Magnetoelectric multiferroics, which display both ferroelectric and magnetic orders, are
appealing because of their rich fundamental physics and promising technological …

We propose a machine-learning interatomic potential for multi-component magnetic
materials. In this potential we consider magnetic moments as degrees of freedom (features) …

Chemical and physical properties of complex materials emerge from the collective motions
of the constituent atoms. These motions are in turn determined by a variety of interatomic …

Solid solution strengthening remains the basis for many industrial alloys, yet chemical short-
range order (CSRO) can also play a significant role in the strengthening of alloys with …

Abstract The Atomic Cluster Expansion (ACE) provides a formally complete basis for the
local atomic environment. ACE is not limited to representing energies as a function of atomic …

We introduce a translational and rotational invariant local representation for vector fields,
which can be employed in the construction of machine learning energy models of solids and …

Chemical reactions, charge transfer reactions, and magnetic materials are notoriously
difficult to describe within Kohn–Sham density functional theory, which is strictly a ground …

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry
practices because of their ability to drastically exceed the accuracy-length/time scale …

A large and increasing number of different types of interatomic potentials exist, either based
on parametrised analytical functions or machine learning. The choice of potential to be used …

Picosecond ultrasonics is a fast growing and advanced research field with broad application
to the imaging and characterization of nanostructured materials as well as at a fundamental …