High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …

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

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 …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

Force fields (FFs) form the basis of molecular simulations and have significant implications
in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is …

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

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 inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

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 …