Search

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.

While control over crystal structure is one of the primary objectives in crystal growth, the present lack of predictive understanding of the mechanisms driving structure selection precludes the predictive synthesis of polymorphic materials. We address the formation of off-stoichiometric intermediates as one such handle driving polymorph selection in the diverse class of MnO2-framework structures. Specifically, we build on the recent benchmark of the SCAN functional for the ab initio modeling of MnO2 to examine the effect of alkali-insertion, protonation, and hydration to derive the thermodynamic conditions favoring the formation of the most common MnO2 phases-β, γ, R, α, δ, and λ-from aqueous solution. We explain the phase selection trends through the geometric and chemical compatibility of the alkali cations and the available phases, the interaction of water with the system, and the critical role of protons. Our results offer both a quantitative synthesis roadmap for this important class of functional oxides, and a description of the various structural phase transformations that may occur in this system.

The space of metastable materials offers promising new design opportunities for next-generation technological materials, such as complex oxides, semiconductors, pharmaceuticals, steels, and beyond. Although metastable phases are ubiquitous in both nature and technology, only a heuristic understanding of their underlying thermodynamics exists. We report a large-scale data-mining study of the Materials Project, a high-throughput database of density functional theory-calculated energetics of Inorganic Crystal Structure Database structures, to explicitly quantify the thermodynamic scale of metastability for 29,902 observed inorganic crystalline phases. We reveal the influence of chemistry and composition on the accessible thermodynamic range of crystalline metastability for polymorphic and phase-separating compounds, yielding new physical insights that can guide the design of novel metastable materials. We further assert that not all low-energy metastable compounds can necessarily be synthesized, and propose a principle of 'remnant metastability'-that observable metastable crystalline phases are generally remnants of thermodynamic conditions where they were once the lowest free-energy phase.

Lattice models, also known as generalized Ising models or cluster expansions, are widely used in many areas of science and are routinely applied to the study of alloy thermodynamics, solid-solid phase transitions, magnetic and thermal properties of solids, fluid mechanics, and others. However, the problem of finding and proving the global ground state of a lattice model, which is essential for all of the aforementioned applications, has remained unresolved for relatively complex practical systems, with only a limited number of results for highly simplified systems known. In this paper, we present a practical and general algorithm that provides a provable periodically constrained ground state of a complex lattice model up to a given unit cell size and in many cases is able to prove global optimality over all other choices of unit cell. We transform the infinite-discrete-optimization problem into a pair of combinatorial optimization (MAX-SAT) and nonsmooth convex optimization (MAX-MIN) problems, which provide upper and lower bounds on the ground state energy, respectively. By systematically converging these bounds to each other, we may find and prove the exact ground state of realistic Hamiltonians whose exact solutions are difficult, if not impossible, to obtain via traditional methods. Considering that currently such practical Hamiltonians are solved using simulated annealing and genetic algorithms that are often unable to find the true global energy minimum and inherently cannot prove the optimality of their result, our paper opens the door to resolving longstanding uncertainties in lattice models of physical phenomena. An implementation of the algorithm is available at https://github.com/dkitch/maxsat-ising.

Cation disorder is a phenomenon that is becoming increasingly important for the design of high-energy lithium transition metal oxide cathodes (LiMO2) for Li-ion batteries. Disordered Li-excess rocksalts have recently been shown to achieve high reversible capacity, while in operando cation disorder has been observed in a large class of ordered compounds. The voltage slope (dVdxLi) is a critical quantity for the design of cation-disordered rocksalts, as it controls the Li capacity accessible at voltages below the stability limit of the electrolyte (∼4.5-4.7 V). In this study, we develop a lattice model based on first principles to understand and quantify the voltage slope of cation-disordered LiMO2. We show that cation disorder increases the voltage slope of Li transition metal oxides by creating a statistical distribution of transition metal environments around Li sites, as well as by allowing Li occupation of high-voltage tetrahedral sites. We further demonstrate that the voltage slope increase upon disorder is generally smaller for high-voltage transition metals than for low-voltage transition metals due to a more effective screening of Li-M interactions by oxygen electrons. Short-range order in practical disordered compounds is found to further mitigate the voltage slope increase upon disorder. Finally, our analysis shows that the additional high-voltage tetrahedral capacity induced by disorder is smaller in Li-excess compounds than in stoichiometric LiMO2 compounds.

Cation disorder is a phenomenon that is becoming increasingly important for the design of high-energy lithium transition-metal oxide positive electrodes (LiMO2) for Li-ion batteries. Disordered Li-excess rocksalts have recently been shown to achieve high reversible capacity, and in operando cation disorder (i.e., disorder induced by electrochemical cycling) has been observed in a large class of ordered materials. Despite the growing importance of cation disorder in the Li-ion battery field, very little is known about the effect of cation disorder on the average voltage (i.e., energy density) of lithium transition metal oxides. In this study, we use first-principles methods to demonstrate that, depending on the transition metal species, cation disorder can lead to an increase or a decrease of the average voltage of lithium transition metal oxides. We further demonstrate that the Ni3+/4+ redox can be high in disordered compounds, so that it may be preceded by oxygen activity. Finally, we establish rules for the voltage evolution of compounds that experience in operando disorder.