Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …

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 …

Chemically complex multicomponent alloys possess exceptional properties derived from an
inexhaustible compositional space. The complexity however makes interatomic potential …

Developing machine learning-based interatomic potentials from ab initio electronic structure
methods remains a challenging task for computational chemistry and materials science. This …

The development of machine-learned interatomic potentials requires generating sufficiently
expressive atomistic data sets. Active learning algorithms select data points on which labels …

The accuracy of the training data limits the accuracy of bulk properties from machine-learned
potentials. For example, hybrid functionals or wave-function-based quantum chemical …

Modeling and understanding properties of materials from first principles require knowledge
of the underlying atomistic structure. This entails knowing the individual chemical identity …

Efficiently creating a concise but comprehensive data set for training machine-learned
interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses …

Assessments of machine-learning (ML) potentials are an important aspect of the rapid
development of this field. We recently reported an assessment of the linear-regression …