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

The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

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 …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Area-selective atomic layer deposition (AS-ALD) is a bottom-up fabrication technique that
may revolutionize the semiconductor manufacturing process. Because the efficiency and …

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 …

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 …

Quantum chemical simulations can be greatly accelerated by constructing machine learning
potentials, which is often done using active learning (AL). The usefulness of the constructed …

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 emergence of artificial intelligence is profoundly impacting computational chemistry,
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …