G. Lambard

2.5k citations

16 papers · 624 indexed · 1 hit paper · h-index 9

Computational Theory and Mathematics top 5%
- Computational Drug Discovery Methods 4
- Advanced Multi-Objective Optimization Algorithms 1
Materials Chemistry top 10%
- Machine Learning in Materials Science 9
Polymers and Plastics
Metals and Alloys
Mechanical Engineering
Nuclear and High Energy Physics
- Astrophysics and Cosmic Phenomena 2
- Dark Matter and Cosmic Phenomena 2
Electronic, Optical and Magnetic Materials
- Magnetic Properties of Alloys 1
Pulmonary and Respiratory Medicine
- Aortic Disease and Treatment Approaches 1
Molecular Biology
- Protein Structure and Dynamics 1

Co-authors: Ryo Yoshida Stephen Wu H. Yamada Keitaro Sodeyama Yibin Xu Junichiro Shiomi Christoph Schick Isao Kuwajima
Cited by: Computational Theory and Mathematics Materials Chemistry Polymers and Plastics
Journals: npj Computational Materials (1 paper)Scientific Reports (1 paper)Advanced Healthcare Materials (1 paper)
Partner nations: Japan Australia Spain

In The Last Decade

G. Lambard

14 papers receiving 603 citations

Hit Papers

align trajectories log scale

Peers

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

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.

Border = papers with G. Lambard Line = papers co-authored together G. Lambard links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

#	Work
1	Exploring the expertise of large language models in materials science and metallurgical engineering Digital Discovery ·Z Afshar-Ghotli,G. Lambard	2025	1
2	MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization Chemometrics and Intelligent Laboratory Systems ·Z Afshar-Ghotli,G. Lambard	2025	0
3	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 ·Cédric Bourgès,G. Lambard,Naoki Sato,Makoto Tachibana,Satoshi Ishii,Takao Mori	2024	7
4	Mining experimental data from materials science literature with large language models: an evaluation study SHILAP Revista de lepidopterología ·Z.H. Meiksin,G. Lambard,Toshiyuki Amagasa,Masashi Ishii	2024	10
5	In Situ Forming Supramolecular Nanofiber Hydrogel as a Biodegradable Liquid Embolic Agent for Postembolization Tissue Remodeling Advanced Healthcare Materials ·Akihiro Nishiguchi,Miho Ohta,Mahdiyeh Bamdad,Svetlana Serhiyenko,Kensaku Mori,Ryuichiro Akagi,Z Afshar-Ghotli,G. Lambard,Keitaro Sodeyama,Tsunemasa Taguchi	2024	1
6	Generation of highly realistic microstructural images of alloys from limited data with a style-based generative adversarial network Scientific Reports ·G. Lambard,S. K. Dhiman,Masahiko Demura	2023	15
7	Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries Cell Reports Physical Science ·Shôichi Matsuda,G. Lambard,Keitaro Sodeyama	2022	40
8	Prediction of the coefficient of linear thermal expansion for the amorphous homopolymers based on chemical structure using machine learning SHILAP Revista de lepidopterología ·Eki Pristiyanto,G. Lambard,Sadaki Samitsu,Keitaro Sodeyama,Ayako Nakata	2021	4
9	Optimization of direct extrusion process for Nd-Fe-B magnets using active learning assisted by machine learning and Bayesian optimization Scripta Materialia ·G. Lambard,Taisuke Sasaki,Keitaro Sodeyama,Tadakatsu Ohkubo,K. Hono	2021	18
10	Mesoporous trimetallic PtPdAu alloy films toward enhanced electrocatalytic activity in methanol oxidation: unexpected chemical compositions discovered by Bayesian optimization Journal of Materials Chemistry A ·Asep Sugih Nugraha,G. Lambard,Jongbeom Na,Md. Shahriar A. Hossain,Toru Asahi,Watcharop Chaikittisilp,Yusuke Yamauchi	2020	49
11	Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithmbreakdown → npj Computational Materials ·Stephen Wu,Sara Ussher,Masa‐aki Kakimoto,Bin Yang,H. Yamada,Isao Kuwajima,G. Lambard,Kenta Hongo,Yibin Xu,Junichiro Shiomi,Christoph Schick,Junko Morikawa,Ryo Yoshida	2019	355
12	SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors arXiv (Cornell University) ·G. Lambard,Eki Pristiyanto	2019	15
13	iQSPR in XenonPy: A Bayesian Molecular Design Algorithm Molecular Informatics ·Stephen Wu,G. Lambard,Chang Liu,H. Yamada,Ryo Yoshida	2019	29
14	Prediction and optimization of epoxy adhesive strength from a small dataset through active learning Science and Technology of Advanced Materials ·Sirawit Pruksawan,G. Lambard,Sadaki Samitsu,Keitaro Sodeyama,Masanobu Naito	2019	78
15	Indirect Search for Dark Matter with the Antares Neutrino Telescope International Cosmic Ray Conference ·Guillaume COHEN,G. Lambard	2016	0
16	Results and prospects of dark matter searches with ANTARES Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment ·J. D. Zornoza,G. Lambard	2013	2

About G. Lambard

G. Lambard is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Nuclear and High Energy Physics, having authored 16 papers that have together received 624 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (4 papers), Astrophysics and Cosmic Phenomena (2 papers), Dark Matter and Cosmic Phenomena (2 papers), Magnetic Properties of Alloys (1 paper), Aortic Disease and Treatment Approaches (1 paper), Protein Structure and Dynamics (1 paper) and Advanced Multi-Objective Optimization Algorithms (1 paper). The work is most often cited by research in Computational Theory and Mathematics (143 citations), Materials Chemistry (395 citations) and Polymers and Plastics (64 citations). G. Lambard has collaborated with scholars based in Japan, Australia and Spain. Frequent co-authors include Ryo Yoshida, Stephen Wu, H. Yamada, Keitaro Sodeyama, Yibin Xu, Junichiro Shiomi, Christoph Schick, Isao Kuwajima, Kenta Hongo and Bin Yang. Their work appears in journals such as npj Computational Materials, Scientific Reports, Advanced Healthcare Materials, Chemometrics and Intelligent Laboratory Systems and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

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.

Explore authors with similar magnitude of impact