, and refine ML models. Lastly, by studying the distribution of 143 surveyed papers across the five data types, six data-centric tasks, and their intersections, we analyze the prospective research directions and envision future research trends.

Cluster-Aware Grid Layout.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision.

Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.

Interactive Reweighting for Mitigating Label Quality Issues.

Label quality issues, such as noisy labels and imbalanced class distributions, have negative effects on model performance. Automatic reweighting methods identify problematic samples with label quality issues by recognizing their negative effects on validation samples and assigning lower weights to them. However, these methods fail to achieve satisfactory performance when the validation samples are of low quality. To tackle this, we develop Reweighter, a visual analysis tool for sample reweighting. The reweighting relationships between validation samples and training samples are modeled as a bipartite graph. Based on this graph, a validation sample improvement method is developed to improve the quality of validation samples. Since the automatic improvement may not always be perfect, a co-cluster-based bipartite graph visualization is developed to illustrate the reweighting relationships and support the interactive adjustments to validation samples and reweighting results. The adjustments are converted into the constraints of the validation sample improvement method to further improve validation samples. We demonstrate the effectiveness of Reweighter in improving reweighting results through quantitative evaluation and two case studies.

Self-Assembly of N-Rich Triimidazoles on Ag(111): Mixing the Pleasures and Pains of Epitaxy and Strain.

In the present report, homochiral hydrogen-bonded assemblies of heavily N-doped (C9H6N6) heterocyclic triimidazole (TT) molecules on an Ag(111) substrate were investigated using scanning tunneling microscopy (STM) and low energy electron diffraction (LEED) techniques. The planar and prochiral TT molecules, which exhibit a threefold rotation symmetry and lack mirror symmetry when assembled on the substrate, carry multiple hydrogen-bonding donor and acceptor functionalities, inevitably leading to the formation of hexameric two-dimensionally extended assemblies that can be either homo- (RR/SS) or heterochiral (RS). Experimental STM data showing well-ordered homochiral domains and experimental LEED data are consistent with simulations assuming the R19.1° overlayer on the Ag(111) lattice. Importantly, we report the unexpected coincidence of spontaneous resolution with the condensation of neighboring islands in adjacent "Janus pairs". The islands are connected by a characteristic fault zone, an observation that we discuss in the context of the fairly symmetric molecule and its propensity to compromise and benefit from interisland bonding at the expense of lattice mismatches and strain in the defect zone. We relate this to the close to triangular shape and the substantial but weak bonding scheme beyond van der Waals (vdW) of the TT molecules, which is due to the three N-containing five-membered imidazole rings. Density functional theory (DFT) calculations show clear energetic differences between homochiral and heterochiral pairwise interactions, clearly supporting the experimental results.

High pressure behaviour of the organic semiconductor salt (TTF-BTD)2I3.

This study focuses on the effect of structure compression and cooling on the stereoelectronic properties of the planar π-conjugated TTF-BTD (TTF = tetrathiafulvalene; BTD = 2,1,3-benzothiadiazole) molecule, a prototypical example in which an electron-donor moiety is compactly annulated to an electron-acceptor moiety. Its partially oxidised iodine salt (TTF-BTD)2I3 is a crystalline semiconductor featuring segregated columns of TTF+0.5 units stacked via alternating short and long π-π interactions. We studied TTF-BTD at temperatures ranging from 300 K to 90 K and at pressures up to 7.5 GPa, using both X-ray diffraction and Raman spectroscopy to determine the properties of the compressed samples. Periodic DFT calculations and several theoretical tools were employed to characterize the calculated structural modifications and to predict the structural changes up to 60 GPa. The existence of an unprecedented new phase is predicted above 20 GPa, following a covalent bond formation between two neighbouring TTF-BTD units.

Photoinduced asymmetric charge trapping in a symmetric tetraazapyrene-fused bis(tetrathiafulvalene) conjugate.

In fused donor-acceptor (D-A) ensembles, rapid charge recombination often occurs because the D and A units are spatially close and strongly coupled. To the best of our knowledge, a long-lived charge separated (CS) state is still elusive in such systems. The results presented here show that symmetric annulation of two tetrathiafulvalene (TTF) donors to a central tetraazapyrene (TAP) acceptor via two quinoxaline units leads to a CS state lifetime of a few ns. A detailed study of the electronic interactions between TTF and TAP units in the ground and excited states was performed and compared with the asymmetric counterpart by cyclic voltammetry, optical absorption and ultrafast transient absorption spectroscopy. The results demonstrate that the photoinduced asymmetric charge trapping between two TTFs significantly stabilizes the CS state, which is also verified theoretically.

Grand Challenges in Visual Analytics Applications.

In the past two decades, research in visual analytics (VA) applications has made tremendous progress, not just in terms of scientific contributions, but also in real-world impact across wide-ranging domains including bioinformatics, urban analytics, and explainable AI. Despite these success stories, questions on the rigor and value of VA application research have emerged as a grand challenge. This article outlines a research and development agenda for making VA application research more rigorous and impactful. We first analyze the characteristics of VA application research and explain how they cause the rigor and value problem. Next, we propose a research ecosystem for improving scientific value, and rigor and outline an agenda with 12 open challenges spanning four areas, including foundation, methodology, application, and community. We encourage discussions, debates, and innovative efforts toward more rigorous and impactful VA research.

Merging of Azulene and Perylene Diimide for Optical pH Sensors.

Polycyclic aromatic hydrocarbons (PAHs) have emerged as promising materials for organic electronics, including organic photovoltaics (OPVs), organic field-effect transistors (OFETs), and organic light-emitting diodes (OLEDs). Particularly, non-hexagonal ring-fused PAHs are highly desirable due to their unique optoelectronic properties. Herein, a new redox-active azulene-perylene diimide triad 1 and its ring-fused counterpart, diazulenocoronene diimide 2, were synthesized and fully characterized by a combination of NMR, cyclic voltammetry, and UV-visible absorption spectroscopy. Direct comparison of their electronic properties leads us to the conclusion that a significant change in the localization of HOMO and LUMO occurs upon the fusion of azulene and perylene diimide in 2, leading to the lack of intramolecular charge-transfer character for transitions in the visible spectral region. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were performed to gain further insight into various electronic transitions. Moreover, we found that the adaptive response to acids and bases manifests itself in a reversible two-color change that can be attributed to changes in the chemical structures. Our findings pave the way for manipulating the relative HOMO and LUMO energy levels of organic chromophores by fusing non-alternant azulenes to an otherwise flat PAH, which could possibly lead to applications in organic electronics and optical sensors.

Strong signature of electron-vibration coupling in molecules on Ag(111) triggered by tip-gated discharging.

Electron-vibration coupling is of critical importance for the development of molecular electronics, spintronics, and quantum technologies, as it affects transport properties and spin dynamics. The control over charge-state transitions and subsequent molecular vibrations using scanning tunneling microscopy typically requires the use of a decoupling layer. Here we show the vibronic excitations of tetrabromotetraazapyrene (TBTAP) molecules directly adsorbed on Ag(111) into an orientational glassy phase. The electron-deficient TBTAP is singly-occupied by an electron donated from the substrate, resulting in a spin 1/2 state, which is confirmed by a Kondo resonance. The TBTAP•- discharge is controlled by tip-gating and leads to a series of peaks in scanning tunneling spectroscopy. These occurrences are explained by combining a double-barrier tunneling junction with a Franck-Condon model including molecular vibrational modes. This work demonstrates that suitable precursor design enables gate-dependent vibrational excitations of molecules on a metal, thereby providing a method to investigate electron-vibration coupling in molecular assemblies without a decoupling layer.

Atomically well-defined nitrogen doping for cross-plane transport through graphene heterojunctions.

The nitrogen doping of graphene leads to graphene heterojunctions with a tunable bandgap, suitable for electronic, electrochemical, and sensing applications. However, the microscopic nature and charge transport properties of atomic-level nitrogen-doped graphene are still unknown, mainly due to the multiple doping sites with topological diversities. In this work, we fabricated atomically well-defined N-doped graphene heterojunctions and investigated the cross-plane transport through these heterojunctions to reveal the effects of doping on their electronic properties. We found that a different doping number of nitrogen atoms leads to a conductance difference of up to ∼288%, and the conductance of graphene heterojunctions with nitrogen-doping at different positions in the conjugated framework can also lead to a conductance difference of ∼170%. Combined ultraviolet photoelectron spectroscopy measurements and theoretical calculations reveal that the insertion of nitrogen atoms into the conjugation framework significantly stabilizes the frontier molecular orbitals, leading to a change in the relative positions of the HOMO and LUMO to the Fermi level of the electrodes. Our work provides a unique insight into the role of nitrogen doping in the charge transport through graphene heterojunctions and materials at the single atomic level.

Proximity-Induced Superconductivity in Atomically Precise Nanographene on Ag/Nb(110).

Obtaining a robust superconducting state in atomically precise nanographene (NG) structures by proximity to a superconductor could foster the discovery of topological superconductivity in graphene. On-surface synthesis of such NGs has been achieved on noble metals and metal oxides; however, it is still absent on superconductors. Here, we present a synthetic method to induce superconductivity of polymeric chains and NGs adsorbed on the superconducting Nb(110) substrate covered by thin Ag films. Using atomic force microscopy at low temperature, we characterize the chemical structure of each subproduct formed on the superconducting Ag layer. Scanning tunneling spectroscopy further allows us to elucidate the electronic properties of these nanostructures, which consistently show a superconducting gap.

Polygonal tessellations as predictive models of molecular monolayers.

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions-self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

Signatures of Room-Temperature Quantum Interference in Molecular Junctions.

ConspectusDuring the past decade or so, research groups around the globe have sought to answer the question: "How does electricity flow through single molecules?" In seeking the answer to this question, a series of joint theory and experimental studies have demonstrated that electrons passing through single-molecule junctions exhibit exquisite quantum interference (QI) effects, which have no classical analogues in conventional circuits. These signatures of QI appear even at room temperature and can be described by simple quantum circuit rules and a rather intuitive magic ratio theory. The latter describes the effect of varying the connectivity of electrodes to a molecular core and how electrical conductance can be controlled by the addition of heteroatoms to molecular cores. The former describes how individual moieties contribute to the overall conductance of a molecule and how the overall conductance can change when the connectivities between different moieties are varied. Related circuit rules have been derived and demonstrated, which describe the effects of connectivity on Seebeck coefficients of organic molecules. This simplicity arises because when a molecule is placed between two electrodes, charge transfer between the molecule and electrodes causes the molecular energy levels to adjust, such that the Fermi energy (EF) of the electrodes lies within the energy gap between the highest occupied molecular orbital and lowest unoccupied molecular orbital. Consequently, when electrons of energy EF pass through a molecule, their phase is protected and transport takes place via phase-coherent tunneling. Remarkably, these effects have been scaled up to self-assembled monolayers of molecules, thereby creating two-dimensional materials, whose room temperature transport properties are controlled by QI. This leads to new molecular design strategies for increasing the on/off conductance ratio of molecular switches and to improving the performance of organic thermoelectric materials. In particular, destructive quantum interference has been shown to improve the Seebeck coefficient of organic molecules and increase their on/off ratio under the influence of electrochemical gating. The aim of this Account is to introduce the novice reader to these signatures of QI in molecules, many of which have been identified in joint studies involving our theory group in Lancaster University and experimental group in Bern University.

In Defence of Visual Analytics Systems: Replies to Critics.

The last decade has witnessed many visual analytics (VA) systems that make successful applications to wide-ranging domains like urban analytics and explainable AI. However, their research rigor and contributions have been extensively challenged within the visualization community. We come in defence of VA systems by contributing two interview studies for gathering critics and responses to those criticisms. First, we interview 24 researchers to collect criticisms the review comments on their VA work. Through an iterative coding and refinement process, the interview feedback is summarized into a list of 36 common criticisms. Second, we interview 17 researchers to validate our list and collect their responses, thereby discussing implications for defending and improving the scientific values and rigor of VA systems. We highlight that the presented knowledge is deep, extensive, but also imperfect, provocative, and controversial, and thus recommend reading with an inclusive and critical eye. We hope our work can provide thoughts and foundations for conducting VA research and spark discussions to promote the research field forward more rigorously and vibrantly.

Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles.

Recent advances in artificial intelligence largely benefit from better neural network architectures. These architectures are a product of a costly process of trial-and-error. To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles. The key idea behind our method is to make the architecture space explainable by exploiting structural distances between architectures. We formulate the pairwise distance calculation as solving an all-pairs shortest path problem. To improve efficiency, we decompose this problem into a set of single-source shortest path problems. The time complexity is reduced from O(kn2N) to O(knN). Architectures are hierarchically clustered according to the distances between them. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.

Energy Dissipation from Confined States in Nanoporous Molecular Networks.

Crystalline nanoporous molecular networks are assembled on the Ag(111) surface, where the pores confine electrons originating from the surface state of the metal. Depending on the pore sizes and their coupling, an antibonding level is shifted upward by 0.1-0.3 eV as measured by scanning tunneling microscopy. On molecular sites, a downshifted bonding state is observed, which is occupied under equilibrium conditions. Low-temperature force spectroscopy reveals energy dissipation peaks and jumps of frequency shifts at bias voltages, which are related to the confined states. The dissipation maps show delocalization on the supramolecular assembly and a weak distance dependence of the dissipation peaks. These observations indicate that two-dimensional arrays of coupled quantum dots are formed, which are quantitatively characterized by their quantum capacitances and resonant tunneling rates. Our work provides a method for studying the capacitive and dissipative response of quantum materials with nanomechanical oscillators.

Quantum Interference-Controlled Conductance Enhancement in Stacked Graphene-like Dimers.

Stacking interactions are of significant importance in the fields of chemistry, biology, and material optoelectronics because they determine the efficiency of charge transfer between molecules and their quantum states. Previous studies have proven that when two monomers are π-stacked in series to form a dimer, the electrical conductance of the dimer is significantly lower than that of the monomer. Here, we present a strong opposite case that when two anthanthrene monomers are π-stacked to form a dimer in a scanning tunneling microscopic break junction, the conductance increases by as much as 25 in comparison with a monomer, which originates from a room-temperature quantum interference. Remarkably, both theory and experiment consistently reveal that this effect can be reversed by changing the connectivity of external electrodes to the monomer core. These results demonstrate that synthetic control of connectivity to molecular cores can be combined with stacking interactions between their π systems to modify and optimize charge transfer between molecules, opening up a wide variety of potential applications ranging from organic optoelectronics and photovoltaics to nanoelectronics and single-molecule electronics.