Teodoro Laino

92 papers receiving 4.1k citations

Teodoro Laino's Hit Papers

Accelerating materials discovery using artificial intelligence, high performance computing and robotics 2022 · 241 citations
2410+1+2Years since publication50100150200

Peers

Teodoro Laino
Comparison fields: 5 of 143
  • Computational Theory and Mathematics 1.1k
  • Materials Chemistry 2.1k
  • Physical and Theoretical Chemistry 255
  • Biophysics 122
  • Catalysis 125
Replace Mark P. Waller with:
Mark P. Waller Germany
Benjamín Sánchez-Lengeling United States
Pascal Friederich Germany
Rohit Batra United States
Sereina Riniker Switzerland
Rafael Gómez‐Bombarelli United States
Gregory A. Landrum Switzerland
Florian Häse Canada
U. Deva Priyakumar India
Teodoro Laino relative to Mark P. Waller Germany Mark P. Waller's profile →
Citations per field
00.5×1.5×2.1×
Mark P. Waller · 1×
Citations per year

Countries citing papers authored by Teodoro Laino

Since Specialization
Citations

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

Fields of papers citing papers by Teodoro Laino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 95 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010371
2 2018289
3 2020252
4
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Hit paper breakdown →
2022241
5 2005228
6 2014206
7 2021200
8 2021170
9 2021167
10 2006135
11 2020133
12 2020132
13 202099
14 201289
15 200380
16 202274
17 201164
18 202261
19 200859
20 201156

About Teodoro Laino

Teodoro Laino is a scholar working on Materials Chemistry, Molecular Biology, Computational Theory and Mathematics, Biomedical Engineering and Electrical and Electronic Engineering, having authored 95 papers that have together received 4.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (19 papers), Topic Modeling (8 papers), Protein Structure and Dynamics (7 papers), Chemical Synthesis and Analysis (7 papers), Advanced Chemical Physics Studies (7 papers), Advanced Battery Materials and Technologies (6 papers) and Advancements in Battery Materials (6 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Materials Chemistry (2.1k citations), Physical and Theoretical Chemistry (255 citations), Biophysics (122 citations) and Catalysis (125 citations). Teodoro Laino has collaborated with scholars based in Switzerland, Italy and United States. Frequent co-authors include Philippe Schwaller, Alessandro Curioni, Alain C. Vaucher, Jean‐Louis Reymond, Michele Parrinello, Vishnu H Nair, Alessandro Laio, Fawzi Mohamed, Costas Bekas and Katharina Meier. Their work appears in journals such as Nature Communications, The Journal of Physical Chemistry B, Journal of Chemical Theory and Computation, npj Computational Materials and The Journal of Physical 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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