Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

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 …

The annotation of small molecules remains a major challenge in untargeted mass
spectrometry-based metabolomics. We here critically discuss structured elucidation …

We investigate the impact of choosing regressors and molecular representations for the
construction of fast machine learning (ML) models of 13 electronic ground-state properties of …

We present a family of minimally empirical double-hybrid DFT functionals parametrized
against the very large and diverse GMTKN55 benchmark. The very recently proposed …

High-level quantum mechanical (QM) calculations are indispensable for accurate
explanation of natural phenomena on the atomistic level. Their staggering computational …

Introduction to Computational Chemistry 3rd Edition provides a comprehensive account of
the fundamental principles underlying different computational methods. Fully revised and …

Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address
chemical phenomena in complex molecular environments. Whereas this modeling approach …

A primary goal of metabolomics studies is to fully characterize the small-molecule
composition of complex biological and environmental samples. However, despite advances …