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Results for “bridge the gap”
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Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy
What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.
Bridging gaps in permafrost-shrub understanding
Permafrost, permanently frozen ground which underlies much of the Arctic and sub-Arctic, is influenced by deciduous shrubs, yet our understanding of permafrost-shrub interactions is limited. This is largely due to a lack of widespread, long-term, high-quality observations across Arctic and sub-Arctic systems, which are difficult to study due to their remote locations. Shrubs are rapidly expanding in many areas of Arctic tundra and can either amplify or inhibit permafrost thaw, making it crucial that we can understand and predict permafrost-shrub interactions. As we have limited time and resources to make observations, we must design our field campaigns to be as impactful as possible. Below we outline the current state of knowledge and give suggestions about how we can maximize our fieldwork’s impact. From field studies conducted around the Arctic, we know that shrubs generally have a cooling effect on permafrost in the summer, largely caused by the shrub canopy shading the soil. However, summertime cooling can be counteracted by a warming effect in winter, caused by the trapping of blowing snow by shrubs, which leads to increased snow depths. In addition to these commonly observed effects, both observations and models show that other local factors such as climate, soil, snow, and disturbances can interact to cause contrasting dominant effects on permafrost temperatures. For example, shrubs that protrude through the snowpack have been observed to cool permafrost in winter via thermal bridging between the air above the snow and the soil below. However, at other research sites, protruding shrubs lower the surface albedo and cause earlier snowmelt in spring, amplifying permafrost thaw by exposing the soil to thawing earlier. Protruding shrubs have also been observed to induce snow melt in autumn, creating ice layers at the snow surface that prevent further snow accumulation and eliminate the ability of shrubs to trap blowing snow, thereby reducing the winter warming effect. While we understand the physical processes that lead to these effects, we cannot explain why we observe different effects between sites.
Mind the Gap: Bridging Differences in Determinations of the Strong Coupling from e + e - Event Shapes [Slides]
Abstract not provided.
Bridging Gaps in Simulation Analysis through a General Purpose, Bidirectional Steering Interface with Ascent
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Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis.
Abstract not provided.
Mind the Gap: On Bridging the Semantic Gap Between Machine Learning and Malware Analysis.
Abstract not provided.
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security.
Abstract not provided.
Translating Material-Level Characterization of Carbon-Nanotube-Reinforced Composite Gridlines to Module-Level Degradation
Cell cracks in PV modules caused by poor handling during shipping and installation as well as from extreme weather events can lead to gradual or immediate power degradation. To directly address cell-crack-induced degradation, we have formulated a carbon nanotube additive for commercial screen printed silver pastes. We have shown in previous work that these metal matrix composites have little to no effect on the cell's efficiency while enhancing the metallization's fracture toughness and electrical gap-bridging capability. In this work, we focus on translating materials level characterization techniques to module level degradation. We found that we get conflicting results from two different methods of measuring the metallization's ability to electrically bridge gaps in cracked solar cells. Mini-module stress testing is currently underway to determine which materials characterization correlates well with the min-module degradation characteristics.
Bridging a gap between continuum-QCD and ab initio predictions of hadron observables
Within contemporary hadron physics there are two common methods for determining the momentum-dependence of the interaction between quarks: the top-down approach, which works toward an ab initio computation of the interaction via direct analysis of the gauge-sector gap equations; and the bottom-up scheme, which aims to infer the interaction by fitting data within a well-defined truncation of those equations in the matter sector that are relevant to bound-state properties. We unite these two approaches by demonstrating that the renormalisation-group-invariant running-interaction predicted by contemporary analyses of QCD's gauge sector coincides with that required in order to describe ground-state hadron observables using a nonperturbative truncation of QCD's Dyson–Schwinger equations in the matter sector. This bridges a gap that had lain between nonperturbative continuum-QCD and the ab initio prediction of bound-state properties.
Electronic structure of aqueous solutions: Bridging the gap between theory and experiments
Predicting the electronic properties of aqueous liquids has been a long-standing challenge for quantum mechanical methods. However, it is a crucial step in understanding and predicting the key role played by aqueous solutions and electrolytes in a wide variety of emerging energy and environmental technologies, including battery and photoelectrochemical cell design. Here, we propose an efficient and accurate approach to predict the electronic properties of aqueous solutions, on the basis of the combination of first-principles methods and experimental validation using state-of-the-art spectroscopic measurements. We present results of the photoelectron spectra of a broad range of solvated ions, showing that first-principles molecular dynamics simulations and electronic structure calculations using dielectric hybrid functionals provide a quantitative description of the electronic properties of the solvent and solutes, including excitation energies. The proposed computational framework is general and applicable to other liquids, thereby offering great promise in understanding and engineering solutions and liquid electrolytes for a variety of important energy technologies.
Recent development of end-of-life strategies for plastic in industry and academia: bridging their gap for future deployment
Bridging the gap between academia and industry in plastic recycling will accelerate innovation and deployment toward solving the global challenge of plastic waste management and establishing net zero carbon society.
Bridging the Gap Between Bulk and Nanostructured Photoelectrodes: The Impact of Surface States on the Electrocatalytic and Photoelectrochemical Properties of MoS2
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Bridging the gap between molecules and materials in quantum chemistry with localized active spaces
The number of materials that “bridge the gap” between single molecules and extended solids, such as metal-organic frameworks and organic semiconductors, has been increasing. Consequently, there is a growing need for modeling approaches that effectively integrate the real-space molecular perspective employed by computational chemists and the reciprocal-space dispersive perspective employed by computational physicists. Here, we propose the localized active space (LAS) approach as a promising method to successfully bridge this gap. The LAS approach extends the active space concept from multiconfigurational methods such as complete active space self-consistent field theory to multiple molecular fragments via a product-form wave function ansatz. Here, we apply this method to solid state phenomena by treating each unit cell as a fragment with different sets of local quantum numbers (e.g., charge and excitation number). State interaction between these LAS states (LASSI) thus provides a comprehensive basis for the study of charge and energy transfer, meeting and surpassing the capabilities of single-reference fragmentation approaches such as constrained density functional theory (cDFT). Most centrally, we show how combining this LASSI approach with multiconfigurational pair-density functional theory (MC-PDFT) provides an elegant and efficient method to compute band structures that capture multiconfigurational character. We apply the LASSI band structure approach to the computation of band gaps in stretched hydrogen chain, polyacetylene, and bulk nickel oxide (NiO), finding good or excellent quantitative agreement with reference values in all cases. Additionally, we use the LAS basis in one-dimensional model systems to demonstrate its ability to treat difficult solid-state phenomena such as exciton transfer and excitation at p-n junctions.
Bridging the Gap: Linking Simulation and Testing
The Materials Genome Initiative (MGI) which is a key enabler for the Advanced Manufacturing Partnership, announced in 2011 by U.S. President Barack Obama, was established to accelerate the development and deployment of advanced materials. The MGI is driven by the need to "bridge the gap" between (I) experimental results and computational analysis to enable the rapid development and validation of new mateirals, and (II) the processes required to convert these materials into useable goods.
Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research
Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.
Bridging the gap between atomistic phenomena and continuum behavior in electrochemical energy storage processes
One of the most significant impediments to advances in electrochemical energy storage lies in the gap between fundamental understanding of atomistic phenomena and our understanding of the impact of these phenomena on system performance at device scales. Atomistic models (DFT, MD, MC) provide insight into such phenomena along with a means of quantification, but such models are too computationally intensive to address device-scale behavior. Similarly, device-scale insight for design and optimization can be obtained through continuum models that are sufficiently fast, but these models account for only the simplest atomistic phenomena. There is thus a large gap between our ability to develop fundamental understanding and our ability to use this understanding to make rapid advances in energy storage technologies. The goal of this work is to help bridge this gap through an innovative synthesis of atomistic and continuum approaches in which atomistic phenomena are captured through fast reduced-order integral methods that can be imbedded into continuum-like models describing device-scale behavior.