Dipendra Jha

1.3k total citations · 1 hit paper
12 papers, 888 citations indexed

About

Dipendra Jha is a scholar working on Materials Chemistry, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Dipendra Jha has authored 12 papers receiving a total of 888 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Artificial Intelligence. Recurrent topics in Dipendra Jha's 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). Dipendra Jha is often cited by papers focused on Machine Learning in Materials Science (9 papers), X-ray Diffraction in Crystallography (6 papers) and Electronic and Structural Properties of Oxides (3 papers). Dipendra Jha collaborates with scholars based in United States, Canada and Philippines. Dipendra Jha's 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 and has published in prestigious journals such as Nature Communications, Acta Materialia and Scientific Reports.

In The Last Decade

Dipendra Jha

12 papers receiving 867 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

2018 334 citations Dipendra Jha, Logan Ward et al. Scientific Reports profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dipendra Jha United States	9	645	171	171	104	87	12	888
Ruoqian Liu United States	9	525 0.8×	112 0.7×	198 1.2×	111 1.1×	132 1.5×	18	819
Tianlu Zhao China	6	683 1.1×	124 0.7×	258 1.5×	250 2.4×	74 0.9×	8	1.1k
Steven K. Kauwe United States	11	743 1.2×	185 1.1×	153 0.9×	156 1.5×	41 0.5×	15	970
Ryan Cohn United States	3	390 0.6×	73 0.4×	127 0.7×	105 1.0×	49 0.6×	6	734
Rongzhi Dong United States	16	418 0.6×	120 0.7×	221 1.3×	126 1.2×	118 1.4×	23	840
Xiaobo Ji China	13	397 0.6×	61 0.4×	143 0.8×	240 2.3×	42 0.5×	33	806
Craig Burkhart United States	17	459 0.7×	69 0.4×	180 1.1×	70 0.7×	265 3.0×	38	1.0k
Thomas Carraro Germany	15	324 0.5×	74 0.4×	92 0.5×	460 4.4×	69 0.8×	34	1.1k
He Zhao United States	9	245 0.4×	64 0.4×	84 0.5×	43 0.4×	89 1.0×	13	430
Fang Ren United States	7	322 0.5×	46 0.3×	217 1.3×	73 0.7×	39 0.4×	11	491

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

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12 of 12 papers shown

1.

Yang, Zijiang, Dipendra Jha, Arindam Paul, et al.. (2022). Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design. Integrating materials and manufacturing innovation. 11(4). 637–647. 12 indexed citations

2.

Jha, Dipendra, Vishu Gupta, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2022). Moving closer to experimental level materials property prediction using AI. Scientific Reports. 12(1). 11953–11953. 20 indexed citations

3.

Jha, Dipendra, Vishu Gupta, Logan Ward, et al.. (2021). Enabling deeper learning on big data for materials informatics applications. Scientific Reports. 11(1). 4244–4244. 48 indexed citations

4.

Al-Bahrani, Reda, Dipendra Jha, Sunwoo Lee, et al.. (2021). SIGRNN: Synthetic Minority Instances Generation in Imbalanced Datasets using a Recurrent Neural Network. 349–356. 2 indexed citations

5.

Jha, Dipendra, Ruifeng Zhang, Denis T. Keane, et al.. (2021). Enhancing Phase Mapping for High-throughput X-ray Diffraction Experiments using Fuzzy Clustering. 507–514. 1 indexed citations

6.

Jha, Dipendra, Kamal Choudhary, Francesca Tavazza, et al.. (2019). Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. 10(1). 5316–5316. 237 indexed citations

7.

Jha, Dipendra, A. Gilad Kusne, Reda Al-Bahrani, et al.. (2019). Peak Area Detection Network for Directly Learning Phase Regions from Raw X-ray Diffraction Patterns. 1–8. 10 indexed citations

8.

Jha, Dipendra, Saransh Singh, Reda Al-Bahrani, et al.. (2018). Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks. Microscopy and Microanalysis. 24(5). 497–502. 48 indexed citations

9.

Jha, Dipendra, Logan Ward, Arindam Paul, et al.. (2018). ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. Scientific Reports. 8(1). 17593–17593. 334 indexed citations breakdown →

10.

Yang, Zijiang, Yuksel C. Yabansu, Dipendra Jha, et al.. (2018). Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches. Acta Materialia. 166. 335–345. 148 indexed citations

11.

Lee, Sunwoo, Dipendra Jha, Ankit Agrawal, Alok Choudhary, & Wei‐keng Liao. (2017). Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication. 183–192. 27 indexed citations

12.

Liu, Ruoqian, Reda Al-Bahrani, Dipendra Jha, et al.. (2016). PinterNet: A thematic label curation tool for large image datasets. 1215. 2353–2362. 1 indexed citations

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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