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

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 …

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

Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …

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 …

Conventional machine-learning (ML) models in computational chemistry learn to directly
predict molecular properties using quantum chemistry only for reference data. While these …

We propose a semiempirical quantum chemical method, designed for the fast calculation of
molecular Geometries, vibrational Frequencies and Non-covalent interaction energies …

Enthalpies of formation and reaction are important thermodynamic properties that have a
crucial impact on the outcome of chemical transformations. Here we implement the …

Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials
properties. It is common wisdom that molecular dynamics simulations should obey physical …

Semi-empirical quantum chemical approaches are known to compromise accuracy for the
feasibility of calculations on huge molecules. However, the need for ultrafast calculations in …