Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

There are several facets of aluminum when it comes to sustainability. While it helps to save
fuel due to its low density, producing it from ores is very energy-intensive. Recycling it shifts …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …

High entropy oxides are emerging as an exciting new avenue to design highly tailored
functional behaviors that have no traditional counterparts. Study and application of these …

The point defect properties of body-centered cubic medium-entropy alloy NbZrTi were
studied by first-principles calculations. Due to severe lattice distortion, a significant portion of …

The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …