Polarizabilities
play significant roles in describing dispersive
and inductive interactions of the atom and molecular systems. However,
an accurate prediction of molecular polarizabilities from first principles
is computationally prohibitive. Although physical models or statistical
machine learning models have been proposed, either a lack of accurate
description of local chemical environments or demanding a large number
of samples for training has limited their practical applications.
In this study, we combine a physically inspired dipole interaction
model and an accurate neural network method for predicting the polarizability
tensors of molecules. With the local chemical environment precisely
described and the requirement of rotational covariance naturally fulfilled,
this hybrid model is proven to give an accurate molecular polarizability
prediction, essentially reducing the number of training samples. The
atomic polarizabilities are physically interpretable and transferable
to larger molecules unseen in the training set. This promising method
may find its wide range of applications, such as spectroscopic simulations
and the construction of polarizable force fields.
Abstract-This letter presents a broadband circularly polarized (CP) crossed dipole antenna with wide axial ratio (AR) beamwidth. The antenna consists of a crossed dipole fed by two baluns, a wideband feed network and a cylindrical metallic cavity. To broaden the beamwidth, circular arms are introduced. Meanwhile, the metallic cavity is utilized to broaden the AR beamwidth. Measurements show that the antenna has an impedance bandwidth of 70.6% (1.87-3.91 GHz) for voltage standing wave ratio (VSWR) ≤ 2 and a 3-dB AR bandwidth of 62.4% (1.92-3.66 GHz). In addition, the 3-dB AR beamwidth of the antenna is larger than 100 • , and the gain varies from 4 dBic to 6 dBic over the whole CP operation bandwidth. Owing to the high-gain and wideband operation, the proposed CP antenna is potentially capable for satellite applications and high-gain applications.
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