Juhwan Noh

2.3k total citations
29 papers, 1.7k citations indexed

About

Juhwan Noh is a scholar working on Materials Chemistry, Renewable Energy, Sustainability and the Environment and Electrical and Electronic Engineering. According to data from OpenAlex, Juhwan Noh has authored 29 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Materials Chemistry, 13 papers in Renewable Energy, Sustainability and the Environment and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Juhwan Noh's work include Machine Learning in Materials Science (24 papers), X-ray Diffraction in Crystallography (9 papers) and Electrocatalysts for Energy Conversion (7 papers). Juhwan Noh is often cited by papers focused on Machine Learning in Materials Science (24 papers), X-ray Diffraction in Crystallography (9 papers) and Electrocatalysts for Energy Conversion (7 papers). Juhwan Noh collaborates with scholars based in South Korea, United States and China. Juhwan Noh's co-authors include Yousung Jung, Geun Ho Gu, Sungwon Kim, Alán Aspuru‐Guzik, Jaehoon Kim, I. P. Kim, John M. Gregoire, Seoin Back, Changhyeok Choi and Benjamín Sánchez-Lengeling and has published in prestigious journals such as Journal of the American Chemical Society, Advanced Materials and Nature Communications.

In The Last Decade

Juhwan Noh

28 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juhwan Noh South Korea 16 1.3k 438 388 324 287 29 1.7k
Geun Ho Gu South Korea 21 1.2k 0.9× 722 1.6× 411 1.1× 475 1.5× 213 0.7× 47 1.9k
Kirsten T. Winther United States 17 1.1k 0.8× 673 1.5× 423 1.1× 282 0.9× 102 0.4× 26 1.5k
Santosh K. Suram United States 22 1.4k 1.1× 693 1.6× 566 1.5× 126 0.4× 127 0.4× 53 1.9k
Dan Guevarra United States 24 1.2k 0.9× 960 2.2× 627 1.6× 153 0.5× 98 0.3× 51 1.8k
Weike Ye United States 10 1.3k 1.0× 155 0.4× 461 1.2× 83 0.3× 349 1.2× 14 1.6k
Helge S. Stein Germany 22 1.0k 0.8× 455 1.0× 750 1.9× 94 0.3× 150 0.5× 63 1.8k
Arghya Bhowmik Denmark 23 1.0k 0.8× 338 0.8× 1.0k 2.7× 243 0.8× 130 0.5× 73 2.0k
Malia B. Wenny United States 7 1.0k 0.8× 133 0.3× 305 0.8× 118 0.4× 202 0.7× 13 1.6k
Sida Huang China 13 671 0.5× 192 0.4× 142 0.4× 209 0.6× 73 0.3× 23 900
Tanjin He United States 19 1.1k 0.8× 94 0.2× 315 0.8× 113 0.3× 178 0.6× 27 1.7k

Countries citing papers authored by Juhwan Noh

Since Specialization
Citations

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

Fields of papers citing papers by Juhwan Noh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juhwan Noh

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

All Works

20 of 20 papers shown
1.
Kang, Yun Chan, Juhwan Noh, Jung H. Shin, et al.. (2025). Data-driven framework based on machine learning and optimization algorithms to predict oxide-zeolite-based composite and reaction conditions for syngas-to-olefin conversion. CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION). 74. 211–227.
2.
Lee, Sujeong, Juhwan Noh, & Ho Jin Ryu. (2025). Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments. Journal of Hazardous Materials. 494. 138735–138735. 1 indexed citations
3.
Kim, Jin‐Soo, Juhwan Noh, & Jino Im. (2024). Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics. npj Computational Materials. 10(1). 17 indexed citations
4.
Noh, Juhwan, et al.. (2024). Reaction Templates: Bridging Synthesis Knowledge and Artificial Intelligence. Accounts of Chemical Research. 57(14). 1964–1972. 5 indexed citations
5.
6.
Kim, Seong-Min, et al.. (2023). Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation. Chemical Science. 15(3). 1039–1045. 10 indexed citations
7.
Kim, Sungwon, et al.. (2023). A structure translation model for crystal compounds. npj Computational Materials. 9(1). 5 indexed citations
8.
Noh, Juhwan & Hyunju Chang. (2023). Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts. Journal of Chemical Information and Modeling. 63(19). 5981–5995. 1 indexed citations
9.
Bang, Gi Joo, Geun Ho Gu, Juhwan Noh, & Yousung Jung. (2022). Activity Trends of Methane Oxidation Catalysts under Emission Conditions. ACS Catalysis. 12(16). 10255–10263. 7 indexed citations
10.
Gu, Geun Ho, Juhyung Lim, Chengzhang Wan, et al.. (2021). Correction to “Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation”. Journal of the American Chemical Society. 143(19). 7590–7590. 2 indexed citations
11.
Gu, Geun Ho, Juhyung Lim, Chengzhang Wan, et al.. (2021). Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation. Journal of the American Chemical Society. 143(14). 5355–5363. 40 indexed citations
12.
Choi, Changhyeok, Geun Ho Gu, Juhwan Noh, Hyun S. Park, & Yousung Jung. (2021). Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions. Nature Communications. 12(1). 4353–4353. 139 indexed citations
13.
Ren, Zekun, Siyu Tian, Juhwan Noh, et al.. (2021). An Invertible Crystallographic Representation for <b>General</b> Inverse Design of Inorganic Crystals with Targeted Properties. SSRN Electronic Journal. 1 indexed citations
14.
Kim, Juhwan, Geun Ho Gu, Juhwan Noh, et al.. (2021). Predicting potentially hazardous chemical reactions using an explainable neural network. Chemical Science. 12(33). 11028–11037. 9 indexed citations
15.
Gu, Geun Ho, et al.. (2020). Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning. Journal of the American Chemical Society. 142(44). 18836–18843. 95 indexed citations
16.
Noh, Juhwan, Geun Ho Gu, Sungwon Kim, & Yousung Jung. (2020). Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals. Journal of Chemical Information and Modeling. 60(4). 1996–2003. 40 indexed citations
17.
Noh, Juhwan, Geun Ho Gu, Sungwon Kim, & Yousung Jung. (2020). Machine-enabled inverse design of inorganic solid materials: promises and challenges. Chemical Science. 11(19). 4871–4881. 139 indexed citations
18.
Kim, Sungwon, Juhwan Noh, Geun Ho Gu, Alán Aspuru‐Guzik, & Yousung Jung. (2020). Generative Adversarial Networks for Crystal Structure Prediction. ACS Central Science. 6(8). 1412–1420. 175 indexed citations
19.
Noh, Juhwan, Sungwon Kim, Geun Ho Gu, et al.. (2019). Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments. Chemical Communications. 55(89). 13418–13421. 22 indexed citations
20.
Gu, Geun Ho, Juhwan Noh, I. P. Kim, & Yousung Jung. (2019). Machine learning for renewable energy materials. Journal of Materials Chemistry A. 7(29). 17096–17117. 247 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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