  Melting is a high temperature process that requires extensive sampling of
configuration space, thus making melting temperature prediction computationally
very expensive and challenging. Over the past few years, I have built two
methods to address this challenge, one via direct density functional theory
(DFT) molecular dynamics (MD) simulations and the other via deep learning graph
neural networks. The DFT approach is based on statistical analysis of
small-size solid-liquid coexistence MD simulations. It eliminates the risk of
metastable superheated solid in the fast-heating method, while also
significantly reducing the computer cost relative to the traditional
large-scale coexistence method. Being both accurate and efficient (at the speed
of several days per material), it is considered as one of the best methods for
direct DFT melting temperature calculation. The deep learning method is based
on graph neural networks that effectively handles permutation invariance in
chemical formula, which drastically improves efficiency and reduces cost. At
the speed of milliseconds per material, the model is extremely fast, while
being moderately accurate, especially within the composition space expanded by
the dataset. I have implemented both methods into automated computer code
packages, making them publicly available and free to download. The DFT and deep
learning methods are highly complementary to each other, and hence they can be
potentially well integrated into a framework for melting temperature
prediction. I demonstrated examples of applying the methods to materials design
and discovery of high-melting-point materials.
