This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design.

Molecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future. This article is characterized under: Software > Simulation Methods Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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