scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science.

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods.

Fast evaluation of spherical harmonics with sphericart.

Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmospheric sciences to signal processing and computer graphics. More recently, they have become a key component of rotationally equivariant models in geometric machine learning, including applications to atomic-scale modeling of molecules and materials. We present an elegant and efficient algorithm for the evaluation of the real-valued spherical harmonics.

Unified theory of atom-centered representations and message-passing machine-learning schemes.

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, which are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), which are used as a basis for a body-ordered, symmetry-adapted expansion of the targets.

Efficient implementation of atom-density representations.

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection.

DUBS: A Framework for Developing irectory of seful enchmarking ets for Virtual Screening.

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank.

Modelling of framework materials at multiple scales: current practices and open questions.

The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties.

Ab Initio Derived Force Fields for Zeolitic Imidazolate Frameworks: MOF-FF for ZIFs.

In this paper, we parametrized in a consistent way a new force field for a range of different zeolitic imidazolate framework systems (ZIF-8, ZIF-8(H), ZIF-8(Br), and ZIF-8(Cl)), extending the MOF-FF parametrization methodology in two aspects. First, we implemented the possibility to use periodic reference data in order to prevent the difficulty of generating representative finite clusters. Second, a new optimizer based on the covariance matrix adaptation evolutionary strategy (CMA-ES) was employed during the parametrization process.

Structure and Dynamics of Water Confined in Imogolite Nanotubes.

We have studied the properties of water adsorbed inside nanotubes of hydrophilic imogolite, an aluminum silicate clay mineral, by means of molecular simulations. We used a classical force field to describe the water and the flexible imogolite nanotube and validated it against the data obtained from first-principles molecular dynamics. With it, we observe a strong structuration of the water confined in the nanotube, with specific adsorption sites and a distribution of hydrogen bond patterns.

Forced intrusion of water and aqueous solutions in microporous materials: from fundamental thermodynamics to energy storage devices.

We review the high pressure forced intrusion studies of water in hydrophobic microporous materials such as zeolites and MOFs, a field of research that has emerged some 15 years ago and is now very active. Many of these studies are aimed at investigating the possibility of using these systems as energy storage devices. A series of all-silica zeolites (zeosil) frameworks were found suitable for reversible energy storage because of their stability with respect to hydrolysis after several water intrusion-extrusion cycles.

Recent advances in the computational chemistry of soft porous crystals.

Here we highlight recent progress in the field of computational chemistry of nanoporous materials, focusing on methods and studies that address the extraordinary dynamic nature of these systems: the high flexibility of their frameworks, the large-scale structural changes upon external physical or chemical stimulation, and the presence of defects and disorder. The wide variety of behavior demonstrated in soft porous crystals, including the topical class of metal-organic frameworks, opens new challenges for computational chemistry methods at all scales.

Fermi resonance in CO: Mode assignment and quantum nuclear effects from first principles molecular dynamics.

Vibrational spectroscopy is a fundamental tool to investigate local atomic arrangements and the effect of the environment, provided that the spectral features can be correctly assigned. This can be challenging in experiments and simulations when double peaks are present because they can have different origins. Fermi dyads are a common class of such doublets, stemming from the resonance of the fundamental excitation of a mode with the overtone of another.