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As the research of 5G technology continues, there has been need of a material that has high thermal conductivity which is very much essential for the progress of 5G. Research of such material could've took us years as the power of our brain is limited. But we are fortunate enough that the Machine learning technology can assist us to compute large amount of data required for computational molecular design. The goal of computational molecular design is to identify new molecules with phisiochemical properties which meet some arbitrary requirements. This is where Machine Learning comes in picture and shows a huge potential in material science.
The research was conducted in Statistical Mathematics Research Organization of Information And Systems, Research and Services Division Of Material Data and Integrated Systems (MaDIS), Tokyo Institute of Technology and Center for Materials Research by Information Integration.
Ryo Yoshida a researcher says that, many aspects are yet to be explored like computational systems to work with limited data, he also says that using Machine Learning for the design of soft material is challenging but a promising field as the discovered materials have properties that differ a lot and cannot be completely predicted by existing theories.
The method used here is known as Bayesian molecular design, in this process there is a library of virtual chemical structures, then the researches provided a higher region of glass transition temperature and melting temperature as alternative design targets to create reliable prediction models.
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