Troy D. Loeffler

1.4k citations
37 papers · 1.0k indexed · 1 hit paper · h-index 15
Topics
Machine Learning in Materials Science (21 papers)nanoparticles nucleation surface interactions (7 papers)Computational Drug Discovery Methods (5 papers)
Partner nations
United StatesIndiaPoland

In The Last Decade

Troy D. Loeffler

35 papers receiving 994 citations

Hit Papers

Machine learning enabled autonomous microstructural chara...20202026202220242020100200300

Peers

Troy D. Loeffler
Comparison fields: 5 of 105
  • Materials Chemistry 611
  • Electrical and Electronic Engineering 174
  • Biomedical Engineering 159
  • Atomic and Molecular Physics, and Optics 149
  • Molecular Biology 141
Replace David Gao with:
David Gao United Kingdom
Zachary Trautt United States
Richard Tran United States
Venkatesh Botu United States
Evgeny V. Podryabinkin Russia
Michal Jahnátek Austria
Pankaj Rajak United States
Matthew K. Horton United States
Lance J. Nelson United States
Vinay I. Hegde United States
Troy D. Loeffler relative to David Gao United Kingdom David Gao's profile →
Citations per field
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David Gao · 1×
Citations per year

Countries citing papers authored by Troy D. Loeffler

Since Specialization
Citations

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

Fields of papers citing papers by Troy D. Loeffler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Troy D. Loeffler. 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 Troy D. Loeffler. The network helps show where Troy D. Loeffler may publish in the future.

Co-authorship network of co-authors of Troy D. Loeffler

This figure shows the co-authorship network connecting the top 25 collaborators of Troy D. Loeffler. A scholar is included among the top collaborators of Troy D. Loeffler 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 Troy D. Loeffler. Troy D. Loeffler 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
#WorkIndexed citations
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5 41
6 100
7 18
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13 12
14
Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data
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About Troy D. Loeffler

Troy D. Loeffler is a scholar working on Structural Biology, Materials Chemistry and Filtration and Separation, having authored 37 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (21 papers), nanoparticles nucleation surface interactions (7 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Materials Chemistry (611 citations), Structural Biology (11 citations) and Metals and Alloys (18 citations). Troy D. Loeffler has collaborated with scholars based in United States, India and Poland. Frequent co-authors include Subramanian K. R. S. Sankaranarayanan, Henry Chan, Mathew J. Cherukara, Badri Narayanan, Tarak K. Patra, Stephen K. Gray, Rohit Batra, Chris J. Benmore, Bin Chen and Bin Chen. Their work appears in journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.

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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