Jump to main content
Jump to site search


Nanoinformatics, and the big challenges for the science of small things

Author affiliations

Abstract

The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties. However, combining these fundamentally different scientific approaches is not as straightforward as it seems. Machine learning methods were developed for large data sets with small numbers of consistent features. Typically nanomaterials data sets are small, with high dimensionality and high variance in the feature space, and suffer from numerous destructive biases. None of the established data science or machine learning methods in widespread use today were devised with (nano)materials data sets in mind, but there are ways to overcome these challenges and use them reliably. In this review we will discuss domain-specific constraints on data-driven nanomaterials design, and explore the differences between nanomaterials simulation and nanoinformatics that can be leveraged for greater impact.

Graphical abstract: Nanoinformatics, and the big challenges for the science of small things

Back to tab navigation

Publication details

The article was received on 12 Jul 2019, accepted on 05 Aug 2019 and first published on 05 Aug 2019


Article type: Minireview
DOI: 10.1039/C9NR05912A
Nanoscale, 2019, Advance Article

  •   Request permissions

    Nanoinformatics, and the big challenges for the science of small things

    A. S. Barnard, B. Motevalli, A. J. Parker, J. M. Fischer, C. A. Feigl and G. Opletal, Nanoscale, 2019, Advance Article , DOI: 10.1039/C9NR05912A

Search articles by author

Spotlight

Advertisements