Publisher Correction: A scalable synergy-first backbone decomposition of higher-order structures in complex systems
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A scalable synergy-first backbone decomposition of higher-order structures in complex systems
In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.
Effects of nitrogen vacancy sites of oxynitride support on the catalytic activity for ammonia decomposition
Nitrogen-containing compounds such as imides and amides have been reported as efficient materials that promote ammonia decomposition over nonnoble metal catalysts. However, these compounds decompose in an air atmosphere and become inactive, which leads to difficulty in handling. Here, we focused on perovskite oxynitrides as air-stable and efficient supports for ammonia decomposition catalysts. Ni-loaded oxynitrides exhibited 2.5–18 times greater catalytic activity than did the corresponding oxide-supported Ni catalysts, even without noticeable differences in the Ni particle size and surface area of the supports. The catalytic performance of the Ni-loaded oxynitrides is well correlated with the nitrogen desorption temperature during N2 temperature-programmed desorption, which suggests that the lattice nitrogen in the oxynitride support rather than the Ni surface is the active site for ammonia decomposition. Furthermore, NH3 temperature-programmed surface reactions and density functional theory (DFT) calculations revealed that NH3 molecules are preferentially adsorbed on the nitrogen vacancy sites on the support surface rather than on the Ni surface. Thus, the ammonia decomposition reaction is facilitated by a vacancy-mediated reaction mechanism.
Advanced 3D printing accelerates electromagnetic wave absorption from ceramic materials to structures
As 3D printing technology and ceramic material advance, significant progress has been achieved in the field of 3D-printed ceramic materials for electromagnetic wave absorption (EMWA), transitioning from simple material fabrication to complex structure creation. This review summarizes the key advancements in ceramic materials and structures fabricated by 3D printing for EMWA. Despite significant progress, the limitations that remain in 3D-printed ceramic materials and structures for EMWA are highlighted, and future development tendencies are also identified. This review aims to motivate further development and application of 3D-printed ceramic materials and structures for EMWA.
Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR
Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, Acinetobacter baumannii (A. baumannii), one of the main pathogens driving the rise of nosocomial infections, is a Gram-negative bacillus that displays intrinsic resistance mechanisms and can also develop resistance by acquiring AMR genes from other bacteria. More importantly, it is resistant to nearly 90% of standard of care (SOC) antimicrobial treatments, resulting in unsatisfactory clinical outcomes and a high infection-associated mortality rate of over 30%. Currently, there is a growing challenge to sustainably develop novel antimicrobials in this ever-expanding arms race against AMR. Therefore, a sustainable workflow that properly manages healthcare resources to ultra-rapidly design optimal drug combinations for effective treatment is needed. In this study, the IDentif.AI-AMR platform was harnessed to pinpoint effective regimens against four A. baumannii clinical isolates from a pool of nine US FDA-approved drugs. Notably, IDentif.AI-pinpointed ampicillin-sulbactam/cefiderocol and cefiderocol/polymyxin B/rifampicin combinations were able to achieve 93.89 ± 5.95% and 92.23 ± 11.89% inhibition against the bacteria, respectively, and they may diversify the reservoir of treatment options for the indication. In addition, polymyxin B in combination with rifampicin exhibited broadly applicable efficacy and strong synergy across all tested clinical isolates, representing a potential treatment strategy for A. baumannii. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for A. baumannii.
Accelerating crystal structure search through active learning with neural networks for rapid relaxations
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates toward their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si16, Na8Cl8, Ga8As8 and Al4O6 but also excels in finding the most stable minimum for the unseen, more complex systems Si46 and Al16O24. Moreover, we demonstrate at the example of Si16 that our method can find multiple relevant local minima while only adding minor computational effort.
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