A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites
National Science Open, ISSN: 2097-1168
2024
- 2Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures2
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- Mentions2
- News Mentions2
- News2
Most Recent News
AI approach enhances efficiency of material multiscale simulation for wearable electronics
The integration of microscale and macroscale simulations has long been a computational challenge in material science. Addressing this, researchers have developed AGAT, a machine learning model that efficiently predicts the behaviors of materials used in wearable electronics, particularly focusing on CNTs/PDMS composites.
Article Description
National Science Open
Bibliographic Details
Science China Press., Co. Ltd.
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