Scientists have claimed that the AI-ANGINEERED engine may reduce the impact of the urban urban island in cities and cut air conditioning bills, as machine learning accelerates the creation of new materials for everything from electric motors to carbon capture.
Artificial intelligence experts used the formula for formulating the new paint that could maintain buildings between 5C and 20 C than normal paint after exposure to sunlight in the middle of the day. It can also be applied to cars, trains, electrical equipment and other things that require more cooling in a heated world.
Using machine learning, researchers at universities in the United States, China, Singapore and Sweden were designed new coating formulas that were seized to better reflect and emit sunlight, according to a study reviewed by peers Published In the Journal of Nature Sciences.
This is the latest example of the use of artificial intelligence to jump from experimental methods and traditional error of scientific progress. Last year, the British company Matnex used Amnesty International to create a new type of permanent magnet used in electric vehicle engines to avoid the use of rare ground minerals, which are carbon dense mining.
Microsoft Absolute AI tools to quickly help researchers design new inorganic materials – the crystal structures used in solar panels and medical implants are often. There are hopes for new materials to better capture carbon in the atmosphere and make more efficient batteries.
Painting research was conducted by academics at the University of Texas in Austin, Shanghai University Jiao Tong, Singapore National University and the University of Omese in Sweden. It found that the application of one of many new paints that support artificial intelligence on the surface of a four -storey residential block can provide electricity, equivalent to 15,800 kilowatts annually in a hot climate such as Rio de Janeiro or Bangkok. If the paint is applied to 1000 blocks, this may save enough electricity to operate more than 10,000 air conditioning units for a year.
“Our automated learning framework represents a big leap forward in the design of the thermal senders. By automating the process and expanding the design space, we can create super performance materials that were previously unimaginable,” said Yuebing Zheng, a professor at the University of Texas and the co -leader of the study.
He said that the work that was designed for a month was designed within a few days using artificial intelligence and that the new materials that may not have been discovered through the experiment and the error was created.
Now, we follow the output of machine learning, [its instructions for] The structure and the type of material that we must use, and we can obtain it properly without going through many design and manufacturing test courses. “
“Things are moving very quickly in this place. Last year or so, there were many startups trying to use artificial intelligence for materials,” said Dr. Alex Ganouz, chemistry lecturer at Imperial College London, who also uses machine learning to design new materials.
He said that the process of designing a new material may require the calculation of millions of potential groups. Artificial intelligence allows material scientists to pay previous restrictions in mathematical energy. This also means that the traditional process of creating a material and then testing its properties can be reversed, as scientists are able to tell artificial intelligence about the characteristics they want in advance.
