The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

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 …

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 …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

The development of accurate and transferable machine learning (ML) potentials for
predicting molecular energetics is a challenging task. The process of data generation to train …

Abstract Machine learning models are changing the paradigm of molecular modeling, which
is a fundamental tool for material science, chemistry, and computational biology. Of …

Traditional force fields cannot model chemical reactivity, and suffer from low generality
without re-fitting. Neural network potentials promise to address these problems, offering …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

Machine learning has emerged as an invaluable tool in many research areas. In the present
work, we harness this power to predict highly accurate molecular infrared spectra with …