The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

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 …

We propose a machine learning (ML)-based strategy for an inexpensive calculation of
excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical …

We present in this work the emle-engine package (https://github. com/chemle/emle-
engine)─ the implementation of a new machine learning embedding scheme for hybrid …

Machine-learning algorithms have been proposed to capture electrostatic interactions by
using effective partial charges. These algorithms often rely on a pretrained model for partial …

Enzyme design faces challenges related to the implementation of the basic principles that
govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic …

Full-dimensional potential energy surfaces (PESs) based on machine learning (ML)
techniques provide a means for accurate and efficient molecular simulations in the gas and …

The interaction of condensed phase systems with external electric fields is of major
importance in a myriad of processes in nature and technology, ranging from the field …

Machine learning (ML) continues to revolutionize computational chemistry for accelerating
predictions and simulations by training on experimental or accurate but expensive quantum …

The application of quantum mechanics (QM)/molecular mechanics (MM) models for studying
dynamics in complex systems is nowadays well established. However, their significant …