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Timeline
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Attention Score in Context
Title |
A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout
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Published in |
Solid-State Electronics, January 2023
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DOI | 10.1016/j.sse.2022.108508 |
Authors |
Sung-Ho Lee, Valerio Olevano, Benoit Sklénard |
X Demographics
Geographical breakdown
Country | Count | As % |
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Mexico | 1 | 50% |
France | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2023.
All research outputs
#16,737,737
of 25,392,582 outputs
Outputs from Solid-State Electronics
#1,641
of 1,748 outputs
Outputs of similar age
#253,545
of 475,273 outputs
Outputs of similar age from Solid-State Electronics
#2
of 15 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,748 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 475,273 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.