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

This article summarizes technical advances contained in the fifth major release of the Q-
Chem quantum chemistry program package, covering developments since 2015. A …

Given access to accurate solutions of the many-electron Schrödinger equation, nearly all
chemistry could be derived from first principles. Exact wave functions of interesting chemical …

Abstract Ignition delay times for 1, 3-butadiene oxidation were measured in five different
shock tubes and in a rapid compression machine (RCM) at thermodynamic conditions …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

Determining the stability of molecules and condensed phases is the cornerstone of atomistic
modeling, underpinning our understanding of chemical and materials properties and …

We present the GMTKN55 benchmark database for general main group thermochemistry,
kinetics and noncovalent interactions. Compared to its popular predecessor GMTKN30 …

Over the last 20 to 25 years theoretical chemistry (particularly theoretical chemical kinetics)
has played an increasingly important role in developing chemical kinetics models for …

The fields of theoretical and computational chemistry have come a long way since their
inception in the mid-20th century. Fifty years ago, only rudimentary approximations for very …

The properties of catenated nitrogen molecules, molecules containing internal chains of
bonded nitrogen atoms, is of fundamental scientific interest in chemical structure and …