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Grant support

We would like to acknowledge support from the Comunidad de Madrid Industrial Doctorate Programme 2017 under reference number IND2017/IND-7793, from Quasar Science Resources S.L., and from the Spanish MINECO (projects MAT2017-83273-R (AEI/FEDER, UE). R.P. acknowledges support from the Spanish Ministry of Science and Innovation, through the "Maria de Maeztu" Programme for Units of Excellence in R&D (CEX2018-000805-M).

Analysis of institutional authors

Romero-Muñiz, CAuthorPerez, RCorresponding Author
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Article

A Deep Learning Approach for Molecular Classification Based on AFM Images

Publicated to:Nanomaterials. 11 (7): 1658- - 2021-07-01 11(7), DOI: 10.3390/nano11071658

Authors: Carracedo-Cosme, Jaime; Romero-Muniz, Carlos; Perez, Ruben

Affiliations

Quasar Sci Resources SL, Camino Ceudas 2, E-28232 Las Rozas De Madrid, Spain - Author
Univ Autonoma Madrid, Condensed Matter Phys Ctr IFIMAC, E-28049 Madrid, Spain - Author
Univ Autonoma Madrid, Dept Fis Teor Mat Condensada, E-28049 Madrid, Spain - Author
Univ Pablo Olavide, Dept Phys Chem & Nat Syst, Ctra Utrera Km 1, E-41013 Seville, Spain - Author
Univ Seville, Dept Fis Aplicada 1, E-41012 Seville, Spain - Author
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Abstract

In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize. We show that a variational autoencoder (VAE) provides a very efficient way to incorporate, from very few experimental images, characteristic features into the training set that assure a high accuracy in the classification of both theoretical and experimental images.

Keywords
atomic force microscopy (afm)deep learningmolecular recognitionAtomic force microscopy (afm)Atomic-force microscopyChemical-identificationDeep learningMolecular recognitionResolutionSilicon (111)-(7x7) surfaceTotal-energy calculationsVariational au-toencoder (vae)Variational autoencoder (vae)

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Nanomaterials due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 37/161, thus managing to position itself as a Q1 (Primer Cuartil), in the category Physics, Applied.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.48. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.16 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 3.74 (source consulted: Dimensions Apr 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-04-24, the following number of citations:

  • WoS: 20
  • Scopus: 25
  • Europe PMC: 5
  • OpenCitations: 21
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 29.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 26 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 8.7.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Carracedo-Cosme, J) and Last Author (PEREZ PEREZ, RUBEN).

the author responsible for correspondence tasks has been PEREZ PEREZ, RUBEN.