Valentín Vassilev-Galindo
- Materials Chemistry top 10%
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Inorganic Chemistry top 10%
- Atomic and Molecular Physics, and Optics
- Co-authors
- Alexandre TkatchenkoStefan ChmielaKlaus‐Robert MüllerJohn A. KeithBingqing ChengMichael GasteggerSudip PanGabriel Merino
- Topics
- Machine Learning in Materials Science (8 papers)Computational Drug Discovery Methods (6 papers)Protein Structure and Dynamics (4 papers)
- Partner nations
- LuxembourgSpainUnited States
In The Last Decade
Valentín Vassilev-Galindo
12 papers receiving 991 citations
Hit Papers
Peers
Comparison fields: 5 of 105
- Materials Chemistry 661
- Computational Theory and Mathematics 276
- Molecular Biology 187
- Inorganic Chemistry 140
- Atomic and Molecular Physics, and Optics 131
Countries citing papers authored by Valentín Vassilev-Galindo
This map shows the geographic impact of Valentín Vassilev-Galindo'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 Valentín Vassilev-Galindo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Valentín Vassilev-Galindo more than expected).
Fields of papers citing papers by Valentín Vassilev-Galindo
This network shows the impact of papers produced by Valentín Vassilev-Galindo. 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 Valentín Vassilev-Galindo. The network helps show where Valentín Vassilev-Galindo may publish in the future.
Co-authorship network of co-authors of Valentín Vassilev-Galindo
This figure shows the co-authorship network connecting the top 25 collaborators of Valentín Vassilev-Galindo. A scholar is included among the top collaborators of Valentín Vassilev-Galindo 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 Valentín Vassilev-Galindo. Valentín Vassilev-Galindo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 27 | |
| 3 | 19 | |
| 4 | 13 | |
| 5 | Accurate global machine learning force fields for molecules with hundreds of atomsbreakdown → | 112 |
| 6 | 19 | |
| 7 | Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systemsbreakdown → | 546 |
| 8 | Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning | 18 |
| 9 | 31 | |
| 10 | 152 | |
| 11 | 58 | |
| 12 | 4 |
About Valentín Vassilev-Galindo
Valentín Vassilev-Galindo is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Catalysis, having authored 12 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (6 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (276 citations), Materials Chemistry (661 citations) and Inorganic Chemistry (140 citations). Valentín Vassilev-Galindo has collaborated with scholars based in Luxembourg, Spain and United States. Frequent co-authors include Alexandre Tkatchenko, Stefan Chmiela, Klaus‐Robert Müller, John A. Keith, Bingqing Cheng, Michael Gastegger, Sudip Pan, Gabriel Merino, Kelling J. Donald and Igor Poltavsky. Their work appears in journals such as Chemical Reviews, Nature Communications and The Journal of Chemical Physics.
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.