91 research outputs found
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
We introduce DeepDFT, a deep learning model for predicting the electronic
charge density around atoms, the fundamental variable in electronic structure
simulations from which all ground state properties can be calculated. The model
is formulated as neural message passing on a graph, consisting of interacting
atom vertices and special query point vertices for which the charge density is
predicted. The accuracy and scalability of the model are demonstrated for
molecules, solids and liquids. The trained model achieves lower average
prediction errors than the observed variations in charge density obtained from
density functional theory simulations using different exchange correlation
functionals.Comment: Workshop paper presented at Machine Learning for Molecules Workshop
at NeurIPS 2020. Implementation and pretrained model are available at
https://github.com/peterbjorgensen/DeepDF
[Transitional strength under plasma] Precise estimations of astrophysically relevant electromagnetic transitions of Ar, Kr, Xe, and Rn under plasma atmosphere
The growing interest in atomic structures of moderately-stripped alkali-like
ions in diagnostic study and modeling of astrophysical and laboratory plasma
makes an accurate many-body study of atomic properties inevitable. This work
presents transition line parameters in the absence or presence of plasma
atmosphere for astrophysically important candidates, Ar, Kr,
Xe, and Rn. We employ relativistic coupled-cluster (RCC) theory,
a well-known correlation exhaustive method. In the case of a plasma
environment, we use Debye Model. Our calculations agree with experiments
available in the literature for ionization potentials, transition strengths of
allowed and forbidden selections, and lifetimes of several low-lying states.
The unit ratios of length and velocity forms of transition matrix elements are
the critical estimation of the accuracy of the transition data presented here,
especially for a few presented first time in the literature. We do compare our
findings with the available recent theoretical results. Our reported data can
be helpful to the astronomer in estimating the density of the plasma
environment around the astronomical objects or in the discovery of
observational spectra corrected by that environment. The present results should
be advantageous in the modeling and diagnostics laboratory plasma, whereas the
calculated ionisation potential depression parameters reveal important
characteristics of atomic structure
Electrochemical Reduction of CO<sub>2</sub> on Ir<i><sub>x</sub></i>Ru<sub>(1–<i>x</i>)</sub>O<sub>2</sub>(110) SurfacesÂ
High
overpotentials and low faradic efficiencies plague metal catalysts
for direct conversion of CO<sub>2</sub> to methanol and other liquid
fuels. RuO<sub>2</sub>-based electrocatalysts have been observed to
evolve methanol at low overpotentials, which has been attributed to
an alternative reaction mechanism with oxygen-coordinated intermediates
that can circumvent the limitations imposed by the scaling relations
on metal catalysts. Here, we introduce an innovative concept of ligand
effects in oxide catalysts. Both IrO<sub>2</sub> and RuO<sub>2</sub> binds OH* and other intermediates from the electrochemical reduction
of CO<sub>2</sub> (CO2RR) strongly, but the stable and miscible system
Ir<sub><i>x</i></sub>Ru<sub>(1‑x)</sub>O<sub>2</sub> exhibits anomalous weaker binding energy in the presence of CO*
spectators, because of Ru–Ir ligand effects. The weakened adsorbate
binding leads to a very low CO2RR onset potential (methanol evolution
at −0.2 V RHE). An Ir atom at the bridge site with Ru neighbors
binds intermediates such as OH* and OCHO* much weaker, because of
synergistic ligand effects and adsorbate–adsorbate interactions.
Consequently, a RuO<sub>2</sub> surface doped with Ir move close to
the top of the predicted CO2RR volcano for oxides, which offers a
significant improvement over state-of-the-art electrocatalysts for
conversion of CO<sub>2</sub> into methanol. Analysis of electronic
structure parameters with adsorbate binding energies indicates the
ligand effect depletes electrons from the Ir atom and shifts the t<sub>2g</sub> orbitals. The lack of electron donation from CO* spectators
to Ir at the active site cause favorable adsorbate binding
Classical molecular dynamics and quantum abs-initio studies on lithium-intercalation in interconnected hollow spherical nano-spheres of amorphous Silicon
A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
- …