Apurba Nandi

1.1k total citations
46 papers, 780 citations indexed

About

Apurba Nandi is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Spectroscopy. According to data from OpenAlex, Apurba Nandi has authored 46 papers receiving a total of 780 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Atomic and Molecular Physics, and Optics, 18 papers in Materials Chemistry and 17 papers in Spectroscopy. Recurrent topics in Apurba Nandi's work include Advanced Chemical Physics Studies (26 papers), Spectroscopy and Quantum Chemical Studies (20 papers) and Machine Learning in Materials Science (17 papers). Apurba Nandi is often cited by papers focused on Advanced Chemical Physics Studies (26 papers), Spectroscopy and Quantum Chemical Studies (20 papers) and Machine Learning in Materials Science (17 papers). Apurba Nandi collaborates with scholars based in United States, Italy and Luxembourg. Apurba Nandi's co-authors include Joel M. Bowman, Chen Qu, Paul L. Houston, Riccardo Conte, Qi Yu, Shridhar R. Gadre, Priyanka Pandey, Nityananda Sahu, Bina Fu and Francesco A. Evangelista and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Apurba Nandi

44 papers receiving 766 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Apurba Nandi United States 17 496 448 199 162 119 46 780
Michael J. Willatt Switzerland 8 200 0.4× 417 0.9× 210 1.1× 75 0.5× 88 0.7× 11 592
Edgar A. Engel United Kingdom 11 239 0.5× 425 0.9× 93 0.5× 56 0.3× 78 0.7× 16 644
Riccardo Conte Italy 25 1.1k 2.3× 538 1.2× 210 1.1× 469 2.9× 170 1.4× 81 1.5k
Marc Riera United States 19 624 1.3× 413 0.9× 40 0.2× 142 0.9× 177 1.5× 24 812
Kejie Shao China 9 376 0.8× 231 0.5× 63 0.3× 132 0.8× 40 0.3× 11 488
Debasish Koner India 14 263 0.5× 162 0.4× 65 0.3× 136 0.8× 73 0.6× 38 472
Duminda S. Ranasinghe United States 13 241 0.5× 253 0.6× 104 0.5× 45 0.3× 33 0.3× 23 497
Kurt R. Brorsen United States 15 678 1.4× 175 0.4× 32 0.2× 248 1.5× 66 0.6× 28 816
Félix Musil Switzerland 12 92 0.2× 637 1.4× 254 1.3× 146 0.9× 154 1.3× 17 869
Kaushik Nanda United States 16 494 1.0× 333 0.7× 43 0.2× 164 1.0× 49 0.4× 30 850

Countries citing papers authored by Apurba Nandi

Since Specialization
Citations

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

Fields of papers citing papers by Apurba Nandi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Apurba Nandi

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

All Works

20 of 20 papers shown
1.
Nandi, Apurba, et al.. (2025). Stable isotope equilibria in the dihydrogen-water-methane-ethane-propane system. Part 1: Path-integral calculations with CCSD(T) quality potentials. Geochimica et Cosmochimica Acta. 396. 71–90. 1 indexed citations
2.
Nandi, Apurba, Riccardo Conte, Priyanka Pandey, et al.. (2025). Quantum Nature of Ubiquitous Vibrational Features Revealed for Ethylene Glycol. Journal of Chemical Theory and Computation. 21(10). 5208–5220. 4 indexed citations
3.
Yu, Qi, Chen Qu, Riccardo Conte, et al.. (2025). Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials. Nature Computational Science. 5(5). 418–426. 2 indexed citations
4.
Nandi, Apurba, et al.. (2025). Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema. Scientific Reports. 15(1). 4569–4569. 4 indexed citations
5.
Qu, Chen, Paul L. Houston, Riccardo Conte, et al.. (2025). Revisiting the H5O2+ IR Spectrum with VSCF/VCI and the Influence of Mark Johnson’s Experiments in Advancing the Theory of Protonated Water Clusters. The Journal of Physical Chemistry A. 129(31). 7051–7060. 1 indexed citations
6.
Banerjee, Sourav, et al.. (2025). A multilayer deep neural network framework for hemodynamic assessment of cognitive load management during problem-solving tasks. Cognitive Neurodynamics. 19(1). 104–104. 1 indexed citations
7.
Nandi, Apurba, et al.. (2024). Cluster Shape Detection Using Dual Attention Induced Convolutional-Clustering Algorithm. 1–6. 1 indexed citations
8.
Nandi, Apurba, Priyanka Pandey, Paul L. Houston, et al.. (2024). Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol. Journal of Chemical Theory and Computation. 20(20). 8807–8819. 9 indexed citations
9.
Houston, Paul L., Chen Qu, Qi Yu, et al.. (2024). Formic Acid–Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer. Journal of Chemical Theory and Computation. 20(5). 1821–1828. 5 indexed citations
10.
Henkel, Stefan, Gerhard Schwaab, Apurba Nandi, et al.. (2024). On the nature of hydrogen bonding in the H2S dimer. Nature Communications. 15(1). 9540–9540. 9 indexed citations
11.
Houston, Paul L., Chen Qu, Qi Yu, et al.. (2024). A New A Priori Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation. The Journal of Physical Chemistry A. 128(2). 479–487. 3 indexed citations
12.
Houston, Paul L., Chen Qu, Qi Yu, et al.. (2024). No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials. Journal of Chemical Theory and Computation. 20(8). 3008–3018. 8 indexed citations
13.
Pandey, Priyanka, Chen Qu, Apurba Nandi, et al.. (2024). Ab Initio Potential Energy Surface for NaCl–H2 with Correct Long-Range Behavior. The Journal of Physical Chemistry A. 128(5). 902–908. 5 indexed citations
14.
Qu, Chen, Paul L. Houston, Qi Yu, et al.. (2023). Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations. The Journal of Chemical Physics. 159(7). 6 indexed citations
15.
Nandi, Apurba, Chen Qu, Riccardo Conte, et al.. (2023). Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands. Journal of the American Chemical Society. 145(17). 9655–9664. 24 indexed citations
16.
Bowman, Joel M., Chen Qu, Riccardo Conte, et al.. (2022). The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials. The Journal of Chemical Physics. 156(24). 240901–240901. 25 indexed citations
17.
Bowman, Joel M., Chen Qu, Riccardo Conte, et al.. (2022). Δ-Machine Learned Potential Energy Surfaces and Force Fields. Journal of Chemical Theory and Computation. 19(1). 1–17. 56 indexed citations
18.
Nandi, Apurba, Peng Zhang, Jun Chen, Hua Guo, & Joel M. Bowman. (2021). Quasiclassical simulations based on cluster models reveal vibration-facilitated roaming in the isomerization of CO adsorbed on NaCl. Nature Chemistry. 13(3). 249–254. 11 indexed citations
19.
Qu, Chen, Paul L. Houston, Riccardo Conte, Apurba Nandi, & Joel M. Bowman. (2021). Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone. The Journal of Physical Chemistry Letters. 12(20). 4902–4909. 58 indexed citations
20.
Nandi, Apurba, Chen Qu, & Joel M. Bowman. (2019). Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations. Journal of Chemical Theory and Computation. 15(5). 2826–2835. 57 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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