Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

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 …

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 …

Graph neural networks (GNNs) constitute a class of deep learning methods for graph data.
They have wide applications in chemistry and biology, such as molecular property …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Capacitive storage devices allow for fast charge and discharge cycles, making them the
perfect complements to batteries for high power applications. Many materials display …

It is well known in the field of machine learning that committee models improve accuracy,
provide generalization error estimates, and enable active learning strategies. In this work …

In a previous paper Fan et al (2021 Phys. Rev. B 104, 104309), we developed the
neuroevolution potential (NEP), a framework of training neural network based machine …

Despite the success of physics-informed neural networks (PINNs) in approximating partial
differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution in …