Geoffroy Hautier

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.

Voltage, Stability and Diffusion Barrier Differences between Sodium-ion and Lithium-ion Intercalation Materials

To evaluate the potential of Na-ion batteries, we contrast in this work the difference between Na-ion and Li-ion based intercalation chemistries in terms of three key battery properties—voltage, phase stability and diffusion barriers. The compounds investigated comprise the layered AMO2 and AMS2 structures, the olivine and maricite AMPO4 structures, and the NASICON A3V2(PO4)3 structures. The calculated Na voltages for the compounds investigated are 0.18–0.57 V lower than that of the corresponding Li voltages, in agreement with previous experimental data. We believe the observed lower voltages for Na compounds are predominantly a cathodic effect related to the much smaller energy gain from inserting Na into the host structure compared to inserting Li. We also found a relatively strong dependence of battery properties on structural features. In general, the difference between the Na and Li voltage of the same structure, DVNa–Li, is less negative for the maricite structures preferred by Na, and more negative for the olivine structures preferred by Li. The layered compounds have the most negative DVNa–Li. In terms of phase stability, we found that open structures, such as the layered and NASICON structures, that are better able to accommodate the larger Na+ ion generally have both Na and Li versions of the same compound. For the close-packed AMPO4 structures, our results show that Na generally prefers the maricite structure, while Li prefers the olivine structure, in agreement with previous experimental work. We also found surprising evidence that the barriers for Na+ migration can potentially be lower than that for Li+ migration in the layered structures. Overall, our findings indicate that Na-ion systems can be competitive with Li-ion systems.

Unlocking the Potential of Cation-Disordered Oxides for Rechargeable Lithium Batteries

Disorderly Flow Lithium batteries are becoming ever more important in society. While their application used to be confined to portable electronics, they are now becoming the enabling technology for electric vehicles and grid storage for renewables. Generally, the flow of lithium ions into and out of battery electrodes is thought to require ordered materials. Lee et al. (p. 519, published online 9 January) used a combination of experimental work and computations to identify disordered electrode materials with high Li diffusion. The improved energy density properties could be attributed to compositions with excess lithium beyond the stoichiometric limit, leading to intermixing between the lithium and transition metal sublattices and the formation of a percolation network providing specific lithium transport pathways. Cation-disordered compounds achieve high lithium (Li) storage capacity, with scope for high–energy density Li battery electrodes. Nearly all high–energy density cathodes for rechargeable lithium batteries are well-ordered materials in which lithium and other cations occupy distinct sites. Cation-disordered materials are generally disregarded as cathodes because lithium diffusion tends to be limited by their structures. The performance of Li1.211Mo0.467Cr0.3O2 shows that lithium diffusion can be facile in disordered materials. Using ab initio computations, we demonstrate that this unexpected behavior is due to percolation of a certain type of active diffusion channels in disordered Li-excess materials. A unified understanding of high performance in both layered and Li-excess materials may enable the design of disordered-electrode materials with high capacity and high energy density.

Thinking Like a Chemist: Intuition in Thermoelectric Materials

The coupled transport properties required to create an efficient thermoelectric material necessitates a thorough understanding of the relationship between the chemistry and physics in a solid. We approach thermoelectric material design using the chemical intuition provided by molecular orbital diagrams, tight binding theory, and a classic understanding of bond strength. Concepts such as electronegativity, band width, orbital overlap, bond energy, and bond length are used to explain trends in electronic properties such as the magnitude and temperature dependence of band gap, carrier effective mass, and band degeneracy and convergence. The lattice thermal conductivity is discussed in relation to the crystal structure and bond strength, with emphasis on the importance of bond length. We provide an overview of how symmetry and bonding strength affect electron and phonon transport in solids, and how altering these properties may be used in strategies to improve thermoelectric performance.

Data Mined Ionic Substitutions for the Discovery of New Compounds

The existence of new compounds is often postulated by solid state chemists by replacing an ion in the crystal structure of a known compound by a chemically similar ion. In this work, we present how this new compound discovery process through ionic substitutions can be formulated in a mathematical framework. We propose a probabilistic model assessing the likelihood for ionic species to substitute for each other while retaining the crystal structure. This model is trained on an experimental database of crystal structures, and can be used to quantitatively suggest novel compounds and their structures. The predictive power of the model is demonstrated using cross-validation on quaternary ionic compounds. The different substitution rules embedded in the model are analyzed and compared to some of the traditional rules used by solid state chemists to propose new compounds (e.g., ionic size).

Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations

The discovery of new chemistries outperforming current lithium intercalation cathodes is of major technological importance. In this context, polyanionic systems with the potential to exchange multiple electrons per transition metal are particularly interesting because they could combine the safety of polyanion systems with higher specific energy. In this paper, we report on a series of new mixed polyanions compounds of formula AxM(YO3)(XO4) (with A = Na, Li; X = Si, As, P; Y = C, B; M = a redox active metal; and x = 0 to 3) identified by high-throughput ab initio computing. The computed stability of both lithium and sodium-based compounds is analyzed along with the voltage, specific energy and energy density of the lithium-based compounds. This analysis suggests several novel carbonophosphates and carbonosilicates as potential high capacity (>200 mAh/g) and specific energy (>700 Wh/kg) cathode materials for lithium-ion batteries.

First principles high throughput screening of oxynitrides for water-splitting photocatalysts

In this paper, we present a first principles high throughput screening system to search for new water-splitting photocatalysts. We use the approach to screen through nitrides and oxynitrides. Most of the known photocatalytic materials in the screened chemical space are reproduced. In addition, sixteen new materials are suggested by the screening approach as promising photocatalysts, including three binary nitrides, two ternary oxynitrides and eleven quaternary oxynitrides.

Synthesis and Electrochemical Properties of Monoclinic LiMnBO[sub 3] as a Li Intercalation Material

We investigated the structural stability and electrochemical properties of LiMnBO3 in the hexagonal and monoclinic form with ab initio computations and, for the first time, report electrochemical data on monoclinic LiMnBO3 . In contrast to the negligible Li-storage capacity in the hexagonal LiMnBO3 , a second cycle discharge capacity of 100mAh/g was achieved in the monoclinic LiMnBO3 , with good capacity retention over multiple cycles. Elevated temperature cycling indicates that the capacity of monoclinic LiMnBO3 is kinetically limited, and further improvement may be expected by addressing the Li ion and/or electron transport limitations.

Synthesis, Computed Stability, and Crystal Structure of a New Family of Inorganic Compounds: Carbonophosphates

Ab initio-based high-throughput computing and screening are now being used to search and predict new functional materials and novel compounds. However, systematic experimental validation on the predictions remains highly challenging, yet desired. Careful comparison between computational predictions and experimental results is sparse in the literature. Here we report on a systematic experimental validation on previously presented computational predictions of a novel alkali carbonophosphate family of compounds. We report the successful hydrothermal synthesis and structural characterization of multiple sodium carbonophosphates. The experimental conditions for formation of the carbonophosphates and the computational results are compared and discussed. We also demonstrate topotactic chemical de-sodiation of one of the compounds, indicating the potential use of this novel class of compounds as Li(+) or Na(+) insertion electrodes.

FireWorks: a dynamic workflow system designed for high-throughput applications

This paper introduces FireWorks, a workflow software for running high‐throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU‐hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high‐throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long‐running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data‐driven and high‐throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work. Copyright