Machine learning in technology promotes discovery of new superhard content

Machine learning in technology promotes discovery of new superhard content
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First, we try to find out what the superhard material is.

Human mind works very fast indeed. I often think how wonderful the human mind is. Nature has given a lot to every living being on earth according to its own.

But a man is going to be satisfied with something or the other, he is ready to challenge nature today.
Well, nature has coined diamonds on earth as the strongest material, but humans have discovered super hard carbon stronger than this.
Yes ! Super hard carbon is by far the strongest material present in this world. This substance is much stronger and stronger than diamond. Friends! Let me tell you here that I have written a very beautiful and curious article about diamonds before this. If you want, you can also read that article once. This article will definitely be able to change your perspective related to diamonds.

you can also read Organize Your Home With Technology

Superhard Material’s journal Superhard presents up-to-date results of basic and applied research on the production, properties, and applications of materials and related equipment. It illuminates the results of fundamental research on the physicochemical processes of the manufacture and enhancement of films such as single-crystals, polycrystalline, and dispersed materials, diamonds and diamonds.

Development of methods for the smooth and controlled synthesis of superhard materials and methods for static, explosive, and epitaxial synthesis. The focus of the journal is large single crystals of synthetic diamonds; Elite grinding powder and micron powder of synthetic diamond and cubic boron nitride.

Polycrystalline and mixed superhard materials based on diamond and cubic boron nitride; Highly efficient metalworking, boring, stonework, coal mining and geological exploration for diamond and carbide tools; Articles of ceramic; Pastes for high precision optics; Precision sticks for diamond turning

Techniques for precision machining of metals, glass and ceramics. The journal covers all fundamental and technical aspects of the synthesis, characterization, properties, devices and applications of these materials. The magazine welcomes manuscripts from all countries in the English language.

It may be noted that superhard materials are in high demand in the industry, from energy production to aerospace, but the detection of suitable new materials has largely been a matter of trial and error based on classical materials such as diamonds. till now.

Researchers at the University of Houston and Manhattan College have reported a machine learning model that can accurately estimate the hardness of new materials, allowing scientists to more easily find suitable compounds for use in various applications. The work was told in Advanced Materials.

Materials that are superhard – defined as values with a hardness greater than 40 gigapascals on the Vickers scale, meaning that there will be pressures greater than 40 gigapascals to leave an indentation on the surface of the material – are rare.

“That makes the identification of new materials challenging,” said Jaco Brogch, associate professor of chemistry at the University of Houston and the same author for the paper. “That’s why materials like synthetic diamond are still used, although they are challenging and expensive to make.”

One of the complicating factors is that the stiffness of a material can vary depending on the amount of pressure, known as load dependence. It is virtually impossible today to experimentally perform complex testing of a material and to use computational modeling.

The model reported by researchers relies on predicting load-dependent Vickers stiffness based on the chemical composition of the material. Researchers report finding more than 10 new and promising stable borocarbide phases; Work is now underway to design and produce the materials so that they can be tested in the laboratory.

for more learn Technology Is Created When Our Mind Thinks

Depending on the accuracy of the model’s report, the difference is good. Researchers reported an accuracy of 97%.

First author Xian Zhang, a doctoral student at UH, stated that the database created to train the algorithm is based on data consisting of 560 different compounds, each producing multiple data points. Data on hundreds of published academic papers are needed to find the data needed to create a representative dataset.

“All good machine learning projects start with a good dataset,” said Brogo, who is also a lead investigator with the Texas Center for Superconductivity at UH. “True success is largely the development of this dataset.”

Researchers have traditionally used machine learning to predict a single variable of stiffness, Brogo said, but this does not account for property complexities such as load dependency, which he said is still well understood. Have not been. Despite its limitations, machine learning is a good tool.

A machine learning system does not need to understand physics,he said.It only analyzes training data and makes new predictions based on data.

Machine learning has its limitations, however.

Brogo said, The idea of using machine learning does not say, ‘Here is the next greatest material, but to help direct our experimental discovery.It tells you where you should look.

Thank you all

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