A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests

Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms

Film Fabrication of Perovskites and their Derivatives for Photovoltaic Applications via Chemical Vapor Deposition

In recent decades, metal halide perovskites have attracted much attention after showing great potential in photovoltaic (PV) applications. With the rapid progress of perovskites, various thin-film fabrication methods have been studied intensively. However, a film deposition method with controllability, cost efficiency, scalability, and uniformity is required to obtain perovskite films with the desired morphologies and properties and achieve large-scale manufacture. Chemical vapor deposition (CVD) stands out among the various deposition methods because of its unique advantages. In this review, perovskite films for PV applications deposited by diverse CVD methods are discussed, and a summary of the development and investigation of CVD processes utilized is provided

Performance comparison of abrasion resistant textile motorcycle clothing

 Falling at speed onto a tarmac surface during cycling can cause abrasion and laceration of the skin and body tissue. Motorcycle clothing designed to reduce or avoid this type of injury has traditionally been made of animal leather as it has well known resistance to abrasion. In the last 20 years there has been an emergence of textile clothing reinforced with high performance/tenacity fibres such as those made from polyamides, aramids, ultra high molecular weight polyethylene and liquid crystal. Almost no comparative work has been undertaken to provide insight into the level of protection these clothing layers can provide.This work has used a CE standard test method to evaluate a number of abrasion resistant textile pant products and compare them with a leather race product. It analysed the protective fabric layer structure for mass, thickness, construction method and resistance to abrasion.Structures manufactured from high tenacity fibres performed better than those from lower tenacity ones. Fabric construction method and mass per unit area were the two key variables in providing an abrasion protective layer. Structures manufactured from knitted para-aramid fibres performed better than their woven counterparts due to the method of fabric failure. Several well designed protective layers performed at a similar level to that of leather; however, most garments tested failed to meet the lower level European standard of abrasion resistance (CE level 1), which may put their wearer at risk in the advent of a collision

A study on the interacting Ricci dark energy in f(R,T)f(R,T) gravity

The present work reports study on the interacting Ricci dark energy in a modified gravity theory named $f(R,T)$ gravity. The specific model $f(R,T)=\mu R+\nu T$ (proposed by R. Myrzakulov, arXiv:1205.5266v2) is considered here. For this model we have observed a quintom-like behavior of the equation of state (EoS) parameter and a transition from matter dominated to dark energy density has been observed through fraction density evolution. The statefinder parameters reveal that the model interpolates between dust and $\Lambda$CDM phases of the universe.Comment: 12 pages, 5 figure

Editorial: Technology for Higher Education, Adult Learning and Professional Development

The basis of competition has shifted more towards the assimilation and creation of knowledge in the fiercely competitive and evolving digital age. Learning has therefore become crucial for sustainable development and innovation across individual, organizational, and community levels. Papers in this special issue are representative of ongoing research on integration of technology with learning and knowledge management in higher education institutions and organizational and community environments.published_or_final_versio

Lessons from silkworm cocoons for future protective materials design

 Evolved over millions of years’ natural selection, very thin and lightweight wild silkworm cocoons can protect silkworms from environmental hazards and physical attacks from predators while supporting their metabolic activity. The knowledge of structure-property-function relationship of multi-layered composite silk cocoon shells gives insight into the design of next-generation protection materials. The mechanical and thermal insulation properties of both domestic (Bombyx mori, or B. moriand Samia. cynthia, or S. cynthia) and wild (Antheraea pernyi and Antheraea mylitta, or A. pernyi and A. mylitta) silkworm cocoons were investigated. The research findings are of relevance to the bio-inspired design of new protective materials and structures. The 180 degree peel tests and needle penetration tests were used for examining the peel resistance and needle penetration resistance of both domestic and wild silkworm cocoon walls. The temperatures inside and outside of the whole silkworm cocoons under warm, cold and windy conditions were monitored for investigating the cocoon’s thermal insulation function. Computational fluid dynamics (CFD) models were created to simulate the heat transfer through the A. pernyi cocoon wall. The wild cocoons experienced much higher peeling peak loads than the domestic cocoon. This transfers to a maximum work-of-fracture (WOF) of about 1000 J/m2 from the A. pernyi outer layer, which was 10 times of the B. mori cocoon. The A. pernyi wild cocoon exhibited a maximum penetration force (11 N) that is 70 % higher than a woven aramid fabric. Silk sericin is shown to play a critical role in providing needle penetration resistance of the non-woven composite cocoon structure by restricting the relative motion of fibres, which prevents the sharp tip of the needle from pushing aside fibres and penetrating between them. The wild A. pernyi cocoon exhibits superior thermal buffer over the domestic B. mori cocoon. The unique structure of the A. pernyi cocoon wall with mineral crystals deposited on the cocoon outer surface, can prohibit most of the air from flowing inside of the cocoon structure, which shows strong wind resistance under windy conditions

Electrochemical Investigation of Phenethylammonium Bismuth Iodide as Anode in Aqueous Zn2+ Electrolytes

Despite the high potential impact of aqueous battery systems, fundamental characteristics such as cost, safety, and stability make them less feasible for large-scale energy storage systems. One of the main barriers encountered in the commercialization of aqueous batteries is the development of large-scale electrodes with high reversibility, high rate capability, and extended cycle stability at low operational and maintenance costs. To overcome some of these issues, the current research work is focused on a new class of material based on phenethylammonium bismuth iodide on fluorine doped SnO2-precoated glass substrate via aerosol-assisted chemical vapor deposition, a technology that is industrially competitive. The anode materials were electrochemically investigated in Zn2+ aqueous electrolytes as a proof of concept, which presented a specific capacity of 220 mAh g−1 at 0.4 A g−1 with excellent stability after 50 scans and capacity retention of almost 100%

Imaging, Tracking and Computational Analyses of Virus Entry and Egress with the Cytoskeleton

Viruses have a dual nature: particles are “passive substances” lacking chemical energy transformation, whereas infected cells are “active substances” turning-over energy. How passive viral substances convert to active substances, comprising viral replication and assembly compartments has been of intense interest to virologists, cell and molecular biologists and immunologists. Infection starts with virus entry into a susceptible cell and delivers the viral genome to the replication site. This is a multi-step process, and involves the cytoskeleton and associated motor proteins. Likewise, the egress of progeny virus particles from the replication site to the extracellular space is enhanced by the cytoskeleton and associated motor proteins. This overcomes the limitation of thermal diffusion, and transports virions and virion components, often in association with cellular organelles. This review explores how the analysis of viral trajectories informs about mechanisms of infection. We discuss the methodology enabling researchers to visualize single virions in cells by fluorescence imaging and tracking. Virus visualization and tracking are increasingly enhanced by computational analyses of virus trajectories as well as in silico modeling. Combined approaches reveal previously unrecognized features of virus-infected cells. Using select examples of complementary methodology, we highlight the role of actin filaments and microtubules, and their associated motors in virus infections. In-depth studies of single virion dynamics at high temporal and spatial resolutions thereby provide deep insight into virus infection processes, and are a basis for uncovering underlying mechanisms of how cells function

Tetra- and Penta-Acylated Lipid A Structures of Porphyromonas gingivalis LPS Differentially Activate TLR4-Mediated NF-κB Signal Transduction Cascade and Immuno-Inflammatory Response in Human Gingival Fibroblasts

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