Balaranjan Selvaratnam

446 total citations
18 papers, 351 citations indexed

About

Balaranjan Selvaratnam is a scholar working on Materials Chemistry, Renewable Energy, Sustainability and the Environment and Computational Theory and Mathematics. According to data from OpenAlex, Balaranjan Selvaratnam has authored 18 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Materials Chemistry, 4 papers in Renewable Energy, Sustainability and the Environment and 3 papers in Computational Theory and Mathematics. Recurrent topics in Balaranjan Selvaratnam's work include Machine Learning in Materials Science (8 papers), X-ray Diffraction in Crystallography (5 papers) and Advanced Photocatalysis Techniques (4 papers). Balaranjan Selvaratnam is often cited by papers focused on Machine Learning in Materials Science (8 papers), X-ray Diffraction in Crystallography (5 papers) and Advanced Photocatalysis Techniques (4 papers). Balaranjan Selvaratnam collaborates with scholars based in United States, Canada and India. Balaranjan Selvaratnam's co-authors include Ranjit T. Koodali, Hongli Sun, Qingqing Yao, Yangxi Liu, Soumen Payra, Subhash Banerjee, Arijit Saha, Sourav Pal, Sumantra Bhattacharya and Anton O. Oliynyk and has published in prestigious journals such as Chemistry of Materials, Journal of Controlled Release and Physical Chemistry Chemical Physics.

In The Last Decade

Balaranjan Selvaratnam

15 papers receiving 348 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Balaranjan Selvaratnam United States 10 143 113 75 60 54 18 351
Lan She China 10 226 1.6× 139 1.2× 87 1.2× 32 0.5× 59 1.1× 15 420
Xiaorui Yang China 12 196 1.4× 96 0.8× 105 1.4× 47 0.8× 69 1.3× 34 477
Antoine Miche France 12 207 1.4× 114 1.0× 77 1.0× 38 0.6× 37 0.7× 33 423
Yue Xu China 13 164 1.1× 99 0.9× 104 1.4× 36 0.6× 87 1.6× 30 563
Binjie Li China 14 153 1.1× 72 0.6× 124 1.7× 27 0.5× 47 0.9× 41 429
Fengfan Zhu China 10 155 1.1× 52 0.5× 163 2.2× 29 0.5× 45 0.8× 23 340
Xuexue Dong China 11 151 1.1× 91 0.8× 132 1.8× 78 1.3× 15 0.3× 17 326
Xiang Meng China 9 128 0.9× 73 0.6× 149 2.0× 30 0.5× 61 1.1× 26 380
Xiaotong Li China 10 106 0.7× 97 0.9× 26 0.3× 41 0.7× 31 0.6× 30 339

Countries citing papers authored by Balaranjan Selvaratnam

Since Specialization
Citations

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

Fields of papers citing papers by Balaranjan Selvaratnam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Balaranjan Selvaratnam

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

All Works

18 of 18 papers shown
1.
Selvaratnam, Balaranjan, et al.. (2025). Dataset of prototype structures adopted by intermetallic compounds with AB stacking. Data in Brief. 63. 112138–112138.
2.
Selvaratnam, Balaranjan, et al.. (2025). Is There a Simple Descriptor to Predict Laves Phases?. Crystal Growth & Design. 25(3). 849–857. 2 indexed citations
3.
Tyvanchuk, Yu., Sangjoon Lee, Volodymyr Babizhetskyy, et al.. (2025). Materials Informatics Tools to Analyze Crystal Structures: Crystal Structure of the Novel Ternary Indide ErCo2In. Integrating materials and manufacturing innovation. 14(2). 170–187.
4.
5.
Owens‐Baird, Bryan, et al.. (2024). Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning. Chemistry of Materials. 36(12). 6180–6192. 1 indexed citations
6.
Roy, Nilanjan, et al.. (2024). Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning. Inorganic Chemistry. 63(13). 5972–5981. 3 indexed citations
7.
Selvaratnam, Balaranjan, Anton O. Oliynyk, & Arthur Mar. (2023). Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees. Inorganic Chemistry. 62(28). 10865–10875. 9 indexed citations
8.
Selvaratnam, Balaranjan, et al.. (2023). Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX. Chemistry of Materials. 35(3). 879–890. 10 indexed citations
9.
10.
Selvaratnam, Balaranjan, Aravind Baride, Ranjit T. Koodali, et al.. (2021). SnS2/TiO2 Nanocomposites for Hydrogen Production and Photodegradation under Extended Solar Irradiation. Catalysts. 11(5). 589–589. 36 indexed citations
11.
Selvaratnam, Balaranjan & Ranjit T. Koodali. (2020). Machine learning in experimental materials chemistry. Catalysis Today. 371. 77–84. 47 indexed citations
12.
Selvaratnam, Balaranjan, Ranjit T. Koodali, & Pere Miró. (2020). Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network. Journal of Chemical Information and Modeling. 60(4). 1928–1935. 4 indexed citations
13.
Selvaratnam, Balaranjan, Ranjit T. Koodali, & Pere Miró. (2020). Prediction of optoelectronic properties of Cu2O using neural network potential. Physical Chemistry Chemical Physics. 22(26). 14910–14917. 3 indexed citations
14.
Saha, Arijit, Soumen Payra, Balaranjan Selvaratnam, et al.. (2018). Hierarchical Mesoporous RuO2/Cu2O Nanoparticle-Catalyzed Oxidative Homo/Hetero Azo-Coupling of Anilines. ACS Sustainable Chemistry & Engineering. 6(9). 11345–11352. 55 indexed citations
15.
Yao, Qingqing, Yangxi Liu, Balaranjan Selvaratnam, Ranjit T. Koodali, & Hongli Sun. (2018). Mesoporous silicate nanoparticles/3D nanofibrous scaffold-mediated dual-drug delivery for bone tissue engineering. Journal of Controlled Release. 279. 69–78. 118 indexed citations
16.
Selvaratnam, Balaranjan & Ranjit T. Koodali. (2017). TiO2-MgO mixed oxide nanomaterials for solar energy conversion. Catalysis Today. 300. 39–49. 20 indexed citations
17.
Payra, Soumen, Arijit Saha, Chia-Ming Wu, et al.. (2016). Fe–SBA-15 catalyzed synthesis of 2-alkoxyimidazo[1,2-a]pyridines and screening of their in silico selectivity and binding affinity to biological targets. New Journal of Chemistry. 40(11). 9753–9760. 20 indexed citations
18.
Yao, Bin, Praveen Kolla, Ranjit T. Koodali, et al.. (2016). Enzymatic decomposition and electrochemical study of alkali lignin by laccase (Trametes versicolor) in the presence of a natural mediator (methyl syringate). New Journal of Chemistry. 41(3). 958–964. 11 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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