Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Over recent years, the use of statistical learning techniques applied to chemical problems
has gained substantial momentum. This is particularly apparent in the realm of physical …

Standfirst Over the last decade, we have witnessed the emergence of ever more machine
learning applications in all aspects of the chemical sciences. Here, we highlight specific …

Rather than numerically solving the computationally demanding equations of quantum or
statistical mechanics, machine learning methods can infer approximate solutions …

The field of artificial intelligence (AI) and machine learning (ML) has exploded over the past
decade, largely due to fundamental advances in deep learning algorithms and broad …

Encouraged by growing computing power and algorithmic development, machine learning
technologies have become powerful tools for a wide variety of application areas, spanning …

MLatom is a program package designed for computationally efficient simulations of atomistic
systems with machine‐learning (ML) algorithms. It can be used out‐of‐the‐box as a stand …

First-principles-based exploration of chemical space deepens our understanding of
chemistry and might help with the design of new molecules, materials or experiments. Due …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …