As
nanoelectronic synapses, memristive ferroelectric tunnel junctions
(FTJs) have triggered great interest due to the potential applications
in neuromorphic computing for emulating biological brains. Here, we
demonstrate multiferroic FTJ synapses based on the ferroelectric modulation
of spin-filtering BaTiO3/CoFe2O4 composite
barriers. Continuous conductance change with an ON/OFF current ratio
of ∼54 400% and long-term memory with the spike-timing-dependent
plasticity (STDP) of synaptic weight for Hebbian learning are achieved
by controlling the polarization switching of BaTiO3. Supervised
learning simulations adopting the STDP results as database for weight
training are performed on a crossbar neural network and exhibit a
high accuracy rate above 97% for recognition. The polarization switching
also alters the band alignment of CoFe2O4 barrier
relative to the electrodes, giving rise to the change of tunneling
magnetoresistance ratio by about 10 times and even the reversal of
its sign depending upon the resistance states. These results, especially
the electrically switchable spin polarization, provide a new approach
toward multiferroic neuromorphic devices with energy-efficient electrical
manipulations through potential barrier design. In addition, the availability
of spinel ferrite barriers epitaxially grown with ferroelectric oxides
also expends the playground of FTJ devices for a broad scope of applications.
We present numerical results of ground-state energies of 9 molecules in the wellestablished G2 molecule set given by the Gutzwiller conjugate gradient minimization (GCGM) method. The method, beyond the commonly used Gutzwiller approximation, was recently developed based on Gutzwiller variational wave functions. We find that compared to benchmark data given by full configuration interaction, GCGM total energies are reasonably well reproduced with the minimum basis set. To include the dynamical correlation beyond the minimal basis calculations, we adopt the local density approximation for the dynamical correlation energy c E . By comparing the results with benchmark data given by experiments and large-basis configuration interaction, the GCGM total energies with c E are in general better reproduced, but discrepancies are still observed for some dimers.
Abstract:We performed a global search for possible Au-Si crystal structures using genetic algorithm (GA) combined with density functional theory (DFT) calculations. Two Au-Si structures of Au 8 Si 8 and Au 16 Si 8 were found to be energetically stable and have no imaginary frequencies by phonon calculations. The formation energies of all the studied structures gave a convex hull of Au-Si system, showing that the most stable composition was Au-Si = 1:1, and the Si-rich structures were much less stable than the Au-rich ones.
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