G. Lambard

2.5k total citations · 1 hit paper
16 papers, 624 citations indexed

About

G. Lambard is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Mechanical Engineering. According to data from OpenAlex, G. Lambard has authored 16 papers receiving a total of 624 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 5 papers in Computational Theory and Mathematics and 3 papers in Mechanical Engineering. Recurrent topics in G. Lambard's work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (4 papers) and Astrophysics and Cosmic Phenomena (2 papers). G. Lambard is often cited by papers focused on Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (4 papers) and Astrophysics and Cosmic Phenomena (2 papers). G. Lambard collaborates with scholars based in Japan, Australia and United States. G. Lambard's co-authors include H. Yamada, Stephen Wu, Keitaro Sodeyama, Ryo Yoshida, Isao Kuwajima, Yibin Xu, Junichiro Shiomi, Christoph Schick, Junko Morikawa and Bin Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Acta Materialia and Scientific Reports.

In The Last Decade

G. Lambard

14 papers receiving 603 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

2019 355 citations Stephen Wu, Masa‐aki Kakimoto et al. npj Computational Materials profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
G. Lambard Japan	9	395	143	129	119	75	16	624
Xun Jiang China	9	507 1.3×	136 1.0×	110 0.9×	107 0.9×	107 1.4×	28	921
Xiaobo Ji China	13	397 1.0×	61 0.4×	240 1.9×	143 1.2×	105 1.4×	33	806
Steven K. Kauwe United States	11	743 1.9×	185 1.3×	156 1.2×	153 1.3×	107 1.4×	15	970
Tianlu Zhao China	6	683 1.7×	124 0.9×	250 1.9×	258 2.2×	119 1.6×	8	1.1k
Rick Barto United States	6	285 0.7×	47 0.3×	137 1.1×	53 0.4×	101 1.3×	7	478
Hermann Tribukait Switzerland	5	461 1.2×	90 0.6×	223 1.7×	63 0.5×	137 1.8×	6	715
Yunjian Wu China	17	360 0.9×	82 0.6×	164 1.3×	261 2.2×	108 1.4×	55	807
Jiazhen He China	16	315 0.8×	296 2.1×	67 0.5×	96 0.8×	76 1.0×	56	980
Wenbo Sun China	9	307 0.8×	61 0.4×	274 2.1×	51 0.4×	35 0.5×	21	538
Ryan Murdock United States	6	493 1.2×	121 0.8×	115 0.9×	89 0.7×	74 1.0×	8	694

Countries citing papers authored by G. Lambard

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of G. Lambard

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

All Works

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16 of 16 papers shown

Lambard, G., et al.. (2025). Exploring the expertise of large language models in materials science and metallurgical engineering. Digital Discovery. 4(2). 500–512. 1 indexed citations

Lambard, G., et al.. (2025). MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization. Chemometrics and Intelligent Laboratory Systems. 258. 105323–105323.

Bourgès, Cédric, G. Lambard, Naoki Sato, et al.. (2024). Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development. Acta Materialia. 281. 120342–120342. 7 indexed citations

Lambard, G., et al.. (2024). Mining experimental data from materials science literature with large language models: an evaluation study. SHILAP Revista de lepidopterología. 4(1). 10 indexed citations

Nishiguchi, Akihiro, Miho Ohta, Kensaku Mori, et al.. (2024). In Situ Forming Supramolecular Nanofiber Hydrogel as a Biodegradable Liquid Embolic Agent for Postembolization Tissue Remodeling. Advanced Healthcare Materials. 14(4). e2403784–e2403784. 1 indexed citations

Lambard, G., et al.. (2023). Generation of highly realistic microstructural images of alloys from limited data with a style-based generative adversarial network. Scientific Reports. 13(1). 566–566. 15 indexed citations

Matsuda, Shôichi, G. Lambard, & Keitaro Sodeyama. (2022). Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries. Cell Reports Physical Science. 3(4). 100832–100832. 40 indexed citations

Lambard, G., et al.. (2021). Prediction of the coefficient of linear thermal expansion for the amorphous homopolymers based on chemical structure using machine learning. SHILAP Revista de lepidopterología. 1(1). 213–224. 4 indexed citations

Lambard, G., Taisuke Sasaki, Keitaro Sodeyama, Tadakatsu Ohkubo, & K. Hono. (2021). Optimization of direct extrusion process for Nd-Fe-B magnets using active learning assisted by machine learning and Bayesian optimization. Scripta Materialia. 209. 114341–114341. 18 indexed citations

10.

Nugraha, Asep Sugih, G. Lambard, Jongbeom Na, et al.. (2020). Mesoporous trimetallic PtPdAu alloy films toward enhanced electrocatalytic activity in methanol oxidation: unexpected chemical compositions discovered by Bayesian optimization. Journal of Materials Chemistry A. 8(27). 13532–13540. 49 indexed citations

11.

Wu, Stephen, Masa‐aki Kakimoto, Bin Yang, et al.. (2019). Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Computational Materials. 5(1). 355 indexed citations breakdown →

12.

Lambard, G., et al.. (2019). SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors. arXiv (Cornell University). 15 indexed citations

13.

Wu, Stephen, G. Lambard, Chang Liu, H. Yamada, & Ryo Yoshida. (2019). iQSPR in XenonPy: A Bayesian Molecular Design Algorithm. Molecular Informatics. 39(1-2). e1900107–e1900107. 29 indexed citations

14.

Pruksawan, Sirawit, G. Lambard, Sadaki Samitsu, Keitaro Sodeyama, & Masanobu Naito. (2019). Prediction and optimization of epoxy adhesive strength from a small dataset through active learning. Science and Technology of Advanced Materials. 20(1). 1010–1021. 78 indexed citations

15.

Lambard, G., et al.. (2016). Indirect Search for Dark Matter with the Antares Neutrino Telescope. International Cosmic Ray Conference. 33. 613.

16.

Zornoza, J. D. & G. Lambard. (2013). Results and prospects of dark matter searches with ANTARES. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 742. 173–176. 2 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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