Mojtaba Haghighatlari

850 total citations
15 papers, 476 citations indexed

About

Mojtaba Haghighatlari is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Mojtaba Haghighatlari has authored 15 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Materials Chemistry, 8 papers in Molecular Biology and 6 papers in Computational Theory and Mathematics. Recurrent topics in Mojtaba Haghighatlari's work include Machine Learning in Materials Science (8 papers), Protein Structure and Dynamics (6 papers) and Computational Drug Discovery Methods (6 papers). Mojtaba Haghighatlari is often cited by papers focused on Machine Learning in Materials Science (8 papers), Protein Structure and Dynamics (6 papers) and Computational Drug Discovery Methods (6 papers). Mojtaba Haghighatlari collaborates with scholars based in United States, Canada and Germany. Mojtaba Haghighatlari's co-authors include Teresa Head‐Gordon, Jie Li, Johannes Hachmann, Farnaz Heidar‐Zadeh, Xingyi Guan, Mohammad Atif Faiz Afzal, Oufan Zhang, Julie D. Forman‐Kay, João M. C. Teixeira and Christopher J. Stein and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry B and The Journal of Physical Chemistry C.

In The Last Decade

Mojtaba Haghighatlari

15 papers receiving 470 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mojtaba Haghighatlari United States 11 316 183 163 58 45 15 476
Alice E. A. Allen United States 9 313 1.0× 109 0.6× 119 0.7× 48 0.8× 32 0.7× 15 466
Pavan Kumar Behara United States 9 206 0.7× 154 0.8× 136 0.8× 32 0.6× 30 0.7× 11 363
Riccardo Petraglia Switzerland 7 263 0.8× 131 0.7× 170 1.0× 47 0.8× 29 0.6× 10 439
Farhad Ramezanghorbani United States 5 252 0.8× 108 0.6× 135 0.8× 60 1.0× 22 0.5× 7 336
Xingyi Guan United States 8 224 0.7× 91 0.5× 122 0.7× 40 0.7× 21 0.5× 12 331
Oufan Zhang United States 7 388 1.2× 158 0.9× 104 0.6× 59 1.0× 26 0.6× 16 591
Joshua T. Horton United Kingdom 9 266 0.8× 191 1.0× 172 1.1× 32 0.6× 55 1.2× 15 451
Adam C. Mater Australia 4 254 0.8× 130 0.7× 177 1.1× 53 0.9× 17 0.4× 6 471
Justin Gilmer United States 8 480 1.5× 196 1.1× 285 1.7× 82 1.4× 46 1.0× 18 742
Jonas A. Finkler Switzerland 6 476 1.5× 127 0.7× 174 1.1× 96 1.7× 22 0.5× 11 524

Countries citing papers authored by Mojtaba Haghighatlari

Since Specialization
Citations

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

Fields of papers citing papers by Mojtaba Haghighatlari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mojtaba Haghighatlari

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

All Works

15 of 15 papers shown
1.
Li, Jie, Oufan Zhang, Yingze Wang, et al.. (2024). Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design. Journal of Chemical Information and Modeling. 64(24). 9082–9097. 2 indexed citations
2.
Zhang, Oufan, Mojtaba Haghighatlari, Jie Li, et al.. (2023). Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data. The Journal of Chemical Physics. 158(17). 26 indexed citations
3.
Teixeira, João M. C., Jie Li, Robert M. Vernon, et al.. (2023). Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states. Biophysical Journal. 122(3). 204a–204a. 1 indexed citations
4.
Teixeira, João M. C., Jie Li, Robert M. Vernon, et al.. (2022). IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States. The Journal of Physical Chemistry A. 126(35). 5985–6003. 39 indexed citations
5.
Naullage, Pavithra M., Mojtaba Haghighatlari, João M. C. Teixeira, et al.. (2022). Protein Dynamics to Define and Refine Disordered Protein Ensembles. The Journal of Physical Chemistry B. 126(9). 1885–1894. 6 indexed citations
6.
Guan, Xingyi, Akshaya Kumar Das, Christopher J. Stein, et al.. (2022). A benchmark dataset for Hydrogen Combustion. Scientific Data. 9(1). 215–215. 18 indexed citations
7.
Haghighatlari, Mojtaba, Jie Li, Xingyi Guan, et al.. (2022). NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1(3). 333–343. 93 indexed citations
8.
Haghighatlari, Mojtaba, Mickaël Krzeminski, João M. C. Teixeira, et al.. (2020). Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states. Communications Chemistry. 3(1). 47 indexed citations
9.
Hanwell, Marcus D., Chris Harris, Mojtaba Haghighatlari, et al.. (2020). Open Chemistry, JupyterLab, REST, and quantum chemistry. International Journal of Quantum Chemistry. 121(1). 8 indexed citations
10.
Haghighatlari, Mojtaba, et al.. (2020). Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods. Chem. 6(7). 1527–1542. 85 indexed citations
11.
Haghighatlari, Mojtaba, et al.. (2020). ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. Wiley Interdisciplinary Reviews Computational Molecular Science. 10(4). 46 indexed citations
12.
Afzal, Mohammad Atif Faiz, et al.. (2019). A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chemical Science. 10(36). 8374–8383. 30 indexed citations
13.
Afzal, Mohammad Atif Faiz, et al.. (2019). Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining. The Journal of Physical Chemistry C. 123(23). 14610–14618. 37 indexed citations
14.
Hachmann, Johannes, et al.. (2018). Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space. Molecular Simulation. 44(11). 921–929. 23 indexed citations
15.
Highfield, James, et al.. (2016). Low-temperature gas–solid carbonation of magnesia and magnesium hydroxide promoted by non-immersive contact with water. RSC Advances. 6(92). 89655–89664. 15 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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