High coercivity in large exchange-bias Co/CoO-MgO nano-granular films

We present a detailed study on the magnetic coercivity of Co/CoO-MgO core-shell systems, which exhibits a large exchange bias due to an increase of the uncompensated spin density at the interface between the CoO shell and the metallic Co core by replacing Co by Mg within the CoO shell. We find a large magnetic coercivity of 7120 Oe around the electrical percolation threshold of the Co/CoO core/shell particles, while samples with a smaller or larger Co metal volume fraction show a considerably smaller coercivity. Thus, this study may lead to a route to improving the magnetic properties of artificial magnetic material in view of potential applications.

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development.Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions.However,the internal mechanisms of batteries are sensitive to many factors,such as charging/discharging protocols,manufacturing/storage conditions,and usage patterns.These factors will induce state transitions,thereby decreasing the prediction accuracy of data-driven approaches.Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources.Hence,we develop two transfer learning methods,Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit,to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance.From our results,our transfer learning methods reduce root-mean-squared error by 41%through adapting to the target domain.Furthermore,the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining.These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

Assessing mechanisms for microbial taxa and community dynamics using process models

Disentangling the assembly mechanisms controlling community composition,structure,distribution,functions,and dynamics is a central issue in ecology.Although various approaches have been proposed to examine community assembly mechanisms,quanti-tative characterization is challenging,particularly in microbial ecology.Here,we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations.Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes,we tested the applicability of three ecological models:the consumer–resource model,the neutral model,and the combined model.Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions.The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated.In contrast,the neutral model showed the best performance for rare taxa.Our analysis further indicated that immigration rates decreased with taxa abundance and com-petitions between taxa were strongly correlated with phylogeny,but within a certain phylogenetic distance only.The determinism underlying taxa and community dynamics were quantitatively assessed,showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning.Given its mechanistic basis,the framework developed here is expected to be potentially applicable beyond microbial ecology.

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state(EOS)for crystalline solids describe material behaviors under changes in pressure,volume,entropy and temperature,making them fundamental to scientific research in a wide range of fields including geophysics,energy storage and development of novel materials.Despite over a century of theoretical development and experimental testing of energy–volume(E–V)EOS for solids,there is still a lack of consensus with regard to which equation is indeed optimal,as well as to what metric is most appropriate for making this judgment.In this study,several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature.Our findings do not indicate a clear“best”EOS,but we identify three which consistently perform well relative to the rest of the set.Furthermore,we find that for the aggregate data set,the RMSrD is not strongly correlated with the nature of the compound,e.g.,whether it is a metal,insulator,or semiconductor,nor the bulk modulus for any of the EOS,indicating that a single equation can be used across a broad range of classes of materials.