• Corpus ID: 227127004

Discovery of materials with extreme work functions by high-throughput density functional theory and machine learning

@article{Schindler2020DiscoveryOM,
  title={Discovery of materials with extreme work functions by high-throughput density functional theory and machine learning},
  author={Peter Schindler and Evan R. Antoniuk and Gowoon Cheon and Yanbing Zhu and Evan J Reed},
  journal={arXiv: Materials Science},
  year={2020}
}
The work function is the key surface property that determines how much energy is required for an electron to escape the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. Data-driven predictions of bulk material properties have been widely explored and work functions of elemental crystals have been studied thoroughly. However, the work functions of more complex compounds have not been investigated… 
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References

SHOWING 1-10 OF 53 REFERENCES
Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
TLDR
It is shown that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points, and is encouraged to encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.
{m
TLDR
The master programme in Applied Geology aims to provide comprehensive knowledge based on various branches of Geology, with special focus on Applied geology subjects in the areas of Geomorphology, Structural geology, Hydrogeology, Petroleum Geologists, Mining Geology), Remote Sensing and Environmental geology.
NOMAD: The FAIR concept for big data-driven materials science
<jats:p><jats:fig position="anchor"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" mime-subtype="jpeg" mimetype="image" position="float" xlink:type="simple"
Scientific Data 3
  • 160080
  • 2016
S
  • Lee, and S.-h. Jhi, Journal of Physics: Condensed Matter 29, 315702
  • 2017
Computational Materials Science X
Journal of Chemical Information and Modeling
Nature communications 4
  • 1576
  • 2013
...
...