Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Site-specific graph neural network for predicting protonation energy of
  oxygenate molecules   Bio-oil molecule assessment is essential for the sustainable development of
chemicals and transportation fuels. These oxygenated molecules have adequate
carbon, hydrogen, and oxygen atoms that can be used for developing new
value-added molecules (chemicals or transportation fuels). One motivation for
our study stems from the fact that a liquid phase upgrading using mineral acid
is a cost-effective chemical transformation. In this chemical upgrading
process, adding a proton (positively charged atomic hydrogen) to an oxygen atom
is a central step. The protonation energies of oxygen atoms in a molecule
determine the thermodynamic feasibility of the reaction and likely chemical
reaction pathway. A quantum chemical model based on coupled cluster theory is
used to compute accurate thermochemical properties such as the protonation
energies of oxygen atoms and the feasibility of protonation-based chemical
transformations. However, this method is too computationally expensive to
explore a large space of chemical transformations. We develop a graph neural
network approach for predicting protonation energies of oxygen atoms of
hundreds of bioxygenate molecules to predict the feasibility of aqueous acidic
reactions. Our approach relies on an iterative local nonlinear embedding that
gradually leads to global influence of distant atoms and a output layer that
predicts the protonation energy. Our approach is geared to site-specific
predictions for individual oxygen atoms of a molecule in comparison with
commonly used graph convolutional networks that focus on a singular molecular
property prediction. We demonstrate that our approach is effective in learning
the location and magnitudes of protonation energies of oxygenated molecules.

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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: math