Quantum physics uses the so -called “Fenman Plans” – simple graphics created by the Nobel Nobel Richard Final – to facilitate the understanding of reactions between particles such as electrons and photons or vibrations of atoms.
In these plans, each particle represents a line, and the points in which the lines meet the places of the interaction, and therefore they are more like a road map explaining how particles move and exchange energy.
Feenman problem
But as the complexity of the reaction increases, the number of these plans becomes very huge, which makes almost impossible calculations for scientists.
Consequently, this problem is very large in the study of materials in which electrons interact strongly with the vibrations of the crystal network, which are defined as a very regular arrangement for atoms, ions or molecules inside a solid material, so that they are repeated in the same style in all directions to form a three -dimensional structure such as an engineering network.
And when an electron moves in a substance, it distorts the crystal network surrounding it, and a new system consisting of the electron and deformity that causes it creates, and this is called “polypharun”.
This phenomenon greatly affects the transmission of electrons in materials, such as semiconductors, and plays an important role in highly connected materials, and therefore scientists have tried for decades to solve this problem and calculate the effect of polyparians accurately, but the traditional accounts were collapsing in front of the complexity.
“Imagine an attempt to predict the stock market behavior tomorrow, to try to do so, every interaction between each trader over a certain period of time to obtain accurate predictions of market dynamics.” Says Marco Bernardi, a professor of applied physics, physics and material science at the California Institute of Technology.
But as with the prediction of the stock market, the number of potential interactions between those particles without offspring is very large, but rather the impossible in reality, says Bernardi.
The first time
Now, a team from the California Institute of Technology (Caltete) led by Bernardi is developed by a new mathematical algorithm based on Mont Carlo’s planning method, which is an advanced statistical method that allows smart samples of millions of plans without the need to calculate each of them manually, according to. the study The team published in the journal Nature Vizix.
This mathematical method was named after the Monte Carlo Casino in Monaco, because the American -American mathematics and physicist Stanisawaf Awam was inspired by the idea of gambling habits of his uncle.
Overall, the Monte Carlo method is a group of randomly -based mathematical methods (random experiences) to solve complex problems, and the basic idea is to use random samples to obtain approximate results.
Only for approximation, you are in a very large restaurant completely dark and do not know the number of tables in it, and you want to know what the percentage of tables has juices, and you cannot pass on all tables because their number is enormous.
Here you can start moving randomly inside the restaurant and stop at any table you meet and record is a drink or not, then this process is repeated hundreds or thousands of times with different tables each time.
After that, the percentage of tables on which you have found drinks is calculated, and the more you repeat the experience, the more due to the real percentage of all tables in the restaurant.
Now, the tables are, there are Final Plans, and instead of the question about drinks the question relates to the contribution of the plans to the quantitative interaction, the algorithm chooses random plans and then calculates its contributions and collects the results to obtain the full image.
According to the study, the algorithm takes random samples of points within the space of all Final plans for a system, but with some directives regarding the most important places from which samples must be taken.
“We have set some rules to move effectively and highly flexible within the space of Fenman’s plans,” Bernardi explains.
Promising applications
Of course, things remain very complicated, and they face several problems such as the need for a balance between accuracy and the time taken in the calculations, the difficulty of dealing with problems that contain a very large number of variables, relying on the quality of random generators, and the need to check the results carefully.
But by using scientists – for the first time – enables the first time to solve the polypharres problem using accounts based on basic principles or physical laws without any auxiliary experimental data, according to. Statement An official journalist from the California Technology Institute.
Based on these results, it is possible to predict the properties of materials accurately such as the movement of electrons inside the materials, and the behavior of materials under certain conditions such as high temperatures or high pressure.
According to the study, the algorithm was applied to crystalline networks for real materials such as lithium Floride, titanium dioxide and sterunchium tittes, and the properties of electrons and vibrations have been calculated in an unprecedented precision.
This technology can change our understanding of modern quantum materials, such as high -delivery materials, electrical thermal materials and new electronic devices, and may also help in developing better quantitative computers and understanding light interactions with the material at a basic level.