Stating that machine learning and materials informatics will change the path of discovering new materials, the chief consultant for research and entrepreneurship said: very fast and accurate predictions of material properties made possible by artificial intelligence help to investigate new materials in a wide compositional and hyperstructured space. do.
According to the news headquarters of the Innotex 2022 exhibition, Maryam Emami, senior consultant for research and entrepreneurship at the next event of the Innotex exhibition, said about the perspective of informatics in the future of science: less than a hundred years ago, the first transistor was made, at this time we entered the silicon era and access to computers And mobile became possible for everyone.
He continued: The variety of materials today is very high, now we have more than 530 different types of aluminum milling cutters and more than 22 thousand steel milling cutters, with this great variety in engineering materials, it can be assumed that there is a solution for every imaginable problem in materials science. There is a misconception.
This senior research and entrepreneurship consultant stated: In 2008, the National Academy of Engineering identified 14 major milestones in engineering for the 21st century, which include providing access to clean water and making solar energy economical, and solving these challenges requires the discovery of new materials. He stated: "The process of developing new materials is very slow and complicated when materials are not discovered by chance."
Referring to how Edison made the light bulb, Emami said: When it comes to materials, there is a lot of space of possibilities, and in the best case, we rely on a series of high-throughput tests in addition to Edison's trial and error. He stated: In the last decade, with innovative works and great initiatives with ambitious goals, we were able to develop and improve new materials at a multiple speed and at a fraction of the cost.
This senior research and entrepreneurship consultant stated: Since we don't know how to calculate the specific properties of a material, machine learning and materials informatics can help researchers by quickly predicting the properties and characteristics of materials.
Stating that machine learning and materials informatics are changing the path of discovering new materials, he said: very fast and accurate predictions of material properties made possible by artificial intelligence means that we can investigate new materials in a wide compositional and hyperstructural space. When it comes to the discovery of materials, we should remove the reliance on luck from this process, because for the first time a tool has been made available that allows us to use calculated luck.
Emami stated: Using artificial intelligence, the properties of material alloys can be predicted, it reduces the development process of materials, and we can use our imagination to create materials that were not possible before.
He added: Now it is possible to investigate new materials with targeted properties at an incredible speed using machine learning, and it represents a quantum leap compared to previous approaches, and this approach is as transformative as the discovery of bronze, steel, and silicon.