Decades of advancements in strategies for the calculation of atomic interactions have
culminated in a class of methods known as machine-learning interatomic potentials …

Machine-learned interatomic potentials (MLIPs) and force fields (ie, interaction laws for
atoms and molecules) are typically trained on limited data-sets that cover only a very small …

The application of machine learning models and algorithms towards describing atomic
interactions has been a major area of interest in materials simulations in recent years, as …

Machine learning interatomic potentials (MLIPs) enable materials simulations at extended
length and time scales with near-ab initio accuracy. They have broad applications in the …

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

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for
atomic modeling. While small errors are widely reported for MLIPs, an open concern is …

Abstract Machine learning interatomic potentials (MLIPs) provide exceptional opportunities
to accurately simulate atomistic systems and/or accelerate the evaluation of diverse physical …

Abstract Machine-learned interatomic potentials (MLIPs) are typically trained on datasets
that encompass a restricted subset of possible input structures, which presents a potential …