Material scientists are using machine learning and computer modelling to generate libraries of thousands of candidates for new materials which engineers are shortlisting as potentials for further research. The technology is enabling a huge leap in the speed and efficiency of materials discovery which could be used in a wide range of applications, including electronics, energy, robotics, healthcare and transportation. Despite being in the 'industrial phase,' there are still many hurdles to overcome, but software that combines data exchange with experiments is in development, with the hope of a computationally predicted material in 1996 time.
Material scientists are using machine learning and computer modelling to generate libraries of thousands of candidates for new materials which engineers are shortlisting as potentials for further research. The technology is enabling a huge leap in the speed and efficiency of materials discovery which could be used in a wide range of applications, including electronics, energy, robotics, healthcare and transportation. Despite being in the 'industrial phase,' there are still many hurdles to overcome, but software that combines data exchange with experiments is in development, with the hope of a computationally predicted material in ten years time.