Machine learning and nanotechnology.

"In-situ liquid-cell transmission electron microscopy electrodeposition of PtNi nanoparticle films on a carbon electrode during cyclic voltammetry. The electron beam (here in green color) illuminated the electrode (here in orange color) submerged in the platinum and nickel salt solution, enhancing the growth of the PtNi nanoparticle film (grey color) on the electrode. The film thickness increases with each cycle and by the fourth cycle reaction-rate limited growth of branched and porous structures were observed. Credit: Weronika Wojtowicz, water background from freepik" (ScitechDaily, Inside the Nano Factory: Real-Time Insights Into Nanostructure Creation)

Machine learning makes it possible to predict the structures of materials. And that makes it possible to create more effective tools for nanotechnology. Nanotechnology means that the systems can create complex synthetic chemical structures. And those structures can turn into fundamental chemistry and mechanical tools. Machine learning makes it possible to create always similar conditions. 

Then it can make very accurate mixtures of chemicals into the reaction chamber. The AI can search and model things that happen in the cells when they create the complex organic molecules those systems can create the cloned cells. Or they can create a chemical factory that emulates living cells. In some cases, the ultra-sharp systems can remove cell organelle from the cells and then activate it using nutrient injections. 

The term fundamental chemistry means. The system can manipulate and control single atoms in molecules. That allows to creation of structures with atom-sharp accuracy. The fundamental chemistry in nanotechnology makes it possible to create graphene or fullerene graphene structures. In the last one, the structure is the fullerene molecules that nanotubes connect into their entirety. That material can be very strong and lightweight. The nanosystems can make many things. Like synthetic proteins and enzymes. 

"By learning from millions of crystal descriptions, CrystaLLM predicts new material structures faster, aiding in the rapid development of technologies. Credit: SciTechDaily.com" (ScitechDaily, AI Decodes Crystal Patterns To Power Tomorrow’s Innovations)

The biomimetic systems can emulate things like slime molds. The slime mold is an organism that can learn things without neurons. And that means that the same proteins and enzymes that make slime mold to learn things can be installed into the silicone structure. The ability to follow thousands of objects at the same time. 

Make it possible to find out, what makes the slime mold learn. Another way is to use the microchips that pull and push the protein fibers. And then it can use the method to control things like liquid robots. Liquid robots are tools that can be used in medical work to clean blood vessels or close them to stop the blood leak. Those systems can also analyze things like water tubes. The liquid, but non-organic robots can also search for things like incipient fire. And then those robots can cover those flames. 

The nanotechnology is one of the biggest things in human history. The nanosystems require very precise and accurate information about the materials and their formation. The system can use the most modern microscopes for analyzing the process and the environment. Where the system creates those complicated molecular and sub-molecular structures.  

https://scitechdaily.com/ai-decodes-crystal-patterns-to-power-tomorrows-innovations/

https://scitechdaily.com/inside-the-nano-factory-real-time-insights-into-nanostructure-creation/