Short-Range Order and Unusual Modes of Nickel Redox in a Fluorine-Substituted Disordered Rocksalt Oxide Lithium-Ion Cathode

Fluorine substitution for oxygen in cation-disordered lithium-excess transition metal oxides (Li1+xTM1-xO2) used as lithium-ion cathodes was recently demonstrated to improve the reversibility of the processes taking place on charge and discharge by reducing the amount of oxygen loss on charge and preventing major structural rearrangements at high voltage. Yet, little is understood about how fluorine incorporates the oxide structure and impacts its electrochemical properties. Here, we use a combination of experimental (solid-state nuclear magnetic resonance (NMR) spectroscopy) and theoretical techniques (density functional theory (DFT) calculations and Monte Carlo simulations) to investigate the evolution of the local structure around fluorine and lithium and the oxidation state of redox-active nickel during charge and discharge of the Li1.15Ni0.45Ti0.3Mo0.1O1.85F0.15 (LNF15) cathode. We show that fluorine doping introduces short-range order in as-synthesized LNF15 by incorporating in lithium-rich sites with five or six lithium nearest neighbors. We observe the emergence of new signals in the ex situ 19F NMR spectra taken at high states of charge, which we tentatively assign to undercoordinated, diamagnetic fluorine environments seen in our computed models. Our theoretical results also suggest that octahedral nickel ions directly bonded to fluorine follow a different oxidation mechanism than those surrounded by six oxygens, forming Ni3+ intermediates instead of oxidizing from Ni2+ directly to Ni4+. While the oxidation of Ni2+ toward Ni4+ is incomplete in oxides, due to overlap between the oxygen and the nickel valence states, this result suggests that fluorination may be an efficient strategy to utilize the Ni2+/Ni4+ redox reservoir to a greater extent

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials

Autonomous decision making for solid-state synthesis of inorganic materials

To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns from their outcomes to identify an optimal set of precursors that leads to maximal yield of that target. Initial experiments are selected based on thermochemical data collected from first principles calculations, which enable the identification of precursors exhibiting large thermodynamic force to form the desired target. Should the initial experiments fail, their associated reaction paths are determined by sampling a range of synthesis temperatures and identifying their products. ARROWS3 then uses this information to pinpoint which intermediate reactions consume most of the available free energy associated with the starting materials. In subsequent experimental iterations, precursors are selected to avoid such unfavorable reactions and therefore maintain a strong driving force to form the target. We validate this approach on three experimental datasets containing results from more than 200 distinct synthesis procedures. When compared to several black-box optimization algorithms, ARROWS3 identifies the most effective set of precursors for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of using domain knowledge in the design of optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms

Design Principles for High-Capacity Mn-Based Cation-Disordered Rocksalt Cathodes

Mn-based Li-excess cation-disordered rocksalt (DRX) oxyfluorides are promising candidates for next-generation rechargeable battery cathodes owing to their large energy densities, the earth abundance, and low cost of Mn. In this work, we synthesized and electrochemically tested four representative compositions in the Li-Mn-O-F DRX chemical space with various Li and F content. While all compositions achieve higher than 200 mAh g−1 initial capacity and good cyclability, we show that the Li-site distribution plays a more important role than the metal-redox capacity in determining the initial capacity, whereas the metal-redox capacity is more closely related to the cyclability of the materials. We apply these insights and generate a capacity map of the Li-Mn-O-F chemical space, LixMn2-xO2-yFy (1.167 ≤ x ≤ 1.333, 0 ≤ y ≤ 0.667), which predicts both accessible Li capacity and Mn-redox capacity. This map allows the design of compounds that balance high capacity with good cyclability

Self-driven lattice-model Monte Carlo simulations of alloy thermodynamic

Monte Carlo (MC) simulations of lattice models are a widely used way to compute thermodynamic properties of substitutional alloys. A limitation to their more widespread use is the difficulty of driving a MC simulation in order to obtain the desired quantities. To address this problem, we have devised a variety of high-level algorithms that serve as an interface between the user and a traditional MC code. The user specifies the goals sought in a high-level form that our algorithms convert into elementary tasks to be performed by a standard MC code. For instance, our algorithms permit the determination of the free energy of an alloy phase over its entire region of stability within a specified accuracy, without requiring any user intervention during the calculations. Our algorithms also enable the direct determination of composition-temperature phase boundaries without requiring the calculation of the whole free energy surface of the alloy system

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.

In the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase. Understanding the competition between thermodynamics and kinetics is a fundamental step towards the rational synthesis of target materials. Here, we use in situ synchrotron X-ray diffraction to investigate the multistage crystallization pathways of the important two-layer (P2) sodium oxides Na0.67MO2 (M = Co, Mn). We observe a series of fast non-equilibrium phase transformations through metastable three-layer O3, O3' and P3 phases before formation of the equilibrium two-layer P2 polymorph. We present a theoretical framework to rationalize the observed phase progression, demonstrating that even though P2 is the equilibrium phase, compositionally unconstrained reactions between powder precursors favour the formation of non-equilibrium three-layered intermediates. These insights can guide the choice of precursors and parameters employed in the solid-state synthesis of ceramic materials, and constitutes a step forward in unravelling the complex interplay between thermodynamics and kinetics during materials synthesis

S=1/2 chains and spin-Peierls transition in TiOCl

We study TiOCl as an example of an S=1/2 layered Mott insulator. From our analysis of new susceptibility data, combined with LDA and LDA+U band structure calculations, we conclude that orbital ordering produces quasi-one-dimensional spin chains and that TiOCl is a new example of Heisenberg-chains which undergo a spin-Peierls transition. The energy scale is an order of magnitude larger than that of previously known examples. The effects of non-magnetic Sc impurities are explained using a model of broken finite chains.Comment: 5 pages, 5 figures (color); details on crystal growth added; to be published in Phys. Rev.

The Collins-Roscoe mechanism and D-spaces

We prove that if a space X is well ordered $(\alpha A)$, or linearly semi-stratifiable, or elastic then X is a D-space

Using bond-length dependent transferable force constants to predict vibrational entropies in Au-Cu, Au-Pd, and Cu-Pd alloys

A model is tested to rapidly evaluate the vibrational properties of alloys with site disorder. It is shown that length-dependent transferable force constants exist, and can be used to accurately predict the vibrational entropy of substitutionally ordered and disordered structures in Au-Cu, Au-Pd, and Cu-Pd. For each relevant force constant, a length- dependent function is determined and fitted to force constants obtained from first-principles pseudopotential calculations. We show that these transferable force constants can accurately predict vibrational entropies of L1$_{2}$-ordered and disordered phases in Cu$_{3}$Au, Au$_{3}$Pd, Pd$_{3}$Au, Cu$_{3}$Pd, and Pd$_{3}$Au. In addition, we calculate the vibrational entropy difference between L1$_{2}$-ordered and disordered phases of Au$_{3}$Cu and Cu$_{3}$Pt.Comment: 9 pages, 6 figures, 3 table