<p></p><p>Dear Editor,</p>
<p> </p>
<p>Enclosed you will find the article entitled “A study
of two-dimensional single atom-supported MXenes as hydrogen evolution reaction
catalysts using DFT and machine learning” submitted for consideration to Journal of Materials Chemistry A. </p>
<p> </p>
<p>Existing
studies predominantly focused on the hydrogen evolution reaction (HER) activities
and stabilities of oxygen-terminated MXenes with single-atom loading. However,
to the best of our knowledge, two-dimensional (2D) MXenes with different
terminations (e.g. Br, I, Se, Te, B, Si, P, and NH) have not yet been
investigated for the purposes of HER catalysis. Therefore, in this work, we
considered the combined effect of the different surface terminations (B, NH, O,
F, Si, P, S, Cl, Se, Br, Te, and I) and single atom loading (Ti, V, Fe, Co, Ni,
Cu, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Hf, Ta, W, Re, Os, Ir, Pt, and Au) using
DFT calculation. Gibbs
free energy of hydrogen adsorption (reflecting activity) and the cohesive energy
(a proxy for thermal stability) of these structures (264 in total) were calculated.
We demonstrate that 21 uninvestigated 2D single-atom MXene catalysts, among 264
promising candidates, show an electrocatalytic activity surpassing that of platinum
and a thermal stability surpassing those of synthesized borophene sheet and MoS<sub>2</sub>.
Moreover, all catalysts examined in this work were
further randomly separated into training and test sets with a ratio of 7:3. The HER
electrocatalytic performance and thermal stability of the catalysts in the test
set were predicted by machine learning algorithms. Most importantly, we present a way
to provide a comparable precision (root mean square error values for the
activity and thermal stability predictions are 0.158 eV and 0.02 eV,
respectively) to the published machine learning works by avoiding their
adoption of complex electronic features and the associated high computational
cost, and <i>by only using features that are </i><i>easily available in chemical
repositories</i>. The algorithms used in this work are expected to help future
researchers quickly screen single atom loaded MXenes HER catalysts at the
initial design stage in a cost-effective manner. </p>
<p> </p>
<p>We have no
financial interest in the subject or instrumentation used and there is no known
conflict of interest. </p><br><p></p>
Segment Descriptor Enabling Prediction of Electronic Properties and Photocatalytic Hydrogen Evolution Rate of Alternating Conjugated Copolymers Based on Machine Learning
