Using AI to predict hybrid nano-particle structure

AI, applied

Researchers at the University of Jyväskylä, Finland, have made significant progress in predicting atomic structures of hybrid nano-particles.

The researchers demonstrated a new algorithm that ‘learns’ to predict binding sites of molecules at the metal-molecule interface of hybrid nano-particles, using already published experimental structural information on nano-particle reference systems.

Funded by the Academy of Finland’s Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research (AIPSE), the algorithm can be applied to any nano-metre-size structure consisting of metal and molecules provided that some structural information already exists on the corresponding systems.

These nano-metre-sized hybrid metal nano-particles have a multitude of uses, such as applications in catalysis, nano-electronics, nano-medicine and biological imaging.

“The basic idea behind our algorithm is very simple. Chemical bonds between atoms are always discrete, having well-defined bond angles and bond distances. Therefore, every nano-particle structure known from experiments, where the positions of all atoms are resolved accurately, tells something essential about the chemistry of the metal-molecule interface. The interesting question regarding applications of artificial intelligence for structural predictions is: how many of these already known structures we need to know so that predictions for new, yet unknown particles become reliable? It looks like we only need a few dozen of known structures,” comments the lead author of the article, Sami Malola, a researcher at the Nano-science Centre of the University of Jyväskylä.

“In the next phase of this work we will build efficient atomic interaction models for hybrid metal nano-particles by using machine learning methods. These models will allow us to investigate several interesting and important topics such as particle-particle reactions and the nano-particles’ ability to function as delivery vehicles for small drug molecules”, say the leader of the study, Professor Hannu Häkkinen.

Häkkinen’s collaborator, professor Tommi Kärkkäinen of the Faculty of Information Science in the University of Jyväskylä continues: “This is a significant step forward within the context of new interdisciplinary collaboration in our university. Applying artificial intelligence to challenging topics in nano-science, such as structural predictions for new nano-materials, will surely lead to new breakthroughs.”

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