Sandip De

4.6k total citations · 2 hit papers
39 papers, 2.2k citations indexed

About

Sandip De is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Sandip De has authored 39 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Materials Chemistry, 10 papers in Electrical and Electronic Engineering and 6 papers in Computational Theory and Mathematics. Recurrent topics in Sandip De's work include Machine Learning in Materials Science (18 papers), Computational Drug Discovery Methods (6 papers) and Semiconductor materials and devices (6 papers). Sandip De is often cited by papers focused on Machine Learning in Materials Science (18 papers), Computational Drug Discovery Methods (6 papers) and Semiconductor materials and devices (6 papers). Sandip De collaborates with scholars based in Switzerland, Germany and United States. Sandip De's co-authors include Michele Ceriotti, Gábor Cśanyi, Albert P. Bartók, Carl Poelking, Noam Bernstein, James R. Kermode, Félix Musil, Stefan Goedecker, Graeme M. Day and Jack Yang and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Nature Communications.

In The Last Decade

Sandip De

36 papers receiving 2.1k 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.

Comparing molecules and solids across structural and alchemical space

2016 543 citations Sandip De, Gábor Cśanyi et al. profile →
Machine learning unifies the modeling of materials and molecules

2017 495 citations Sandip De, Gábor Cśanyi et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sandip De Switzerland	17	1.7k	581	393	282	278	39	2.2k
Álvaro Vázquez‐Mayagoitia United States	21	1.0k 0.6×	326 0.6×	418 1.1×	281 1.0×	185 0.7×	45	1.6k
Chenru Duan United States	25	1.4k 0.8×	567 1.0×	238 0.6×	293 1.0×	245 0.9×	60	2.0k
Johannes Hachmann United States	15	941 0.5×	341 0.6×	541 1.4×	484 1.7×	140 0.5×	25	1.7k
John Parkhill United States	18	917 0.5×	344 0.6×	390 1.0×	612 2.2×	222 0.8×	31	1.5k
Benjamin Nebgen United States	21	1.9k 1.1×	968 1.7×	340 0.9×	824 2.9×	686 2.5×	44	2.7k
Huziel E. Sauceda Mexico	13	2.6k 1.5×	1.2k 2.1×	347 0.9×	468 1.7×	815 2.9×	24	3.0k
Roberto Olivares‐Amaya United States	12	786 0.5×	244 0.4×	451 1.1×	463 1.6×	144 0.5×	15	1.5k
Lixin Sun United States	18	2.1k 1.2×	421 0.7×	640 1.6×	192 0.7×	328 1.2×	37	2.6k
Reinhard J. Maurer United Kingdom	27	1.3k 0.8×	199 0.3×	771 2.0×	1.1k 4.0×	131 0.5×	110	2.5k
Benjamin Meyer Switzerland	18	772 0.4×	282 0.5×	86 0.2×	286 1.0×	158 0.6×	27	1.1k

Countries citing papers authored by Sandip De

Since Specialization

Citations

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

Fields of papers citing papers by Sandip De

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sandip De

This figure shows the co-authorship network connecting the top 25 collaborators of Sandip De. A scholar is included among the top collaborators of Sandip De 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 Sandip De. Sandip De 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:

20 of 20 papers shown

Weber, Sebastian, Edvin Fako, Sandip De, et al.. (2025). Copper Gallium Aluminum Mixed Metal Oxides as Alternative Catalyst Candidates for Efficient Conversion of Carbon Dioxide to Methanol and Dimethyl Ether. ACS Catalysis. 15(21). 18857–18876.

Gupta, Srishti, et al.. (2025). MLIPX: machine-learned interatomic potential eXploration. Journal of Physics Condensed Matter. 37(38). 385901–385901. 1 indexed citations

Mendes, Pedro S.F., et al.. (2025). Data as a Key Resource in Catalysis: A Community Account. ChemCatChem. 17(24).

Maulana, Arifin Luthfi, Shuang Han, Shan Yu, et al.. (2025). Stabilizing Ru in Multicomponent Alloy as Acidic Oxygen Evolution Catalysts with Machine Learning-Enabled Structural Insights and Screening. Journal of the American Chemical Society. 147(12). 10268–10278. 14 indexed citations

Mazitov, Arslan, Guillaume Fraux, Marnik Bercx, et al.. (2025). Massive Atomic Diversity: a compact universal dataset for atomistic machine learning. Scientific Data. 12(1). 1857–1857. 2 indexed citations

Midya, A., et al.. (2025). Impact of Annealing Temperature on the Dielectric Properties of SmCrO3. Transactions on Electrical and Electronic Materials. 26(2). 209–215. 1 indexed citations

Gupta, Srishti, Edvin Fako, Imke B. Müller, et al.. (2025). Exploring the Intricacies of Glycerol Hydrodeoxygenation on Copper Surface: A Comprehensive Investigation with the Aid of Machine Learning Force Field. The Journal of Physical Chemistry C. 129(13). 6262–6274. 4 indexed citations

De, Sandip, et al.. (2024). Improved ammonia gas adsorption of surface engineered WS2 nanoflakes. Journal of environmental chemical engineering. 12(5). 113832–113832. 2 indexed citations

De, Sandip, et al.. (2024). Augmented ammonia sensing of ion-beam modified MoSe2. Surfaces and Interfaces. 49. 104394–104394. 1 indexed citations

10.

Mazitov, Arslan, et al.. (2024). Surface segregation in high-entropy alloys from alchemical machine learning. Journal of Physics Materials. 7(2). 25007–25007. 10 indexed citations

11.

Rajbhar, Manoj K., Sandip De, Gopal Sanyal, et al.. (2023). Defect-Engineered 3D Nanostructured MoS₂ for Detection of Ammonia Gas at Room Temperature. ACS Applied Nano Materials. 6(7). 5284–5297. 13 indexed citations

12.

Fraux, Guillaume, et al.. (2023). Modeling high-entropy transition metal alloys with alchemical compression. Physical Review Materials. 7(4). 35 indexed citations

13.

Schaaf, Lars L., Edvin Fako, Sandip De, Ansgar Schäfer, & Gábor Cśanyi. (2023). Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields. npj Computational Materials. 9(1). 51 indexed citations

14.

Fako, Edvin, et al.. (2023). A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO₂ hydrogenation to methanol. Catalysis Science & Technology. 13(9). 2656–2661. 11 indexed citations

15.

Foppa, Lucas, Christopher Sutton, Luca M. Ghiringhelli, et al.. (2022). Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence. ACS Catalysis. 12(4). 2223–2232. 35 indexed citations

16.

De, Sandip, et al.. (2019). Chemical machine learning with kernels: The impact of loss functions. International Journal of Quantum Chemistry. 119(9). 1 indexed citations

17.

Paruzzo, Federico M., Albert Hofstetter, Félix Musil, et al.. (2018). Chemical shifts in molecular solids by machine learning. Nature Communications. 9(1). 4501–4501. 198 indexed citations

18.

Musil, Félix, et al.. (2017). Machine learning for the structure–energy–property landscapes of molecular crystals. Chemical Science. 9(5). 1289–1300. 153 indexed citations

19.

Cartier, E., Takashi Ando, M. Hopstaken, et al.. (2013). Characterization and optimization of charge trapping in high-k dielectrics. 5A.2.1–5A.2.7. 9 indexed citations

20.

De, Sandip, Alexander Willand, Maximilian Amsler, et al.. (2011). Energy Landscape of Fullerene Materials: A Comparison of Boron to Boron Nitride and Carbon. Physical Review Letters. 106(22). 225502–225502. 164 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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