Dipendra Jha

1.3k citations
12 papers · 888 indexed · 1 hit paper · h-index 9
Topics
Machine Learning in Materials Science (9 papers)X-ray Diffraction in Crystallography (6 papers)Electronic and Structural Properties of Oxides (3 papers)

In The Last Decade

Dipendra Jha

12 papers receiving 867 citations

Hit Papers

ElemNet: Deep Learning the Chemistry of Materials From On...20182026202020232018100200300

Peers

Dipendra Jha
Comparison fields: 5 of 89
  • Materials Chemistry 645
  • Computational Theory and Mathematics 171
  • Mechanical Engineering 171
  • Electrical and Electronic Engineering 104
  • Mechanics of Materials 87
Replace Tianlu Zhao with:
Tianlu Zhao China
Ruoqian Liu United States
Steven K. Kauwe United States
Ryan Cohn United States
Thomas Carraro Germany
Craig Burkhart United States
Xiaobo Ji China
Fang Ren United States
Norman Jin United States
Rongzhi Dong United States
Dipendra Jha relative to Tianlu Zhao China Tianlu Zhao's profile →
Citations per field
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Tianlu Zhao · 1×
Citations per year

Countries citing papers authored by Dipendra Jha

Since Specialization
Citations

This map shows the geographic impact of Dipendra Jha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dipendra Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dipendra Jha more than expected).

Fields of papers citing papers by Dipendra Jha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dipendra Jha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dipendra Jha. The network helps show where Dipendra Jha may publish in the future.

Co-authorship network of co-authors of Dipendra Jha

This figure shows the co-authorship network connecting the top 25 collaborators of Dipendra Jha. A scholar is included among the top collaborators of Dipendra Jha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dipendra Jha. Dipendra Jha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
#WorkIndexed citations
1 12
2 20
3 48
4 2
5 1
6 237
7 10
8 48
9
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Compositionbreakdown →
334
10 148
11 27
12 1

About Dipendra Jha

Dipendra Jha is a scholar working on Metals and Alloys, Materials Chemistry and Media Technology, having authored 12 papers that have together received 888 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), X-ray Diffraction in Crystallography (6 papers) and Electronic and Structural Properties of Oxides (3 papers). The work is most often cited by research in Metals and Alloys (54 citations), Materials Chemistry (645 citations) and Computational Theory and Mathematics (171 citations). Dipendra Jha has collaborated with scholars based in United States, Canada and Philippines. Frequent co-authors include Ankit Agrawal, Wei‐keng Liao, Alok Choudhary, Logan Ward, Arindam Paul, Chris Wolverton, Alok Choudhary, Francesca Tavazza, Carelyn E. Campbell and Kamal Choudhary. Their work appears in journals such as Nature Communications, Acta Materialia and Scientific Reports.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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