The visualization of data is indispensable in scientific research, from the early stages when human insight forms, to the final step of communicating results. In computational physics, 1 chemistry and materials science, it can be as simple as making a scatter plot, or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bio-activities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural datasets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool, ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large datasets, but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science 2 has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of datasets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited...
Hyaluronan (HA) is a simple but diverse glycosaminoglycan. It plays a major role in aging, cellular senescence, cancer, and tissue homeostasis. In which way HA affects the surrounding tissues greatly depends on the molecular weight of HA. Whereas high molecular weight HA is associated with homeostasis and protective effects, HA fragments tend to be linked to the pathologic state. Furthermore, the interaction of HA with its binding partners, the hyaladherins, such as CD44, is essential for sustaining tissue integrity and is likewise related to cancer. The naked mole rat, a rodent species, possesses a special form of very high molecular weight (vHMW) HA, which is associated with the extraordinary cancer resistance and longevity of those animals. This review addresses HA and its diverse facets: from HA synthesis to degradation, from oligomeric HA to vHMW-HA and from its beneficial properties to the involvement in pathologies. We further discuss the functions of HA in the naked mole rat and compare them to human conditions. Though intensively researched, this simple polymer bears some secrets that may hold the key for a better understanding of cellular processes and the development of diseases, such as cancer.
Over the last two decades, several classes of highly ion-conductive SSEs have been developed which reach or surpass current liquid-state electrolyte conductivity. [5,6] Yet, no ASSB paying in on the above promises has been developed to date. This is mainly due to mechanochemical, chemical, and electrochemical stability issues and interfacial processes that have severely compromised any proposed cell's lifetime. [7][8][9][10][11] While many SSE material inherent (mechano-)chemical processing issues seem amenable to modern engineering approaches, [12][13][14][15][16][17][18][19] the situation is less bright regarding the control of interfacial chemical and electrochemical stability (especially when featuring a LMA), as well as ionic and electronic transport quantities across these interfaces. A hitherto missing deep understanding of the structural, chemical, and physical properties of the buried solid-solid interfaces inside ASSBs at the atomic level is required to overcome these performance limiting interfacial issues.The most studied interfacial properties so far are contact stability and dendrite nucleation and growth. [20][21][22] Both issues are accentuated for LMA/SSE interfaces. In a first approximation, interfacial stability can be traced back to the Dendrite formation and growth remains a major obstacle toward highperformance all solid-state batteries using Li metal anodes. The ceramic Li (1+x) Al (x) Ti (2−x) (PO 4 ) 3 (LATP) solid-state electrolyte shows a higher than expected stability against electrochemical decomposition despite a bulk electronic conductivity that exceeds a recently postulated threshold for dendrite-free operation. Here, transmission electron microscopy, atom probe tomography, and first-principles based simulations are combined to establish atomistic structural models of glass-amorphous LATP grain boundaries. These models reveal a nanometer-thin complexion layer that encapsulates the crystalline grains. The distinct composition of this complexion constitutes a sizable electronic impedance. Rather than fulfilling macroscopic bulk measures of ionic and electronic conduction, LATP might thus gain the capability to suppress dendrite nucleation by sufficient local separation of charge carriers at the nanoscale.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.