Using machine learning to find an optimal mixture of metals to create a desired alloy
A large team of researchers at the Max-Planck-Institut für Eisenforschung GmbH, working with colleagues from Technische Universität Darmstadt, Delft University of Technology and KTH Royal Institute of Technology, has found that it is possible to use machine learning to help metallurgists find the optimal mixture of metals to create a desired alloy. In their paper published in the journal Science, the group describes their three-step process and how well it worked when tested. Qing-Miao Hu and Rui Yang with the Chinese Academy of Sciences, Institute of Metal Research, have published a Perspectives piece in the same journal issue outlining the work done by the team on this new effort.
Joint research led by Sosuke Ito of the University of Tokyo has shown that nonequilibrium thermodynamics, a branch of physics that deals with constantly changing systems, explains why optimal transport theory, a mathematical framework for the optimal change of distribution to reduce cost, makes generative models optimal. As nonequilibrium thermodynamics…
A team of researchers at Google's DeepMind, London, has found that AI can find faster algorithms to solve matrix multiplication problems. In their paper published in the journal Nature, the group describes using reinforcement learning to improve math-based algorithms. A Research Briefing has also been published in the same journal…
An international team of scientists has used machine learning to help them develop perovskite solar cells with near-record efficiency. In their paper published in the journal Science, the group describes how they used the machine-learning algorithm to help them find new hole-transporting materials to improve the efficiency of perovskite solar…