Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method
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Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method is a scholarly article[1].
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APA4ort.xyz Knowledge Graph. (2026). Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method. Retrieved May 24, 2026, from https://4ort.xyz/entity/prediction-of-vacancy-formation-energies-at-tungsten-grain-boundaries-from-local-structure-via-machine-learning-method
MLA“Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/prediction-of-vacancy-formation-energies-at-tungsten-grain-boundaries-from-local-structure-via-machine-learning-method.
BibTeX@misc{4ortxyz_prediction-of-vacancy-formation-energies-at-tungsten-grain-boundaries-from-local-structure-via-machine-learning-method_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method}}, year = {2026}, url = {https://4ort.xyz/entity/prediction-of-vacancy-formation-energies-at-tungsten-grain-boundaries-from-local-structure-via-machine-learning-method}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method — https://4ort.xyz/entity/prediction-of-vacancy-formation-energies-at-tungsten-grain-boundaries-from-local-structure-via-machine-learning-method (retrieved 2026-05-24)
