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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Machine Learning Prediction of Vacancy Formation Energies in CoNiCrFe High-Entropy Alloy: The Role of Atomic Descriptors and Local Chemical Order

2026The Journal of Physical Chemistry CJournal

10.1021/acs.jpcc.5c07217 Full text1 citations

Authors

11 authors across 5 institutions in 4 countries.

Shehu Adam IbrahimFirst author
- Binyaminu Usman Polytechnic (NG)
Jinxue Yang
Tan ShiCorresponding
Chen Zhang
Da Chen
Jing Li
Yang Li
Jeremiah Chinonso Mbazor
Yizhuo Zhang
Zhengxiong SuCorresponding
Chenyang LuSenior authorCorresponding

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

High Entropy Alloys Studieslevel 3 · 0.92
Machine Learning in Materials Sciencelevel 3 · 0.03
Additive Manufacturing Materials and Processeslevel 3 · 0.02

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2026 · The Journal of Physical Chemistry C · 1 citations
https://openalex.org/W7134966147

Machine Learning Prediction of Vacancy Formation Energies in CoNiCrFe High-Entropy Alloy: The Role of Atomic Descriptors and Local Chemical Order · Sankore