Kristin A. Persson

The thermodynamic scale of inorganic crystalline metastability

Data-mining the stability of 29,902 material phases reveals the thermodynamic landscape of inorganic crystalline metastability. 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.

Computational and experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first- principles high-throughput screening†

A new group of thermoelectric materials, trigonal and tetragonal XYZ2 (X, Y: rare earth or transition metals, Z: group VI elements), the prototype of which is TmAgTe2, is identified by means of high-throughput computational screening and experiment. Based on density functional theory calculations, this group of materials is predicted to attain high zT (i.e. B1.8 for p-type trigonal TmAgTe2 at 600 K). Among approximately 500 chemical variants of XYZ2 explored, many candidates with good stability and favorable electronic band structures (with high band degeneracy leading to high power factor) are presented. Trigonal TmAgTe2 has been synthesized and exhibits an extremely low measured thermal conductivity of 0.2–0.3 W m � 1 K � 1 for T 4 600 K. The zT value achieved thus far for p-type trigonal TmAgTe2 is approximately 0.35, and is limited by a low hole concentration (B10 17 cm � 3 at room temperature). Defect calculations indicate that TmAg antisite defects are very likely to form and act as hole killers. Further defect engineering to reduce such XY antisites is deemed important to optimize the zT value of the p-type TmAgTe2.

Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment

We present an overview and preliminary analysis of computed thermoelectric properties for more than 48 000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined with MP band structures and the BoltzTraP code under a constant relaxation time approximation (R2 = 0.79). We additionally find that scissoring the band gap to the experimental value improves the agreement. We find that power factors calculated with a constant and universal relaxation time approximation show much poorer agreement with experiment (R2 = 0.33). We test two minimum thermal conductivity models (Clarke and Cahill–Pohl), finding that both these models reproduce measured values fairly accurately (R2 = 0.82) using parameters obtained from computation. Additionally, we analyze this data set to gain broad insights into the effects of chemistry, crystal structure, and electronic structure on thermoelectric properties. For example, our computations indicate that oxide band structures tend to produce lower power factors than those of sulfides, selenides, and tellurides, even under the same doping and relaxation time constraints. We also list families of compounds identified to possess high valley degeneracies. Finally, we present a clustering analysis of our results. We expect that these studies should help guide and assess future high-throughput computational screening studies of thermoelectric materials.

Evaluation of sulfur spinel compounds for multivalent battery cathode applications

The rapid growth of portable consumer electronics and electric vehicles demands new battery technologies with greater energy stored at a reduced cost. Energy storage solutions based on multivalent metals, such as Mg, could significantly increase the energy density as compared to lithium-ion based technology. In this paper, we employ density functional theory calculations to systematically evaluate the performance, such as thermodynamic stability, ion diffusivity and voltage, of a group of 3d transition-metal sulfur-spinel compounds (21 in total) for multivalent cathode applications. Based on our calculations, Cr2S4, Ti2S4 and Mn2S4 spinel compounds exhibit improved Mg2+ mobility (diffusion activation energy <650 meV) relative to their oxide counterparts, however the improved mobility comes at the expense of lower voltage and thereby lower theoretical specific energy. Ca2+ intercalating into Cr2S4 spinel exhibits a low diffusion activation barrier of 500 meV and a voltage of ∼2 V, revealing a potential cathode for use in Ca rechargeable batteries.

First Principles Study of the Li–Bi–F Phase Diagram and Bismuth Fluoride Conversion Reactions with Lithium

First principles calculations have been used to explore the Li–Bi–F ternary phase diagram. Our results confirm the thermodynamic stability of previously observed phases and find no new phases in this system. Electrochemical voltage profiles for the reaction of Li and BiF3 are in reasonable agreement with experiment. The driving force to form ternary Li–Bi–F intermediates is small. We also investigated the effect of particle size on the reaction voltage and find a potential decrease when nanoscale vs bulk Bi forms

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

Significance Combining high-throughput computation and experiment accelerates the discovery of photoelectrocatalysts for water oxidation and explains the origin of their functionality, establishing ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight. The limited number of known low-band-gap photoelectrocatalytic materials poses a significant challenge for the generation of chemical fuels from sunlight. Using high-throughput ab initio theory with experiments in an integrated workflow, we find eight ternary vanadate oxide photoanodes in the target band-gap range (1.2–2.8 eV). Detailed analysis of these vanadate compounds reveals the key role of VO4 structural motifs and electronic band-edge character in efficient photoanodes, initiating a genome for such materials and paving the way for a broadly applicable high-throughput-discovery and materials-by-design feedback loop. Considerably expanding the number of known photoelectrocatalysts for water oxidation, our study establishes ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight and demonstrates our high-throughput theory–experiment pipeline as a prolific approach to materials discovery.

YCuTe2: a member of a new class of thermoelectric materials with CuTe4-based layered structure

Intrinsically doped samples of YCuTe2 were prepared by solid state reaction of the elements. Based on the differential scanning calorimetry and the high temperature X-ray diffraction analyses, YCuTe2 exhibits a first order phase transition at ∼440 K from a low-temperature-phase crystallizing in the space group Pm1 to a high-temperature-phase in P. Above the phase transition temperature, partially ordered Cu atoms become completely disordered in the crystal structure. Small increases to the Cu content are observed to favour the formation of the high temperature phase. We find no indication of superionic Cu ions as for binary copper chalcogenides (e.g., Cu2Se or Cu2Te). All investigated samples exhibit very low thermal conductivities (as low as ∼0.5 W m−1 K−1 at 800 K) due to highly disordered Cu atoms. Electronic structure calculations are employed to better understand the high thermoelectric efficiency for YCuTe2. The maximum thermoelectric figure of merit, zT, is measured to be ∼0.75 at 780 K for Y0.96Cu1.08Te2, which is promising for mid-temperature thermoelectric applications.

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A–B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner–Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.Machine learning a defect’s effectA method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys.

Concentration dependent electrochemical properties and structural analysis of a simple magnesium electrolyte: magnesium bis(trifluoromethane sulfonyl)imide in diglyme

Development of Mg electrolytes that can plate/strip Mg is not trivial and remains one of the major roadblocks to advance Mg battery research. Halogen-free electrolyte has attracted great attention due to its high stability, less corrosive nature and compatibility with Mg metal anodes. However, the electrochemical properties of such electrolytes have not been analytically evaluated in the literature. Herein, we report a systematic study of the concentration-dependent electrochemical and mass transport properties of a non-aqueous, halogen-free Mg electrolyte composed of magnesium bis(trifluoromethane sulfonyl)imide in diglyme (Mg(TFSI)2/G2). Specifically, cyclic voltammograms confirm that plating and stripping of Mg in Mg(TFSI)2/G2 electrolyte occur over a wide concentration range. Results suggest a comparably difficult magnesium dissolution in Mg(TFSI)2/G2 electrolyte in contrast to in Grignard based electrolytes. Dissolution overpotential shows a non-monotonic dependence on electrolyte concentration, it requires an ∼2 V overpotential to deposit Mg. Findings also reveal concentration-dependent mass transport properties, including concentration-dependent electrolyte diffusivity and transference number. The atomic environment of the Mg(TFSI)2/G2, as being further explored by Nuclear Magnetic Resonance (NMR) measurement and Molecular Dynamics (MD) simulations, is coupled with the electrochemical measurements to explain the observed concentration-dependent mass transport properties.