In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Machine learning (ML) is having an increasing impact on the physical sciences,
engineering, and technology and its integration into molecular simulation frameworks holds …

Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain
the high accuracy, while inflicting little computational demands. On the downside, they need …

While RNA appears as a good candidate for the first autocatalytic systems preceding the
emergence of modern life, the synthesis of RNA oligonucleotides without enzymes remains …

The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a
correction to a low-level energy calculation, provides a versatile route to accelerate high …

We train an equivariant machine learning (ML) model to predict energies and forces for
hydrogen combustion under conditions of finite temperature and pressure. This challenging …

The global energy optimization problem is an acute and important problem in chemistry. It is
crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given …

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-
purpose method that was shown to achieve high accuracy for many applications with a …

The role of AI within science is growing. Here we assess its impact on research and argue
that AI often lacks reproducibility, transparency, objectivity, and mechanistic understanding …

This article gives a perspective on the progress of AI tools in computational chemistry
through the lens of the author's decade-long contributions put in the wider context of the …