Daniel W. Davies

5.4k citations
49 papers · 3.9k indexed · 1 hit paper · h-index 17

Impact in

Papers in

Daniel W. Davies

46 papers receiving 3.8k citations

Hit Papers

Machine learning for molecular and materials science 2018 · 3.1k citations
3.1k201820262020202310002.0k3.0k

Peers

Daniel W. Davies
Comparison fields: 5 of 150
  • Materials Chemistry 2.6k
  • Computational Theory and Mathematics 693
  • Metals and Alloys 75
  • Catalysis 168
  • Physical and Theoretical Chemistry 185
Replace Hugh Cartwright with:
Hugh Cartwright United Kingdom
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Ohad Levy United States
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Jonathan Schmidt Germany
Nongnuch Artrith United States
Logan Ward United States
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Daniel W. Davies relative to Hugh Cartwright United Kingdom Hugh Cartwright's profile →
Citations per field
00.5×1.5×1.8×
Hugh Cartwright · 1×
Citations per year

Countries citing papers authored by Daniel W. Davies

Since Specialization
Citations

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

Fields of papers citing papers by Daniel W. Davies

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel W. Davies, 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 Daniel W. Davies Line = papers co-authored together Daniel W. Davies links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20246
3 202312
4 202218
5 20224
6 202115
7 20207
8 201940
9 20180
10 201825
11
Machine learning for molecular and materials science
Hit paper breakdown →
20183052
12 201826
13 2016137
14 200611
15 19838
16 19746
17 197114
18 196937
19 196844
20 196811

About Daniel W. Davies

Daniel W. Davies is a scholar working on Electronic, Optical and Magnetic Materials, Atomic and Molecular Physics, and Optics, Materials Chemistry, Inorganic Chemistry and Biophysics, having authored 49 papers that have together received 3.9k indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (14 papers), Machine Learning in Materials Science (11 papers), Electronic and Structural Properties of Oxides (10 papers), Magnetism in coordination complexes (7 papers), X-ray Diffraction in Crystallography (5 papers), Inorganic Fluorides and Related Compounds (4 papers), Atomic and Molecular Physics (4 papers) and Atmospheric Ozone and Climate (4 papers). The work is most often cited by research in Materials Chemistry (2.6k citations), Computational Theory and Mathematics (693 citations), Metals and Alloys (75 citations), Catalysis (168 citations) and Physical and Theoretical Chemistry (185 citations). Daniel W. Davies has collaborated with scholars based in United Kingdom, South Korea and United States. Frequent co-authors include Aron Walsh, Keith T. Butler, Olexandr Isayev, Hugh Cartwright, Jonathan M. Skelton, Adam Jackson, Jarvist M. Frost, Andrew J. Morris, David O. Scanlon and Artem R. Oganov. Their work appears in journals such as Molecular Physics, Chemical Physics Letters, Chemistry of Materials, Chemical Science and Journal of Materials Chemistry C.

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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