Learning from the Machine: Uncovering Sustainable Nanoparticle Design Rules

Machines
consisting of bags of artificial neural networks (ANNs)
have been constructed to connect nanoparticle features to the viability
of a broad class of organisms upon exposure. The optimization of these
machines is based on a relatively small data set. However, through
consensus across a bag of ANNs, these machines predict at a level
of confidence comparable to the experiment and perform better than
chance. The mining of the machine across the feature space allows
for the discovery of design rules for nanoparticles with increased
viability. As such, we demonstrate the efficacy of inversion as an approach to learn from the machine
in the context of designing sustainable nanoparticles. For example,
we find that increased manganese content in lithium NiMnCo oxide nanoparticles
is associated with greater viability, carbon dots reduce viability
less than quantum dots, and gold nanoparticle coatings can significantly
affect viability at high concentration.