A. Miguel

4.4k citations
74 papers · 3.3k indexed · 1 hit paper · h-index 32

A. Miguel

73 papers receiving 3.3k citations

Hit Papers

Machine Learning Interatomic Potentials as Emerging Tools...6872019202620212023200400600

Peers

A. Miguel
Comparison fields: 5 of 94
  • Condensed Matter Physics 695
  • Materials Chemistry 2.1k
  • Structural Biology 41
  • Electrochemistry 164
  • Atomic and Molecular Physics, and Optics 696
Replace Ralf Drautz with:
Ralf Drautz Germany
Pengfei Guan China
Panchapakesan Ganesh United States
Hiori Kino Japan
Roman Engel‐Herbert United States
Osamu Sugino Japan
Deyu Lu United States
Qimin Yan United States
A. Fazzio Brazil
Wahyu Setyawan United States
A. Miguel relative to Ralf Drautz Germany Ralf Drautz's profile →
Citations per field
00.5×1.5×2.2×
Ralf Drautz · 1×
Citations per year

Countries citing papers authored by A. Miguel

Since Specialization
Citations

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

Fields of papers citing papers by A. Miguel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20258
3 20241
4 20244
5 20247
6 202326
7 20236
8 202321
9 202337
10 202227
11 202245
12 202131
13 20216
14 202119
15 2020108
16 2019106
17
Machine Learning Interatomic Potentials as Emerging Tools for Materials Sciencebreakdown →
2019687
18 2018147
19 2015285
20 20113

About A. Miguel

A. Miguel is a scholar working on Condensed Matter Physics, Materials Chemistry, Electrochemistry, Surfaces, Coatings and Films and Structural Biology, having authored 74 papers that have together received 3.3k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (28 papers), GaN-based semiconductor devices and materials (21 papers), Diamond and Carbon-based Materials Research (13 papers), Semiconductor materials and devices (12 papers), Metal and Thin Film Mechanics (10 papers), Semiconductor Quantum Structures and Devices (9 papers), Acoustic Wave Resonator Technologies (8 papers) and Advanced Chemical Physics Studies (8 papers). The work is most often cited by research in Condensed Matter Physics (695 citations), Materials Chemistry (2.1k citations), Structural Biology (41 citations), Electrochemistry (164 citations) and Atomic and Molecular Physics, and Optics (696 citations). A. Miguel has collaborated with scholars based in Finland, United Kingdom and Ireland. Frequent co-authors include Volker L. Deringer, Gábor Cśanyi, Tomi Laurila, Eoin P. O’Reilly, Stefan Schulz, Sami Sainio, Olga Lopez‐Acevedo, Anja Aarva, Jari Koskinen and Markku Ylilammi. Their work appears in journals such as Physical review. B., Chemistry of Materials, Physical Review B, The Journal of Chemical Physics and physica status solidi (b).

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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