Sae Dieb

772 total citations
23 papers, 246 citations indexed

About

Sae Dieb is a scholar working on Materials Chemistry, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Sae Dieb has authored 23 papers receiving a total of 246 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Materials Chemistry, 8 papers in Artificial Intelligence and 6 papers in Molecular Biology. Recurrent topics in Sae Dieb's work include Machine Learning in Materials Science (11 papers), Biomedical Text Mining and Ontologies (4 papers) and Topic Modeling (4 papers). Sae Dieb is often cited by papers focused on Machine Learning in Materials Science (11 papers), Biomedical Text Mining and Ontologies (4 papers) and Topic Modeling (4 papers). Sae Dieb collaborates with scholars based in Japan, China and United Kingdom. Sae Dieb's co-authors include Koji Tsuda, Zhufeng Hou, Junichiro Shiomi, Shenghong Ju, Kazuki Yoshizoe, Masaharu Yoshioka, Shinjiro Hara, Xin Tang, Tadakatsu Ohkubo and A. Bolyachkin and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Journal of Applied Physics.

In The Last Decade

Sae Dieb

21 papers receiving 243 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sae Dieb Japan	8	175	45	40	38	26	23	246
Hasan Kurban United States	12	181 1.0×	19 0.4×	86 2.1×	42 1.1×	18 0.7×	40	329
Kevin Cruse United States	11	302 1.7×	29 0.6×	87 2.2×	94 2.5×	55 2.1×	18	401
Xinfang Zhang China	9	116 0.7×	22 0.5×	61 1.5×	31 0.8×	9 0.3×	31	289
Qiaohao Liang United States	6	202 1.2×	10 0.2×	63 1.6×	29 0.8×	50 1.9×	9	292
Kevin Decker United States	5	259 1.5×	12 0.3×	64 1.6×	37 1.0×	53 2.0×	11	363
Yuxiao Tu China	7	147 0.8×	23 0.5×	68 1.7×	12 0.3×	20 0.8×	12	283
Sean Paradiso United States	8	372 2.1×	13 0.3×	59 1.5×	18 0.5×	68 2.6×	9	456
Max C. Gallant United States	4	317 1.8×	13 0.3×	119 3.0×	26 0.7×	47 1.8×	8	505
Bernardus Rendy United States	4	287 1.6×	11 0.2×	113 2.8×	23 0.6×	46 1.8×	8	472

Countries citing papers authored by Sae Dieb

Since Specialization

Citations

This map shows the geographic impact of Sae Dieb'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 Sae Dieb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sae Dieb more than expected).

Fields of papers citing papers by Sae Dieb

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sae Dieb. 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 Sae Dieb. The network helps show where Sae Dieb may publish in the future.

Co-authorship network of co-authors of Sae Dieb

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

All Works

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20 of 20 papers shown

Kato, Masaru, Sae Dieb, Keitaro Sodeyama, et al.. (2025). Machine Learning-Assisted Development of Platinum-Free RuNiCo Nanocages for Electrocatalytic Hydrogen Oxidation Reaction in Acidic Media. ACS Applied Energy Materials. 8(19). 14052–14057. 1 indexed citations

Bolyachkin, A., Xin Tang, H. Sepehri‐Amin, et al.. (2024). Data-driven compositional optimization of La(Fe,Si)13-based magnetocaloric compounds for cryogenic applications. Scripta Materialia. 258. 116486–116486. 4 indexed citations

Nakanishi, Jun, Takeshi Ueki, Sae Dieb, et al.. (2024). Data-driven optimization of the in silico design of ionic liquids as interfacial cell culture fluids. Science and Technology of Advanced Materials. 25(1). 2418287–2418287. 1 indexed citations

Song, Zhilong, Qionghua Zhou, Shuaihua Lu, et al.. (2023). Adaptive Design of Alloys for CO₂ Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm. The Journal of Physical Chemistry Letters. 14(14). 3594–3601. 9 indexed citations

Dieb, Sae, Yoshiaki Toda, Keitaro Sodeyama, & Masahiko Demura. (2023). Machine learning-assisted determination of material chemical compositions: a study case on Ni-base superalloy. SHILAP Revista de lepidopterología. 3(1).

Dieb, Sae, et al.. (2023). Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys. Scientific Reports. 13(1). 12660–12660. 4 indexed citations

Dieb, Sae, et al.. (2023). Creating a Visual Topic Map for Battery Researchers Using a Large Global Open Dataset. ECS Meeting Abstracts. MA2023-02(8). 3321–3321.

Lai, Jiawei, A. Bolyachkin, Noriki Terada, et al.. (2022). Machine learning assisted development of Fe2P-type magnetocaloric compounds for cryogenic applications. Acta Materialia. 232. 117942–117942. 32 indexed citations

Dieb, Sae, Akira Suzuki, Yan Meng, et al.. (2021). SuperMat: construction of a linked annotated dataset from superconductors-related publications. SHILAP Revista de lepidopterología. 1(1). 34–44. 15 indexed citations

10.

Dieb, Sae, et al.. (2021). Creating research topic map for NIMS SAMURAI database using natural language processing approach. SHILAP Revista de lepidopterología. 1(1). 2–11. 1 indexed citations

11.

Dieb, Sae & Masashi Ishii. (2020). Machine-learning-assisted design of depth-graded multilayer X-ray structure. 12–12. 1 indexed citations

12.

Dieb, Sae, Shenghong Ju, Junichiro Shiomi, & Koji Tsuda. (2019). Monte Carlo tree search for materials design and discovery. MRS Communications. 9(2). 532–536. 31 indexed citations

13.

Dieb, Sae, Zhufeng Hou, & Koji Tsuda. (2018). Structure prediction of boron-doped graphene by machine learning. The Journal of Chemical Physics. 148(24). 241716–241716. 57 indexed citations

14.

Dieb, Sae, Shenghong Ju, Kazuki Yoshizoe, et al.. (2017). MDTS: automatic complex materials design using Monte Carlo tree search. Science and Technology of Advanced Materials. 18(1). 498–503. 51 indexed citations

15.

Dieb, Sae, Masaharu Yoshioka, & Shinjiro Hara. (2016). NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices. Journal of Information Processing. 24(3). 554–564. 2 indexed citations

16.

Dieb, Sae & Masaharu Yoshioka. (2015). Extraction of Chemical and Drug Named Entities by Ensemble Learning Using Chemical NER Tools Based on Different Extraction Guidelines.. 8(2). 61–76. 1 indexed citations

17.

Dieb, Sae, Masaharu Yoshioka, Shinjiro Hara, & Mary Newton. (2015). Framework for automatic information extraction from research papers on nanocrystal devices. Beilstein Journal of Nanotechnology. 6. 1872–1882. 9 indexed citations

18.

Dieb, Sae, Masaharu Yoshioka, Shinjiro Hara, & Mary Newton. (2014). Automatic Annotation of Parameters from Nanodevice Development Research Papers. 77–85. 3 indexed citations

19.

Dieb, Sae, Masaharu Yoshioka, & Shinjiro Hara. (2012). Automatic Information Extraction of Experiments from Nanodevices Development Papers. 42–47. 7 indexed citations

20.

Dieb, Sae, Masaharu Yoshioka, & Shinjiro Hara. (2011). Construction of tagged corpus for Nanodevices development papers. 3. 167–170. 4 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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