Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia
  Synthesis with Interpretable Models and Molecular Machine Learning   In this work, we experimentally determined the efficacy of several classes of
proton donors for lithium-mediated electrochemical nitrogen reduction in a
tetrahydrofuran-based electrolyte, an attractive alternative method for
producing ammonia. We then built an interpretable data-driven classification
model which identified solvatochromic Kamlet-Taft parameters as important for
distinguishing between active and inactive proton donors. After curating a
dataset for the Kamlet-Taft parameters, we trained a deep learning model to
predict the Kamlet-Taft parameters. The combination of classification model and
deep learning model provides a predictive mapping from a given proton donor to
the ability to produce ammonia. We demonstrate that this combination of
classification model with deep learning is superior to a purely mechanistic or
data-driven approach in accuracy and experimental data efficiency.

18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph