Daniel M. Packwood

976 total citations
57 papers, 744 citations indexed

About

Daniel M. Packwood is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Daniel M. Packwood has authored 57 papers receiving a total of 744 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Materials Chemistry, 16 papers in Electrical and Electronic Engineering and 12 papers in Biomedical Engineering. Recurrent topics in Daniel M. Packwood's work include Machine Learning in Materials Science (14 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Surface Chemistry and Catalysis (7 papers). Daniel M. Packwood is often cited by papers focused on Machine Learning in Materials Science (14 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Surface Chemistry and Catalysis (7 papers). Daniel M. Packwood collaborates with scholars based in Japan, New Zealand and Thailand. Daniel M. Packwood's co-authors include Taro Hitosugi, Satoshi Horike, Yusuke Nishiyama, Kentaro Kadota, Patrick Han, Nghia Tuan Duong, Gen Zhang, Masahiko Tsujimoto, Susumu Kitagawa and Pichaya Pattanasattayavong and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Angewandte Chemie International Edition.

In The Last Decade

Daniel M. Packwood

53 papers receiving 737 citations

Peers

Daniel M. Packwood

EEE

RESE

Harpreet Singh India

Jinyu Wang China

Hengbo Li China

Swastik Mondal India

Nanette N. Jarenwattananon United States

Mingxuan Zhang China

Ming Cheng China

Yong Xia China

Jongwoo Park South Korea

Jacob Townsend United States

Harpreet Singh India

Daniel M. Packwood

463 ×1.0

179 ×0.9

EEE

187 ×1.1

72 ×0.7

126 ×1.2

RESE

Citations per year, relative to Daniel M. Packwood Daniel M. Packwood (= 1×) peers Harpreet Singh

Countries citing papers authored by Daniel M. Packwood

Since Specialization

Citations

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

Fields of papers citing papers by Daniel M. Packwood

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Packwood. 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 Daniel M. Packwood. The network helps show where Daniel M. Packwood may publish in the future.

Co-authorship network of co-authors of Daniel M. Packwood

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Packwood. A scholar is included among the top collaborators of Daniel M. Packwood 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 Daniel M. Packwood. Daniel M. Packwood 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

Nurhuda, Maryam, et al.. (2025). Graph neural networks to predict atomic transition charges and exciton couplings in organic semiconductors. The Journal of Chemical Physics. 163(2).

Lin, Zirui, Ken‐ichi Otake, T. Kajiwara, et al.. (2025). Interconnected Lamellar 3D Semiconductive PCP for Rechargeable Aqueous Zinc Battery Cathodes. Small. 21(10). e2411386–e2411386.

Packwood, Daniel M., et al.. (2025). Cross-correlation for detecting and understanding patterns in ab initio molecular dynamics simulations of liquid metals. Journal of Physics Condensed Matter. 37(34). 345401–345401.

Nurhuda, Maryam, Ken‐ichi Otake, Susumu Kitagawa, & Daniel M. Packwood. (2025). Density of States and Binding Energy Informatics for Exploring Early Disease Detection in MOF‐Metal Oxide Chemiresistive Sensors. Advanced Theory and Simulations. 8(5). 2 indexed citations

Ohara, Yuki, et al.. (2024). Entropically driven melting of Cu-based 1D coordination polymers. Chemical Communications. 60(72). 9833–9836. 2 indexed citations

Sutton, Joshua J., et al.. (2024). Towards high-throughput exciton diffusion rate prediction in molecular organic semiconductors. Journal of Materials Chemistry C. 12(24). 8747–8758. 3 indexed citations

Yasui, Kosuke, Itsunari Minami, Motonari Uesugi, et al.. (2024). Molecular Design for Cardiac Cell Differentiation Using a Small Data Set and Decorated Shape Features. Journal of Chemical Information and Modeling. 64(23). 8824–8837. 1 indexed citations

Hume, Paul, et al.. (2023). Exciton diffusion in amorphous organic semiconductors: Reducing simulation overheads with machine learning. The Journal of Chemical Physics. 158(20). 4 indexed citations

Nurhuda, Maryam, et al.. (2023). Machine learning of isomerization in porous molecular frameworks: exploring functional group pair distance distributions. Inorganic Chemistry Frontiers. 10(18). 5379–5390. 3 indexed citations

10.

Maruoka, Masahiro, Ryo Suzuki, Daniel M. Packwood, et al.. (2023). Extracellular calcium functions as a molecular glue for transmembrane helices to activate the scramblase Xkr4. Nature Communications. 14(1). 5592–5592. 2 indexed citations

11.

Packwood, Daniel M., et al.. (2023). An Intelligent, User‐Inclusive Pipeline for Organic Semiconductor Design. Advanced Theory and Simulations. 6(8). 6 indexed citations

12.

Packwood, Daniel M., et al.. (2022). Machine Learning in Materials Chemistry: An Invitation. SHILAP Revista de lepidopterología. 8. 100265–100265. 30 indexed citations

13.

Noda, Naotaka, Amélie Perron, Daniel M. Packwood, et al.. (2022). Discovery of a phase-separating small molecule that selectively sequesters tubulin in cells. Chemical Science. 13(19). 5760–5766. 13 indexed citations

14.

Kadota, Kentaro, You‐lee Hong, Yusuke Nishiyama, et al.. (2021). One-Pot, Room-Temperature Conversion of CO ₂ into Porous Metal–Organic Frameworks. Journal of the American Chemical Society. 143(40). 16750–16757. 28 indexed citations

15.

Noda, Naotaka, Hiroki Yoshida, Ippei Takashima, et al.. (2020). Discovery of Self‐Assembling Small Molecules as Vaccine Adjuvants. Angewandte Chemie International Edition. 60(2). 961–969. 14 indexed citations

16.

Packwood, Daniel M., et al.. (2020). Data-driven design of glasses with desirable optical properties using statistical regression. AIP Advances. 10(10). 8 indexed citations

17.

Sahu, Debashis, Pinit Kidkhunthod, Taweesak Sudyoadsuk, et al.. (2020). Elucidating the Coordination of Diethyl Sulfide Molecules in Copper(I) Thiocyanate (CuSCN) Thin Films and Improving Hole Transport by Antisolvent Treatment. Advanced Functional Materials. 30(36). 31 indexed citations

18.

Nakayama, Ryo, Nobuaki Yasuo, Ryota Shimizu, et al.. (2020). Bayesian statistics-based analysis of AC impedance spectra. AIP Advances. 10(4). 7 indexed citations

19.

Packwood, Daniel M. & Pichaya Pattanasattayavong. (2020). Disorder-robust bands from anisotropic orbitals in a coordination polymer semiconductor. Journal of Physics Condensed Matter. 32(27). 275701–275701. 7 indexed citations

20.

Pattanasattayavong, Pichaya, Daniel M. Packwood, & David J. Harding. (2019). Structural versatility and electronic structures of copper(i) thiocyanate (CuSCN)–ligand complexes. Journal of Materials Chemistry C. 7(41). 12907–12917. 14 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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