Compact atomic descriptors enable accurate predictions via linear models

Tensor-Reduced Atomic Density Representations

Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.

Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library

A non-trivial interplay rules the relationship between the structure and the chemophysical properties of a nanoparticle. In this context, characterization experiments, molecular dynamics simulations and electronic structure calculations may allow the variables that determine a given property to be pinpointed. Conversely, a rigorous computational characterization of the geometry and chemical ordering of metallic nanoparticles and nanoalloys enables discrimination of which descriptors could be linked with their stability and performance. To this end, we introduce a modular and open-source library, Sapphire, which may classify the structural characteristics of a given nanoparticle through several structural analysis techniques and order parameters. A special focus is geared towards using geometrical descriptors to make predictions on a given nanoparticle's catalytic activity.

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Li-S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulfide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li-S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe-N4-C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyze the adsorption trends on Zn-N4-C SAC and observe similar adsorption strength and behavior with the Fe-N4-C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption.

Data-driven simulation and characterisation of gold nanoparticle melting

The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

The Interplay between Diradical Character and Stability in Organic Molecules

The number of scientific papers on the unique properties and the potential for various applications of compounds with a diradical character is growing constantly. The diradical character enhances and even engenders certain desired optical properties and its modulation is a modern molecular design strategy. Nowadays, molecules with a non-zero diradical character are regarded as promising materials for new-generation and highly efficient solar cells and photonics devices. What is the price, however, of the unique properties of open-shell compounds? Alongside all the benefits, the diradical character is usually associated with low stability and high reactivity—unwanted molecular qualities for practical purposes. Thus, from a fundamental and applied point of view, it is important to investigate the correlation between the diradical character and laboratory stability, which is the goal of the present paper. Here, we report a combined quantum–chemical study (conceptual DFT and spin-projected HF theory) and multivariate analysis of the diradical character of a series of o- and p-quinomethides, for the stability of which experimental data are available. Our results reveal that a compromise between the diradical character and laboratory stability of a molecule is feasible and that the relationship between these two quantities can be understood in the framework of Clar’s sextet theory.