Recent years witnessed an explosion of machine learning methods in all sectors. However, in the materials sector, and even more specifically in the biomaterials sector, although there have been numerous attempts at generalization, there has been a severe problem of coding the problem. The reason lies mainly in the temporal and spatial dimensions of the materials and their intrinsic complexity. In this contribution, we wish to suggest a possible universal coding of materials. This coding exploits a pseudo-semantic analysis and can be particularly advantageous in the study of polymeric biomaterials.

Encoding Materials Dynamics for Machine Learning Applications

Piotto S.
;
Nardiello A. M.;Di Biasi L.;Sessa L.
2020

Abstract

Recent years witnessed an explosion of machine learning methods in all sectors. However, in the materials sector, and even more specifically in the biomaterials sector, although there have been numerous attempts at generalization, there has been a severe problem of coding the problem. The reason lies mainly in the temporal and spatial dimensions of the materials and their intrinsic complexity. In this contribution, we wish to suggest a possible universal coding of materials. This coding exploits a pseudo-semantic analysis and can be particularly advantageous in the study of polymeric biomaterials.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4769948
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact