A new simulation technique allows scientists to pick the appropriate molecular precursors to synthesize nanomaterials from the bottom up.
One of the most popular fields of research in nanotechnology is the manufacture of new materials or nanomaterials by toying around with the structure of matter at the molecular and atomic level.
To produce nanomaterials, material scientists have to follow one of two options: either the top-down or bottom-up fabrication processes.
For the substructive top-down method, researchers start with a bulk mass of a material and keep breaking it down until they get to the atomic level. For example, if you keep chopping down a block of carbon, you end up with graphene.
Machine Learning Technique for Bottom-up Fabrication of Nanomaterials
The bottom-up approach is to build nanostructures using building blocks in the form of molecular precursors.
Synthesizing nanoparticles from the bottom up is more advantageous in that it gives scientists more control over the final product’s shape and size and lends itself more to scaling up.
However, one of the major challenges with the bottom-up approach is that scientists can’t know for sure how molecules would interact and which of them would be best for their target nanoparticle.
Now, a team of materials scientists from two universities in Japan has developed a new technique that takes a lot of the guesswork out of the bottom-up fabrication process of nanoparticles.
The Machine Learning-based method, devised by Daniel Packwood (Kyoto University’s iCeMs) and Taro Hitosugi (Tokyo Institute of Technology), takes into account the chemical properties of molecular precursors and their interaction to show how the target nanostructure would end up.
By categorizing different molecules according to the structure they’d form, the modal allows scientists, via diagrams (dendrograms), to see how molecules would assemble into their target nanomaterial before they get to fabricate it from scratch.
To test their simulation technique, researchers worked on graphene nanoribbons and investigated how different molecules and the temperatures would affect the end product and which would yield the best results.
Hitosugi and Packwood think of their technique’s dendrograms as a periodic table for nanomaterials that categorizes molecules based on the way they would self-assemble into nano-sized structures.
Although a step in the right direction, there is still work to be done. In their research paper published in Nature Communications, the authors wrote:
“However, in order to truly prove that the dendrograms or other informatics-based approaches can be as valuable to materials science as the periodic table, we must incorporate them in a real bottom-up nanomaterial fabrication experiment. We are currently pursuing this direction in our laboratories,’’