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

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.

Nanomaterial Transformation in the Soil–Plant System: Implications for Food Safety and Application in Agriculture

“…Metabolomics, [126] proteomics, [127] exposomics, [128] and secretomics [129] are emerging powerful tools to identify the components of root exudates, EPS, and plant/microbial metabolite response to ENMs exposure. The datasets obtained could be combined with machine learning approaches (e.g., nanoinformatics) [130] to identify key drivers and critical steps of ENMs transformation, and to predict ENMs transformations thereby facilitating safe-by-design of ENMs for sustainable application in agriculture.…”

Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

“…The exciting examples of machine discovery in probe microscopy described above are a subset of a much wider, and rapidly expanding, field: nanoinformatics. Barnard et al's very recent mini-review, Nanoinformatics, and the big challenges for the science of small things [40], is an engaging and exceptionally timely overview of the power and pitfalls of bringing the 'big data' strategy, applied so successfully in bioinformatics and cheminformatics, to bear on the nanoscopic (and sub-nanoscopic) world. They highlight a number of key, but surmountable, issues with the emerging nanoinformatics discipline including, in particular, the perennial problem of limited datasets.…”

“…Burzawa et al [46] have taken his strategy one step further and adopted machine learning not to extract materials properties but to determine which particular physical model/dynamics drives pattern formation in a system (in their case, the 2D Ising model). A second essential issue identified by Barnard et al [40] in their review of nanoinformatics is the bias inherent in very many AI frameworks. Bias, of course, is not just an issue for machine learning in nanoscience and probe microscopy; it is a fundamental issue with machine learning per se [47].…”

Shaping the Future of Solid‐State Electrolytes through Computational Modeling

“…However, for many materials, we have a variety of features and small available data sets with dependent features, and methods that are appropriate to use with such systems is currently a topic of great interest. [ 46 ]…”