Two new kinds of magnets have been produced by the first supercomputer-generated recipes. Both the magnetic materials have been built with the use of high-throughput computational models and have been made by atom by atom structure. This indicates a new era for both the magnetic materials that can be produced at large scale with ground-breaking speed.
According to Science Daily, the material scientists from Duke University have discovered the procedure where the capability to predict magnetism can be shown in new materials with the help of computer models. To prove this working, these material scientists have generated two new magnetic materials which are not known or never have been seen before.
The results of the above study of magnetic materials have been published in Science Advances on 14 April 2017. "Predicting magnets are a heck of a job and their discovery is very rare. Even with our screening process, it took years of work to synthesize our predictions. We hope others will use this approach to create magnets for use in a wide range of applications," Stefano Curtarolo, the professor of mechanical and engineering and materials science said. Curtarolo is also the director of Center for Materials Genomics at Duke University.
Duke University wrote that the new magnetic materials which have been generated are Co2MnTi and Mn2PtPd. Co2MnTi has been made of cobalt, magnesium, and titanium. After tests, it was found that this magnet lost its magnetism at 940 K temperature and the actual "Curie temperature" was 938 K, an exceptionally high number. This property makes this magnet very useful for commercial applications.
Mn2PtPd has been made with a mixture of manganese, platinum, and palladium. This magnet finished up as an antiferromagnetic which means that the electrons of Mn2PtPd are evenly distributed in their alignments. This implies that this magnetic material will have no internal magnetic moment but its electrons will be responsive to outer magnetic fields. Scientist materials will continue to study to get more ability for predicting for moving forward and make these types of materials useful in the future.
