Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful
computational method for fundamental research in science branches such as biology …

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 …

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 …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Structures and processes at water/metal interfaces play an important technological role in
electrochemical energy conversion and storage, photoconversion, sensors, and corrosion …

Since the seminal works on the application of density functional theory and the
computational hydrogen electrode to electrochemical CO2 reduction (eCO2R) and …

In the past two and a half decades machine learning potentials have evolved from a special
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …

The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing
an important role in areas such as climate change and the production of drugs. As the early …