Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …

Interatomic potentials approximate the potential energy of atoms as a function of their
coordinates. Their main application is the effective simulation of many-atom systems. Here …

We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …

Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

We demonstrate that fast and accurate linear force fields can be built for molecules using the
atomic cluster expansion (ACE) framework. The ACE models parametrize the potential …

The atomic cluster expansion is a general polynomial expansion of the atomic energy in
multi-atom basis functions. Here we implement the atomic cluster expansion in the …