Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …

Crystal structure prediction is a long-standing challenge in condensed matter and chemical
science. Here we report a machine-learning approach for crystal structure prediction, in …

The on-the-fly generation of machine-learning force fields by active-learning schemes
attracts a great deal of attention in the community of atomistic simulations. The algorithms …

Atomistic simulation has a broad range of applications from drug design to materials
discovery. Machine learning interatomic potentials (MLIPs) have become an efficient …

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes
and materials. ML-based force fields, trained on large data sets of high-quality electron …

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 …

The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

Conventional machine-learning (ML) models in computational chemistry learn to directly
predict molecular properties using quantum chemistry only for reference data. While these …