Neural network algorithm predicts Arrhenius crossover temperature with 90% accuracy

A joint paper by the Department of Computational Physics and Modeling of Physical Processes and Udmurt Federal Research Center of the Russian Academy of Sciences published in the journal Materials introduces an algorithm that allows for the correct estimation of the crossover temperature for a large class of materials, regardless of their compositions or glass-forming abilities.