Hideki Mori

926 total citations
59 papers, 660 citations indexed

About

Hideki Mori is a scholar working on Materials Chemistry, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Hideki Mori has authored 59 papers receiving a total of 660 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Materials Chemistry, 17 papers in Computer Networks and Communications and 11 papers in Hardware and Architecture. Recurrent topics in Hideki Mori's work include Microstructure and mechanical properties (13 papers), Parallel Computing and Optimization Techniques (10 papers) and Embedded Systems Design Techniques (9 papers). Hideki Mori is often cited by papers focused on Microstructure and mechanical properties (13 papers), Parallel Computing and Optimization Techniques (10 papers) and Embedded Systems Design Techniques (9 papers). Hideki Mori collaborates with scholars based in Japan, United States and China. Hideki Mori's co-authors include Shigenobu Ogata, Svein Stølen, Fredrik Grønvold, Tooru Ataké, H.W. Brinks, Taisuke Ozaki, Hajime Kimizuka, Yoshikazu Nakayama, Seiji Akita and Hiroyuki Yasuda and has published in prestigious journals such as The Journal of Chemical Physics, Physical review. B, Condensed matter and Journal of Applied Physics.

In The Last Decade

Hideki Mori

53 papers receiving 645 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hideki Mori Japan	14	426	146	131	85	84	59	660
Young Soo Han South Korea	14	404 0.9×	154 1.1×	25 0.2×	49 0.6×	41 0.5×	44	618
Saurabh Bajaj United States	12	1.2k 2.7×	162 1.1×	142 1.1×	26 0.3×	77 0.9×	16	1.3k
Jiabo Chen China	17	431 1.0×	86 0.6×	231 1.8×	12 0.1×	284 3.4×	60	869
Eiji Hashimoto Japan	13	334 0.8×	164 1.1×	112 0.9×	42 0.5×	17 0.2×	74	688
Pengjie Wang United States	12	401 0.9×	33 0.2×	57 0.4×	25 0.3×	163 1.9×	34	663
J. Miškuf Slovakia	14	264 0.6×	335 2.3×	68 0.5×	5 0.1×	55 0.7×	80	707
Yongpeng Shi China	10	641 1.5×	88 0.6×	180 1.4×	4 0.0×	51 0.6×	22	770
Yuma Iwasaki Japan	10	306 0.7×	46 0.3×	73 0.6×	7 0.1×	36 0.4×	30	440
M. Isshiki Japan	16	350 0.8×	114 0.8×	39 0.3×	5 0.1×	39 0.5×	84	782
Aly Saeed Egypt	15	640 1.5×	40 0.3×	36 0.3×	16 0.2×	10 0.1×	47	838

Countries citing papers authored by Hideki Mori

Since Specialization

Citations

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

Fields of papers citing papers by Hideki Mori

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hideki Mori

This figure shows the co-authorship network connecting the top 25 collaborators of Hideki Mori. A scholar is included among the top collaborators of Hideki Mori 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 Hideki Mori. Hideki Mori 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

Ito, Kazuma, et al.. (2025). Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron. Communications Materials. 7(1).

Ito, Kazuma, et al.. (2025). Transferability of machine-learning interatomic potential to α-Fe nanocrystalline deformation. International Journal of Mechanical Sciences. 291-292. 110132–110132. 4 indexed citations

Suzudo, Tomoaki, et al.. (2024). Large-Scale Atomistic Simulations of Cleavage in BCC Fe using Machine-Learning Potential. Journal of the Society of Materials Science Japan. 73(2). 129–135. 1 indexed citations

Ito, Kazuma, et al.. (2024). Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe. npj Computational Materials. 10(1). 11 indexed citations

Suzudo, Tomoaki, Ken-ichi Ebihara, Tomohito Tsuru, & Hideki Mori. (2024). Emergence of crack tip plasticity in semi-brittle α-Fe. Journal of Applied Physics. 135(7). 4 indexed citations

Lobzenko, Ivan, Yoshinori Shiihara, Hideki Mori, & Tomohito Tsuru. (2023). Influence of group IV element on basic mechanical properties of BCC medium-entropy alloys using machine-learning potentials. Computational Materials Science. 219. 112010–112010. 6 indexed citations

Mori, Hideki, Tomohito Tsuru, Masahiko Okumura, et al.. (2023). Dynamic interaction between dislocations and obstacles in bcc iron based on atomic potentials derived using neural networks. Physical Review Materials. 7(6). 4 indexed citations

Lobzenko, Ivan, Tomohito Tsuru, Hideki Mori, Daisuke MATSUNAKA, & Yoshinori Shiihara. (2023). Implementation of Atomic Stress Calculations with Artificial Neural Network Potentials. MATERIALS TRANSACTIONS. 64(10). 2481–2488.

Suzudo, Tomoaki, Ken-ichi Ebihara, Tomohito Tsuru, & Hideki Mori. (2022). Cleavages along {110} in bcc iron emit dislocations from the curved crack fronts. Scientific Reports. 12(1). 19701–19701. 14 indexed citations

10.

Mori, Hideki. (2016). Temperature and Stress Dependence of Mobility of Screw Dislocation in BCC Iron. Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena. 258. 17–20. 1 indexed citations

11.

Mori, Hideki. (2014). Peierls Barrier of Screw Dislocation in bcc Iron at Finite Temperature. MATERIALS TRANSACTIONS. 55(10). 1531–1535. 4 indexed citations

12.

Sato, Toshinori, et al.. (2012). Importance of single-core performance in the multicore era. 122. 107–113. 3 indexed citations

13.

Uehara, Minoru, et al.. (2009). Virtual Large-Scale Disk Base on PC Grid. Scalable Computing Practice and Experience. 10(1).

14.

Uehara, Minoru, et al.. (2009). Evaluation of Stateful Reliability Counter in Small-World Cellular Neural Networks. 1. 440–445. 4 indexed citations

15.

Mori, Hideki, Hajime Kimizuka, & Shigenobu Ogata. (2009). Dislocation Properties and Peierls Stress of BCC Iron Based on Generalized-Stacking-Fault Energy Surface by Using First Principles Calculations. Journal of the Japan Institute of Metals and Materials. 73(8). 595–600. 8 indexed citations

16.

Yanagisawa, Hideaki, Masato Uehara, & Hideki Mori. (2004). Automatic generation of a simulation compiler by a HW/SW codesign system. 53–59. 2 indexed citations

17.

Mori, Hideki, et al.. (2002). Stream oriented fault tolerant array. 172–181. 1 indexed citations

18.

Stølen, Svein, Fredrik Grønvold, H.W. Brinks, Tooru Ataké, & Hideki Mori. (1998). Heat capacity and thermodynamic properties of LaFeO3and LaCoO3from =13 K to =1000 K. The Journal of Chemical Thermodynamics. 30(3). 365–377. 60 indexed citations

19.

Mori, Hideki, et al.. (1985). Parallel processing FFT for VLSi implementation. 68(5). 284–291. 2 indexed citations

20.

Tokoro, Mario, et al.. (1974). A Very High-Speed Microprogrammable Pipeline Signal Processor.. IFIP Congress. 60–64. 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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