10 research outputs found
Theory of Half-metallic Ferrimagnetism in Double Perovskites
We present a comprehensive theory of the temperature- and disorder-dependence
of half-metallic ferrimagnetism in the double perovskite SrFeMoO (SFMO)
with above room temperature. We show that the magnetization and
conduction electron polarization are both proportional to the
magnetization of localized Fe spins. We derive and validate an
effective spin Hamiltonian, amenable to large-scale three-dimensional
simulations. We show how and are affected by disorder, ubiquitous
in these materials. We suggest a way to enhance in SFMO without
sacrificing polarization
Theory of High T Ferrimagnetism in a Multi-orbital Mott Insulator
We propose a model for the multi-orbital material SrCrOsO (SCOO), an
insulator with remarkable magnetic properties and the highest
K among {\em all} perovskites with a net moment. We derive a new criterion for
the Mott transition using
slave rotor mean field theory, where is the bandwidth and
are the effective Coulomb interactions on Cr(Os)
including Hund's coupling. We show that SCOO is a Mott insulator, where the
large Cr compensates for the small Os .
The spin sector is described by a frustrated antiferromagnetic Heisenberg model
that naturally explains the net moment arising from canting and also the
observed non-monotonic magnetization . We predict characteristic magnetic
structure factor peaks that can be probed by neutron experiments
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Hierarchical forecasting problems arise when time series have a natural group
structure, and predictions at multiple levels of aggregation and disaggregation
across the groups are needed. In such problems, it is often desired to satisfy
the aggregation constraints in a given hierarchy, referred to as hierarchical
coherence in the literature. Maintaining hierarchical coherence while producing
accurate forecasts can be a challenging problem, especially in the case of
probabilistic forecasting. We present a novel method capable of accurate and
coherent probabilistic forecasts for hierarchical time series. We call it Deep
Poisson Mixture Network (DPMN). It relies on the combination of neural networks
and a statistical model for the joint distribution of the hierarchical
multivariate time series structure. By construction, the model guarantees
hierarchical coherence and provides simple rules for aggregation and
disaggregation of the predictive distributions. We perform an extensive
empirical evaluation comparing the DPMN to other state-of-the-art methods which
produce hierarchically coherent probabilistic forecasts on multiple public
datasets. Compared to existing coherent probabilistic models, we obtained a
relative improvement in the overall Continuous Ranked Probability Score (CRPS)
of 11.8% on Australian domestic tourism data, and 8.1% on the Favorita grocery
sales dataset.Comment: Probabilistic Hierarchical Forecasting, Neural Networks, Poisson
Mixtures, Preprint submitted to IJ