G. Lambard
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- Thermal properties of materials
- X-ray Diffraction in Crystallography
- Catalytic Processes in Materials Science
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 9
-
- Computational Drug Discovery Methods 4
- Co-authors
- Keitaro Sodeyama (5 shared papers)Stephen Wu (2 shared papers)H. Yamada (2 shared papers)Ryo Yoshida (2 shared papers)Christoph Schick (1 shared paper)Bin Yang (1 shared paper)Masa‐aki Kakimoto (1 shared paper)Isao Kuwajima (1 shared paper)
- Journals
- Advanced Healthcare Materials (1 paper)Science and Technology of Advanced Materials (1 paper)Journal of Materials Chemistry A (1 paper)Molecular Informatics (1 paper)ACS Sustainable Chemistry & Engineering (1 paper)
- Partner nations
- JapanSwitzerlandAustralia
In The Last Decade
G. Lambard
14 papers receiving 668 citations
G. Lambard's Hit Papers
Peers
Comparison fields: 5 of 96
- Materials Chemistry 423
- Computational Theory and Mathematics 150
- Polymers and Plastics 67
- Metals and Alloys 10
- Catalysis 25
Countries citing papers authored by G. Lambard
This map shows the geographic impact of G. Lambard'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 G. Lambard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Lambard more than expected).
Fields of papers citing papers by G. Lambard
This network shows the impact of papers produced by G. Lambard. 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 G. Lambard. The network helps show where G. Lambard may publish in the future.
Co-authors
The 25 scholars most cited alongside G. Lambard, 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 | Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm Hit paper breakdown → | 2019 | 385 |
| 2 | 2019 | 84 | |
| 3 | 2020 | 52 | |
| 4 | 2022 | 45 | |
| 5 | 2019 | 30 | |
| 6 | 2021 | 20 | |
| 7 | 2023 | 18 | |
| 8 | 2019 | 18 | |
| 9 | 2024 | 14 | |
| 10 | 2024 | 9 | |
| 11 | 2021 | 5 | |
| 12 | 2013 | 3 | |
| 13 | 2025 | 1 | |
| 14 | 2024 | 1 | |
| 15 | Indirect Search for Dark Matter with the Antares Neutrino Telescope | 2016 | 0 |
| 16 | 2025 | 0 | |
| 17 | 2025 | 0 | |
| 18 | 2025 | 0 |
About G. Lambard
G. Lambard is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Mechanical Engineering, Electrical and Electronic Engineering and Process Chemistry and Technology, having authored 18 papers that have together received 685 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (4 papers), Carbon dioxide utilization in catalysis (2 papers), Astrophysics and Cosmic Phenomena (2 papers), Dark Matter and Cosmic Phenomena (2 papers), Advanced Battery Technologies Research (1 paper), Aortic Disease and Treatment Approaches (1 paper) and Superconducting Materials and Applications (1 paper). The work is most often cited by research in Materials Chemistry (423 citations), Computational Theory and Mathematics (150 citations), Polymers and Plastics (67 citations), Metals and Alloys (10 citations) and Catalysis (25 citations). G. Lambard has collaborated with scholars based in Japan, Switzerland and Australia. Frequent co-authors include Keitaro Sodeyama, Stephen Wu, H. Yamada, Ryo Yoshida, Christoph Schick, Bin Yang, Masa‐aki Kakimoto, Isao Kuwajima, Junko Morikawa and Kenta Hongo. Their work appears in journals such as Advanced Healthcare Materials, Science and Technology of Advanced Materials, Journal of Materials Chemistry A, Molecular Informatics and ACS Sustainable Chemistry & Engineering.
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.