Arindam Paul

1.0k total citations · 1 hit paper
15 papers, 699 citations indexed

About

Arindam Paul is a scholar working on Materials Chemistry, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Arindam Paul has authored 15 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Materials Chemistry, 4 papers in Mechanical Engineering and 3 papers in Artificial Intelligence. Recurrent topics in Arindam Paul's work include Machine Learning in Materials Science (6 papers), Spam and Phishing Detection (2 papers) and Titanium Alloys Microstructure and Properties (2 papers). Arindam Paul is often cited by papers focused on Machine Learning in Materials Science (6 papers), Spam and Phishing Detection (2 papers) and Titanium Alloys Microstructure and Properties (2 papers). Arindam Paul collaborates with scholars based in United States, Norway and India. Arindam Paul's co-authors include Ankit Agrawal, Wei‐keng Liao, Alok Choudhary, Dipendra Jha, Logan Ward, Chris Wolverton, Alok Choudhary, Reda Al-Bahrani, Kornel F. Ehmann and Sarah J. Wolff and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and Solar Energy.

In The Last Decade

Arindam Paul

15 papers receiving 677 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
Arindam Paul United States	9	390	221	118	107	76	15	699
Aldair E. Gongora United States	9	229 0.6×	117 0.5×	87 0.7×	57 0.5×	81 1.1×	13	507
G. Lambard Japan	9	395 1.0×	119 0.5×	33 0.3×	143 1.3×	129 1.7×	16	624
Steven K. Kauwe United States	11	743 1.9×	153 0.7×	34 0.3×	185 1.7×	156 2.1×	15	970
John Hogden United States	7	634 1.6×	207 0.9×	29 0.2×	170 1.6×	148 1.9×	17	994
Daylond Hooper United States	7	382 1.0×	98 0.4×	47 0.4×	68 0.6×	104 1.4×	13	581
He Zhao United States	9	245 0.6×	84 0.4×	24 0.2×	64 0.6×	43 0.6×	13	430
Jeroen van Duren United States	5	437 1.1×	82 0.4×	38 0.3×	38 0.4×	259 3.4×	10	596
Dahui Liu China	13	271 0.7×	64 0.3×	71 0.6×	31 0.3×	119 1.6×	32	558
Anthony Wang Germany	5	425 1.1×	82 0.4×	25 0.2×	95 0.9×	109 1.4×	5	554
Peter Starke Germany	18	243 0.6×	596 2.7×	35 0.3×	166 1.6×	45 0.6×	113	1.1k

Countries citing papers authored by Arindam Paul

Since Specialization

Citations

This map shows the geographic impact of Arindam Paul'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 Arindam Paul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arindam Paul more than expected).

Fields of papers citing papers by Arindam Paul

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arindam Paul. 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 Arindam Paul. The network helps show where Arindam Paul may publish in the future.

Co-authorship network of co-authors of Arindam Paul

This figure shows the co-authorship network connecting the top 25 collaborators of Arindam Paul. A scholar is included among the top collaborators of Arindam Paul 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 Arindam Paul. Arindam Paul is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Li, Fa, Qing Zhu, Kunxiaojia Yuan, et al.. (2024). Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method. Earth s Future. 12(10). 3 indexed citations

Paul, Arindam, Fan Yang, Pankaj Verma, et al.. (2024). “Molecular Designs Featuring Cyanobenzene-Decorated Phenazine Acceptor Units for the Highly Efficient Deep-Red Thermally Activated Delayed Fluorescent Emitters”. ACS Applied Optical Materials. 2(11). 2248–2261. 3 indexed citations

Hasan, Mahmudul, Arindam Paul, Vishu Gupta, et al.. (2023). An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems. npj Computational Materials. 9(1). 12 indexed citations

Paul, Arindam, et al.. (2023). Yoga Pose Estimation Using Angle-Based Feature Extraction. Healthcare. 11(24). 3133–3133. 6 indexed citations

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

Paul, Arindam, et al.. (2022). A multi-input multi-label claims channeling system using insurance-based language models. Expert Systems with Applications. 202. 117166–117166. 8 indexed citations

Paul, Arindam, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2021). Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards. PubMed. 5(3). 319–341. 1 indexed citations

Paul, Arindam, et al.. (2021). Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing. Journal of Materials Processing Technology. 302. 117472–117472. 42 indexed citations

Paul, Arindam, et al.. (2021). An attention-driven long short-term memory network for high throughput virtual screening of organic photovoltaic candidate molecules. Solar Energy. 224. 43–50. 9 indexed citations

10.

Paul, Arindam, Al’ona Furmanchuk, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2019). Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees. Molecular Informatics. 38(11-12). e1900038–e1900038. 33 indexed citations

11.

Paul, Arindam, Pınar Acar, Wei‐keng Liao, et al.. (2019). Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation. Computational Materials Science. 160. 334–351. 35 indexed citations

12.

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 →

13.

Paul, Arindam, Pınar Acar, Ruoqian Liu, et al.. (2018). Data Sampling Schemes for Microstructure Design with Vibrational Tuning Constraints. AIAA Journal. 56(3). 1239–1250. 8 indexed citations

14.

Mozaffar, Mojtaba, Arindam Paul, Reda Al-Bahrani, et al.. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters. 18. 35–39. 148 indexed citations

15.

Birnholtz, Jeremy, et al.. (2015). "Is it Weird to Still Be a Virgin". 2613–2622. 45 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.

Explore authors with similar magnitude of impact