Lightweight lattice Boltzmann.

A regularized version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows significantly lower memory requirements and data access costs. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers.

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

Quantum ESPRESSO: One Further Step toward the Exascale.

We review the status of the Quantum ESPRESSO software suite for electronic-structure calculations based on plane waves, pseudopotentials, and density-functional theory. We highlight the recent developments in the porting to GPUs of the main codes, using an approach based on OpenACC and CUDA Fortran offloading. We describe, in particular, the results achieved on linear-response codes, which are one of the distinctive features of the Quantum ESPRESSO suite. We also present extensive performance benchmarks on different GPU-accelerated architectures for the main codes of the suite.

Hydrodynamics from statistical mechanics: combined dynamical-NEMD and conditional sampling to relax an interface between two immiscible liquids.

We present a method to study hydrodynamic phenomena from atomistic simulations. In statistical mechanics, these fields are computed as the ensemble average over the time dependent probability density function corresponding to the time evolution of an initial conditional probability density function consistent with some initial conditions. These initial conditions typically consist in constraints on some macroscopic fields, e.g. the density field. We show how these processes can be studied by combining the dynamical approach to non-equilibrium molecular dynamics with the restrained simulation approach. As an illustration of our method, we study the relaxation to the equilibrium of an interface between two immiscible liquids. We show that, at a variance with the local time average method, the standard atomistic approach used in this field, our method is able to produce (macroscopic) fields satisfying the symmetry conditions of the problem.