To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

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

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

Abstract Machine learning approaches have been extensively applied to improve the
accuracy and reliability of potentials, addressing inherent limitations in molecular dynamics …

The emergence of artificial intelligence is profoundly impacting computational chemistry,
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …

In atomistic modeling, machine learning interatomic potential (MLIP) has emerged as a
powerful technique for studying alloy materials. However, given that MLIPs are often trained …

The validity of the Classical Nucleation Theory (CNT), the standard tool for describing and
predicting nucleation kinetics in metastable systems, has been under scrutiny for almost a …

Machine learning interatomic potentials powered by neural networks have been shown to
readily model a gradient of compositions in metallic systems. However, their application to …

The W–Cu materials hold vast potential for applications in electronic information, nuclear
energy, and aerospace sectors. Here, we report a new occurrence of solid-state …

The prediction of phase composition in metallic alloys is one of the main challenges in
modern material science. The most effective and promising methods to solve this problem …