Huziel E. Sauceda
- Materials Chemistry top 2%
- Computational Theory and Mathematics top 0.2%
- Molecular Biology top 10%
- Atomic and Molecular Physics, and Optics top 5%
- Electrical and Electronic Engineering top 10%
- Co-authors
- Alexandre TkatchenkoKristof T. SchüttPieter-Jan KindermansK. MüllerKlaus‐Robert MüllerStefan ChmielaIgor PoltavskyOliver T. Unke
- Topics
- Machine Learning in Materials Science (15 papers)Protein Structure and Dynamics (8 papers)Computational Drug Discovery Methods (5 papers)
- Partner nations
- MexicoGermanySouth Korea
In The Last Decade
Huziel E. Sauceda
22 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Materials Chemistry 2.6k
- Computational Theory and Mathematics 1.2k
- Molecular Biology 815
- Atomic and Molecular Physics, and Optics 468
- Electrical and Electronic Engineering 347
Countries citing papers authored by Huziel E. Sauceda
This map shows the geographic impact of Huziel E. Sauceda'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 Huziel E. Sauceda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Huziel E. Sauceda more than expected).
Fields of papers citing papers by Huziel E. Sauceda
This network shows the impact of papers produced by Huziel E. Sauceda. 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 Huziel E. Sauceda. The network helps show where Huziel E. Sauceda may publish in the future.
Co-authorship network of co-authors of Huziel E. Sauceda
This figure shows the co-authorship network connecting the top 25 collaborators of Huziel E. Sauceda. A scholar is included among the top collaborators of Huziel E. Sauceda 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 Huziel E. Sauceda. Huziel E. Sauceda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | Accurate global machine learning force fields for molecules with hundreds of atomsbreakdown → | 112 |
| 4 | 12 | |
| 5 | 54 | |
| 6 | 9 | |
| 7 | SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsbreakdown → | 215 |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 34 | |
| 11 | Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces | 1 |
| 12 | Modeling Molecular Spectra with Interpretable Atomistic Neural Networks | 1 |
| 13 | 160 | |
| 14 | 68 | |
| 15 | 2 | |
| 16 | Machine learning of accurate energy-conserving molecular force fieldsbreakdown → | 810 |
| 17 | 33 | |
| 18 | 38 | |
| 19 | 12 | |
| 20 | 60 |
About Huziel E. Sauceda
Huziel E. Sauceda is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Atomic and Molecular Physics, and Optics, having authored 24 papers that have together received 3.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (15 papers), Protein Structure and Dynamics (8 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Computational Theory and Mathematics (1.2k citations), Materials Chemistry (2.6k citations) and Catalysis (155 citations). Huziel E. Sauceda has collaborated with scholars based in Mexico, Germany and South Korea. Frequent co-authors include Alexandre Tkatchenko, Kristof T. Schütt, Pieter-Jan Kindermans, K. Müller, Klaus‐Robert Müller, Stefan Chmiela, Igor Poltavsky, Oliver T. Unke, Michael Gastegger and Ignacio L. Garzón. Their work appears in journals such as Nature Communications, The Journal of Chemical Physics and Nano Letters.
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.