  Gold nanoparticles are highly desired for a range of technological
applications due to their tunable properties, which are dictated by the size
and shape of the constituent particles. Many heuristic methods for controlling
the morphological characteristics of gold nanoparticles are well known.
However, the underlying mechanisms controlling their size and shape remain
poorly understood, partly due to the immense range of possible combinations of
synthesis parameters. Data-driven methods can offer insight to help guide
understanding of these underlying mechanisms, so long as sufficient synthesis
data are available. To facilitate data mining in this direction, we have
constructed and made publicly available a dataset of codified gold nanoparticle
synthesis protocols and outcomes extracted directly from the nanoparticle
materials science literature using natural language processing and text-mining
techniques. This dataset contains 5,154 data records, each representing a
single gold nanoparticle synthesis article, filtered from a database of
4,973,165 publications. Each record contains codified synthesis protocols and
extracted morphological information from a total of 7,608 experimental and
12,519 characterization paragraphs.

Materials Science