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

Decades of advancements in strategies for the calculation of atomic interactions have
culminated in a class of methods known as machine-learning interatomic potentials …

The atomic cluster expansion (ACE) provides a general, local, and complete representation
of atomic energies. Here we present an efficient framework for parametrization of ACE …

In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum
mechanical data have seen tremendous success recently. Top-down approaches that learn …

Most metallurgical properties, eg, dislocation propagation, precipitate formation, can only be
fully understood atomistically but most phenomena and quantities of interest cannot be …

Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their
many-body capacity allows highly accurate approximations of the potential of mean force …

The mechanical performance—including deformation, fracture and radiation damage—of
zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the …

Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-
aged condition where the strength-limiting processes of precipitate shearing and Orowan …

The concept of high entropy materials (HEMs) provides a fertile ground for developing novel
irradiation-resistant structural materials. In HEMs, the vast and complicated configurational …

Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the
strengthening is studied computationally at the peak-aged condition where precipitate …