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

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

Semiconductors are promising in photoelectric and thermoelectric devices, for which the
thermal transport properties are of particular interest. However, they have not been fully …

This study comprehensively investigates the impact of various parameters on aluminum
matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu …

The interaction mechanism between the precipitates and moving dislocations is one of the
key factors that governs the mechanical properties of precipitation-hardened aluminum …

Efficiently designing lightweight alloys with combined high corrosion resistance and
mechanical properties remains an enduring topic in materials engineering. Due to the …

In several recent years, a significant progress has been made in atomistic simulation of
materials, involving the application of machine learning methods to constructing classical …

The physical mechanisms behind cluster formation during quenching and aging of age-
hardening metallic alloys are poorly understood based on classical nucleation and growth …