Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

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 …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

The distribution of ions at the air/water interface plays a decisive role in many natural
processes. Several studies have reported that larger ions tend to be surface-active, implying …

Two-dimensional electrocatalysts have attracted great interest in recent years for renewable
energy applications. However, the atomistic mechanisms are still under debate. Here we …

Machine learning models for the potential energy of multi-atomic systems, such as the deep
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …

Advances in machine learning (ML) provide the means to bypass bottlenecks in the
discovery of new electrocatalysts using traditional approaches. In this review, we highlight …

We present a new approach to construct machine-learned interatomic potentials including
long-range electrostatic interactions based on a charge equilibration scheme. This new …

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …

Abstract Machine learned interatomic interaction potentials have enabled efficient and
accurate molecular simulations of closed systems. However, external fields, which can …