Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Combustion is a complex chemical system which involves thousands of chemical reactions
and generates hundreds of molecular species and radicals during the process. In this work …

Rechargeable solid-state batteries (SSBs) are of prime importance for develo** the
necessary safe and efficient energy infrastructures of the future. With several inherent …

We wish to describe a potential energy surface by using a basis of permutationally invariant
polynomials whose coefficients will be determined by numerical regression so as to …

Machine learning (ML) has shown to advance the research field of quantum chemistry in
almost any possible direction and has also recently been applied to investigate the …

Permutationally invariant polynomial (PIP) regression has been used to obtain machine-
learned potential energy surfaces, including analytical gradients, for many molecules and …

There has been great progress in develo** methods for machine-learned potential energy
surfaces. There have also been important assessments of these methods by comparing so …

The accuracy of the training data limits the accuracy of bulk properties from machine-learned
potentials. For example, hybrid functionals or wave-function-based quantum chemical …

Computation of intermolecular interactions is a challenge in drug discovery because
accurate ab initio techniques are too computationally expensive to be routinely applied to …

The theoretical treatment of ionic liquids must focus now on more realistic models while at
the same time kee** an accurate methodology when following recent ionic liquids …