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 …

Phase-field method (PFM) has become a mainstream computational method for predicting
the evolution of nano and mesoscopic microstructures and properties during materials …

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 …

Machine learning of the quantitative relationship between local environment descriptors and
the potential energy surface of a system of atoms has emerged as a new frontier in the …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …

Increasing demand for the fabrication of components with complex designs has spurred a
revolution in manufacturing methods. Additive manufacturing stands out as a promising …

While artificial intelligence (AI), and by extension machine learning (ML), continues to be
adopted in parallel engineering disciplines, the integration of AI/ML into the structural …