Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting densities and elastic moduli of SiO2-based glasses by machine
  learning   Chemical design of SiO2-based glasses with high elastic moduli and low weight
is of great interest. However, it is difficult to find a universal expression
to predict the elastic moduli according to the glass composition before
synthesis since the elastic moduli are a complex function of interatomic bonds
and their ordering at different length scales. Here we show that the densities
and elastic moduli of SiO2-based glasses can be efficiently predicted by
machine learning (ML) techniques across a complex compositional space with
multiple (>10) types of additive oxides besides SiO2. Our machine learning
approach relies on a training set generated by high-throughput molecular
dynamic (MD) simulations, a set of elaborately constructed descriptors that
bridges the empirical statistical modeling with the fundamental physics of
interatomic bonding, and a statistical learning/predicting model developed by
implementing least absolute shrinkage and selection operator with a gradient
boost machine (GBM-LASSO). The predictions of the ML model are comprehensively
compared and validated with a large amount of both simulation and experimental
data. By just training with a dataset only composed of binary and ternary glass
samples, our model shows very promising capabilities to predict the density and
elastic moduli for k-nary SiO2-based glasses beyond the training set. As an
example of its potential applications, our GBM-LASSO model was used to perform
a rapid and low-cost screening of many (~105) compositions of a multicomponent
glass system to construct a compositional-property database that allows for a
fruitful overview on the glass density and elastic properties.

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The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas