Max Veit
Impact in
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- Machine Learning in Materials Science
- Ferroelectric and Piezoelectric Materials
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 3
- Electronic and Structural Properties of Oxides 1
- X-ray Diffraction in Crystallography 1
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- Advanced Battery Materials and Technologies 1
- Ferroelectric and Negative Capacitance Devices 1
- Co-authors
- Michele Ceriotti (2 shared papers)Michele Kotiuga (1 shared paper)Nicola Marzari (1 shared paper)Giovanni Pizzi (1 shared paper)Detlef Hohl (1 shared paper)Gábor Cśanyi (1 shared paper)Satyanarayana Bonakala (1 shared paper)Indranil Rudra (1 shared paper)
- Journals
- npj Computational Materials (1 paper)Journal of Chemical Theory and Computation (1 paper)Journal of The Electrochemical Society (1 paper)Refubium (Universitätsbibliothek der Freien Universität Berlin) (1 paper)
- Partner nations
- SwitzerlandUnited KingdomSweden
In The Last Decade
Max Veit
4 papers receiving 138 citations
Peers
Comparison fields: 5 of 35
- Materials Chemistry 109
- Computational Theory and Mathematics 27
- Structural Biology 2
- Automotive Engineering 15
- Atomic and Molecular Physics, and Optics 25
Countries citing papers authored by Max Veit
This map shows the geographic impact of Max Veit'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 Max Veit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Veit more than expected).
Fields of papers citing papers by Max Veit
This network shows the impact of papers produced by Max Veit. 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 Max Veit. The network helps show where Max Veit may publish in the future.
Co-authors
The 17 scholars most cited alongside Max Veit, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 52 | |
| 2 | 2019 | 42 | |
| 3 | 2022 | 32 | |
| 4 | 2019 | 17 |
About Max Veit
Max Veit is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Automotive Engineering, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 4 papers that have together received 143 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Advanced Battery Materials and Technologies (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Advanced Battery Technologies Research (1 paper), Computational Drug Discovery Methods (1 paper), Electronic and Structural Properties of Oxides (1 paper), X-ray Diffraction in Crystallography (1 paper) and Phase Equilibria and Thermodynamics (1 paper). The work is most often cited by research in Materials Chemistry (109 citations), Computational Theory and Mathematics (27 citations), Structural Biology (2 citations), Automotive Engineering (15 citations) and Atomic and Molecular Physics, and Optics (25 citations). Max Veit has collaborated with scholars based in Switzerland, United Kingdom and Sweden. Frequent co-authors include Michele Ceriotti, Michele Kotiuga, Nicola Marzari, Giovanni Pizzi, Detlef Hohl, Gábor Cśanyi, Satyanarayana Bonakala, Indranil Rudra, Till Junge and Michael J. Willatt. Their work appears in journals such as npj Computational Materials, Journal of Chemical Theory and Computation, Journal of The Electrochemical Society and Refubium (Universitätsbibliothek der Freien Universität Berlin).
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.