No abstract

Structure plays a vital role in determining materials properties. In lithium ion cathode materials, the crystal structure defines the dimensionality and connectivity of interstitial sites, thus determining lithium ion diffusion kinetics. In most conventional cathode materials that are well-ordered, the average structure as seen in diffraction dictates the lithium ion diffusion pathways. Here, we show that this is not the case in a class of recently discovered high-capacity lithium-excess rocksalts. An average structure picture is no longer satisfactory to understand the performance of such disordered materials. Cation short-range order, hidden in diffraction, is not only ubiquitous in these long-range disordered materials, but fully controls the local and macroscopic environments for lithium ion transport. Our discovery identifies a crucial property that has previously been overlooked and provides guidelines for designing and engineering cation-disordered cathode materials.

The atomic environments at metal surfaces differ strongly from the bulk, and, in particular, in case of reconstructions or imperfections at "real surfaces," very complicated atomic configurations can be present. This structural complexity poses a significant challenge for the development of accurate interatomic potentials suitable for large-scale molecular dynamics simulations. In recent years, artificial neural networks (NN) have become a promising new method for the construction of potential-energy surfaces for difficult systems. In the present work, we explore the applicability of such high-dimensional NN potentials to metal surfaces using copper as a benchmark system. A detailed analysis of the properties of bulk copper and of a wide range of surface structures shows that NN potentials can provide results of almost density functional theory (DFT) quality at a small fraction of the computational costs.

Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this article, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.

Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potentialenergy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.

Best practices in machine learning for chemistryStatistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports.

Fluorine substitution is a critical enabler for improving the cycle life and energy density of disordered rocksalt (DRX) Li‐ion battery cathode materials which offer prospects for high energy density cathodes, without the reliance on limited mineral resources. Due to the strong Li–F interaction, fluorine also is expected to modify the short‐range cation order in these materials which is critical for Li‐ion transport. In this work, density functional theory and Monte Carlo simulations are combined to investigate the impact of Li–F short‐range ordering on the formation of Li percolation and diffusion in DRX materials. The modeling reveals that F substitution is always beneficial at sufficiently high concentrations and can, surprisingly, even facilitate percolation in compounds without Li excess, giving them the ability to incorporate more transition metal redox capacity and thereby higher energy density. It is found that for F levels below 15%, its effect can be beneficial or disadvantageous depending on the intrinsic short‐range order in the unfluorinated oxide, while for high fluorination levels the effects are always beneficial. Using extensive simulations, a map is also presented showing the trade‐off between transition‐metal capacity, Li‐transport, and synthetic accessibility, and two of the more extreme predictions are experimentally confirmed.

Amorphous Li-ion conductors are important solid-state electrolytes. However, Li transport in these systems is much less understood than for crystalline materials. We investigate amorphous LiPON electrolytes via ab initio molecular dynamics, providing atomistic-level insight into the mechanisms underlying the Li+ mobility. We find that the latter is strongly influenced by the chemistry and connectivity of phosphate polyanions near Li+. Amorphization generates edge-sharing polyhedral connections between Li­(O,N)4 and P­(O,N)4, and creates under- and overcoordinated Li sites, which destabilizes the Li+ and enhances their mobility. N substitution for O favors conductivity in two ways: (1) excess Li accompanying 1­(N):1­(O) substitutions introduces extra carriers; (2) energetically favored N-bridging substitutions condense phosphate units and densify the structure, which, counterintuitively, corresponds to higher Li+ mobility. Finally, bridging N is not only less electronegative than O but also engaged in strong covalent bonds with P. This weakens interactions with neighboring Li+ smoothing the way for their migration. When condensation of PO4 polyhedra leads to the formation of isolated O anions, the Li+ mobility is reduced, highlighting the importance of oxygen partial pressure control during synthesis. This detailed understanding of the structural mechanisms affecting Li+ mobility is the key for optimizing the conductivity of LiPON and other amorphous Li-ion conductors.