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The performance of thermoelectric materials is determined by their electrical and thermal transport properties that are very sensitive to small modifications of composition and microstructure. Discovery and design of next-generation materials are starting to be accelerated by computational guidance. We review progress and challenges in the development of accurate and efficient first-principles methods for computing transport coefficients and illustrate approaches for both rapid materials screening and focused optimization. Particularly important and challenging are computations of electron and phonon scattering rates that enter the Boltzmann transport equations, and this is where there are many opportunities for improving computational methods. We highlight the first successful examples of computation-driven discoveries of high-performance materials and discuss avenues for tightening the interaction between theoretical and experimental materials discovery and optimization.

Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum-structure relationship that can be eventually mapped to physicochemical properties. The latent space of the autoencoder also provides a pathway to interpret the information content "hidden" in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes.

Quasi-elastic neutron scattering experiments on mixtures of poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt, a standard polymer electrolyte, led to the quantification of the effect of salt on segmental dynamics in the 1–10 A length scale. The monomeric friction coefficient characterizing segmental dynamics on these length scales increases exponentially with salt concentration. More importantly, we find that this change in monomeric friction alone is responsible for all of the observed nonlinearity in the dependence of ionic conductivity on salt concentration. Our analysis leads to a surprisingly simple relationship between macroscopic ion transport in polymers and dynamics at monomeric length scales.

Salt-in-ionic liquid electrolytes have attracted significant attention as potential electrolytes for next generation batteries largely due to their safety enhancements over typical organic electrolytes. However, recent experimental and computational studies have shown that under certain conditions alkali cations can migrate in electric fields as if they carried a net negative effective charge. In particular, alkali cations were observed to have negative transference numbers at small mole fractions of alkali metal salt that revert to the expected net positive transference numbers at large mole fractions. Simulations have provided some insights into these observations, where the formation of asymmetric ionic clusters, as well as a percolating ion network could largely explain the anomalous transport of alkali cations. However, a thermodynamic theory that captures such phenomena has not been developed, as ionic associations were typically treated via the formation of ion pairs. The theory presented herein, based on the classical polymer theories, describes thermoreversible associations between alkali cations and anions, where the formation of large, asymmetric ionic clusters and a percolating ionic network are a natural result of the theory. Furthermore, we present several general methods to calculate the effective charge of alkali cations in ionic liquids. We note that the negative effective charge is a robust prediction with respect to the parameters of the theory, and that the formation of a percolating ionic network leads to the restoration of net positive charges of the cations at large mole fractions of alkali metal salt. Overall, we find excellent qualitative agreement between our theory and molecular simulations in terms of ionic cluster statistics and the effective charges of the alkali cations.

Incorporation of elastomers into bioelectronics that reduces the mechanical mismatch between electronics and biological systems could potentially improve the long-term electronics-tissue interface. However, the chronic stability of elastomers in physiological conditions has not been systematically studied. Here, using electrochemical impedance spectrum we find that the electrochemical impedance of dielectric elastomers degrades over time in physiological environments. Both experimental and computational results reveal that this phenomenon is due to the diffusion of ions from the physiological solution into elastomers over time. Their conductivity increases by 6 orders of magnitude up to 10

Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This mutual weakening effect is explained by the formation of the anion-solvent charge-transfer complex, which we study for 16 anion-solvent combinations. This understanding of the oxidation mechanism allows the formulation of a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries., Based on computations, the authors show that voltage stability of battery electrolytes is determined not by a single component but by formation of charge transfer complexes between salt anions and solvent molecules. A physical model is proposed and validated on several common electrolyte compositions.

Strong ionic interactions in concentrated ionic liquids is shown to result in significant correlations and deviations from ideal solution behavior. We use rigorous concentrated solution theory coupled with molecular dynamics simulations to compute and explain the unusual dependence of transport properties on cation concentration in the Na + - Pyr 13 + - FSI − ionic liquid electrolyte. When accounting for intra- and inter-species correlation, beyond the commonly used uncorrelated Nernst-Einstein equation, an anomalously low and even negative transference number emerges for x NaFSI lower than 0.2. With increasing concentration the transference number monotonically increases, approaching unity, while the total conductivity decreases as the system transitions to a state resembling a single-ion solid-state electrolyte. The degree of spatial ionic association is explored further by employing unsupervised single-linkage clustering algorithm. We emphasize that strong ion-ion coupling in the electrolyte significantly impacts the trade-off between key electrolyte transport properties, and consequently governs the power density of the battery.

Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials., 30 pages of main text, 14 pages of supplementary materials, 9 figures in total

Currently available solid polymer electrolytes for Li-ion cells require deeper understanding and significant improvement in ionic transport properties to enable their use in high-power batteries. We use molecular dynamics simulations to model the solid amorphous polymer electrolyte system comprising poly(ethylene) oxide (PEO), lithium, and bis(trifluoromethane)sulfonimide anion (TFSI), exploring effects of high salt concentrations relevant for battery applications. Using statistical analysis of ion distribution and transport, we investigate the significant effect that salt concentration has on ion mobility. At practical salt concentrations, a previously undetected ensemble of Li–TFSI clusters emerges where Li ions have significantly lower coordination by the polymer, and this results in their significantly lower mobility as compared to Li ions coordinated by the polymer. We also find the tendency for cation–anion clusters to be asymmetrical, with the anions in greater number than Li cations, which may fur...

In this communication, we present a method of predicting the Seebeck coefficient and electrical conductivity of inorganic semiconductors by estimating the electron-phonon (el-ph) coupling from the first principles for fast-screening potential thermoelectric (TE) materials. The method we propose, i.e. the electron-phonon averaged via moving least squares (EPA-MLS) method, combines the EPA method and the MLS averaging strategy. To demonstrate the performance of the EPA-MLS method, the Seebeck coefficient and electrical conductivity of a half-Heusler compound, i.e. HfCoSb, were computed with the EPA-MLS method and compared with the results from the EPA method and comparable experimental data sets. The results show that the EPA-MLS method exhibits several advantages over the original EPA method. The smoother interpolation reduces the risk of spurious numerical behaviors. The EPA-MLS method also requires less human intervention for tuning numerical parameters, since the calculation result of the EPA-MLS method exhibits robustness against the change of associated numerical parameters. The method may even reduce the overall computational cost by allowing the employment of a coarser resolution. All these advantages make the EPA-MLS method a suitable tool for fast-screening potential TE materials. One more example of an archetypical Skutterudite, i.e. CoSb3, is also provided for showing that the method can be used for TE materials with more complex structures.

If theory is to be able to predict the rates of catalytic reactions over extended ranges of temperature and pressure, it must provide accurate rate constants for elementary reaction steps, includin...

Electronic transport in narrow gap semiconductors is characterized by spontaneous vertical transitions between carriers in the valence and conduction bands, a phenomenon also known as Zener tunneling. However, this effect is not captured by existing models based on the Boltzmann transport equation. In this work, we propose a new fully first principles model for electronic transport using the Wigner distribution function and implement it to solve the equations of motion for electrons. The formalism generalizes the Boltzmann equation to materials with strong interband coupling and include transport contributions from off-diagonal components of the charge current operator. We illustrate the method with a study of Bi2Se3, showing that interband tunneling dominates the electron transport dynamics at experimentally relevant small doping concentrations, a behavior that is likely shared with other semiconductors, including topological insulators. Surprisingly, Zener tunneling occurs also between band subvalleys separated by energy much larger than the band gap.

We present a combined treatment of the non-equilibrium dynamics and transport of electrons and phonons by carrying out \textit{ab initio} calculations of the fully coupled electron and phonon Boltzmann transport equations. We find that the presence of mutual drag between the two carriers causes the thermopower to be enhanced and dominated by the transport of phonons, rather than electrons as in the traditional semiconductor picture. Drag also strongly boosts the intrinsic electron mobility, thermal conductivity and the Lorenz number. Impurity scattering is seen to suppress the drag-enhancement of the thermal and electrical conductivities, while having weak effects on the enhancement of the Lorenz number and thermopower. We demonstrate these effects in \textit{n}-doped 3C-SiC at room temperature, and explain their origins. This work establishes the roles of microscopic scattering mechanisms in the emergence of strong drag effects in the transport of the interacting electron-phonon gas.

Copyright © 2020 American Chemical Society. We investigate the charge densities, lithium intercalation potentials, and Li-diffusion barrier energies of LixNiO2 (0.0 < x < 1.0) system using the diffusion quantum Monte Carlo (DMC) method. We find an average redox potential of 4.1(2) eV and a Li-diffusion barrier energy of 0.39(3) eV with DMC. Comparisoin of the charge densities from DMC and density functional theory (DFT) and show that local and semilocal DFT functionals yield spin polarization densities with an incorrect sign on the oxygen atoms. The SCAN functional and Hubbard-U correction improves the polarization density around Ni and O atoms, resulting in smaller deviations from the DMC densities. DMC accurately captures the many-body nature of Ni-O bonding, hence yielding accurate lithium intercalation voltages, polarization densities, and reaction barriers. ©

We show that strong cation-anion interactions in a wide range of lithium-salt/ionic liquid mixtures result in a negative lithium transference number, using molecular dynamics simulations and rigorous concentrated solution theory. This behavior fundamentally deviates from that obtained using self-diffusion coefficient analysis and explains well recent experimental electrophoretic nuclear magnetic resonance measurements, which account for ion correlations. We extend these findings to several ionic liquid compositions. We investigate the degree of spatial ionic coordination employing single-linkage cluster analysis, unveiling asymmetrical anion-cation clusters. We formulate a way to compute the effective lithium charge and show that lithium-containing clusters carry a negative charge over a remarkably wide range of compositions and concentrations. This finding has significant implications for the overall performance of battery cells based on ionic liquid electrolytes. It also provides a rigorous prediction recipe and design protocol for optimizing transport properties in next-generation highly correlated electrolytes.

Surface restructuring in bimetallic systems has recently been shown to play a crucial role in heterogeneous catalysis. In particular, the segregation in binary alloys can be reversed in the presence of strongly bound adsorbates. Mechanistic characterization of such restructuring phenomena at the atomic level remains scarce and challenging due to the large configurational space that must be explored. To this end, we propose an automated method to discover elementary surface restructuring processes in an unbiased fashion, using Pd/Ag as an example. We employ high-temperature classical molecular dynamics (MD) to rapidly detect restructuring events, isolate them, and optimize using density functional theory (DFT). In addition to confirming the known exchange descent mechanism, our systematic approach has revealed three new predominant classes of events at step edges of close-packed surfaces that have not been considered before: (1) vacancy insertion; (2) direct exchange; (3) interlayer exchange. The discovered events enable us to construct the complete set of mechanistic pathways by which Pd is incorporated into the Ag host in vacuum. These atomistic insights provide a step toward systematic understanding and engineering of surface segregation dynamics in bimetallic catalysts.

We present the electron-phonon averaged via Gaussian process regression (EPA-GPR) method, in which the electron-phonon coupling matrix is represented as a function of two energies and is in turn modeled as a Gaussian process. The EPA-GPR method can be used as an efficient method to estimate thermoelectric properties of materials for fast-screening applications, comparable to the original electron-phonon averaged (EPA) method and the electron-phonon averaged via moving-least-squares (EPA-MLS) method. The EPA-GPR method does not require specification of any open parameter, unlike the other EPA-related methods, since all the hyperparameters in the model can be unambiguously estimated within the type II maximum likelihood (ML-II) approximation. Thus, the EPA-GPR method is a parameter-free estimation method. Additionally, the concept of Gaussian processes in the EPA-GPR method allows us to quantify the uncertainty in estimated properties of thermoelectric materials. One can randomly realize the electron-phonon coupling coefficients from the identified Gaussian process, and those realized samples can be further analyzed in the solution process of the semiclassical Boltzmann transport equation for charge carriers. The results of the semiclassical Boltzmann transport equation provide the statistical properties of the thermoelectric properties of interest. The means, standard deviations, histograms, and confidence intervals of the Seebeck coefficient, the electrical conductivity, and the power factor can be constructed and analyzed. The proposed EPA-GPR method is applied to a $p$-type half-Heusler compound, i.e., HfCoSb, as a case example, the results of which clearly present the advantages of the method.

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work, we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the smooth overlap of atomic positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li$_{7}$La$_{3}$Zr$_{2}$O$_{12}$, tetragonal Li$_{10}$GeP$_{2}$S$_{12}$, and the $\beta$-eucryptite LiAlSiO$_{4}$. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated.

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

Understanding the ionic diffusion mechanism in polymer electrolytes is critical to the development of advanced lithium-ion batteries. We report here molecular dynamics-based characterization of structures and diffusion in poly(ethylene oxide) (PEO) with lithium and bis(trifluoromethysulfonyl)imide (TFSI) ions imbedded into the PEO structure. We consider a range of temperatures (360–480 K), molecular weights (43, 22, 10, and 2 chains with 23, 45, 100, and 450 EO monomers, respectively), and ion concentrations (r = 0.02, 0.04, 0.06, and 0.08 Li:EO) for which there is experimental data. The found dependence of the diffusion coefficients on these variables is in good agreement with experimental measurements. We then analyze how the diffusion performance depends on details of the atomistic diffusion mechanism, the motion of the Li and TFSI along the polymer chains and hopping between them, the role of polymer motion, the temperature dependence of the intrachain and interchain diffusion contributions to the total ionic diffusion coefficients, and how these depend on ionic concentration and molecular weight. The most diffusive Li atoms exhibit frequent interchain hopping, whereas the least diffusive Li atoms oscillate or “shift” between two or more polymer chains. These shifts may affect the segmental motion of the PEO–LiTFSI polymer that is expected to be important for fast lithium-ion diffusion. The excellent agreement between experiment and theory validates the approach and methodology used in this study, setting the stage for applying this methodology to predicting how to modify the polymer structure to increase ionic conductivity for a new generation of electrochemical materials.

A detailed experimental analysis of the factors affecting cyclic durability of all-solid-state lithium batteries using poly(ethylene oxide)-based polymer electrolytes was published in EES by Nakayama et al. We use quantum mechanics to interpret these results, identifying processes involved in the degradation of rechargeable lithium batteries based on polyethylene oxide (PEO) polymer electrolyte with LiTFSI. We consider that ionization of the electrolyte near the cathode at the end of the recharge step is probably responsible for this degradation. We find that an electron is likely removed from PEO next to a TFSI anion, triggering a sequence of steps leading to neutralization of a TFSI anion and anchoring of another TFSI to the PEO. This decreases the polymer conductivity near the cathode, making it easier to ionize additional PEO and leading to complete degradation of the battery. We refer to this as the Cathode Overpotential Driven Ionization of the Solvent (CODIS) model. We suggest possible ways to confirm experimentally our interpretation and propose modifications to suppress or reduce electrolyte degradation.

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows., 30 pages, 7 figures

Recent discovery of new materials for thermoelectric energy conversion is enabled by efficient prediction of materials' performance from first-principles, without empirically fitted parameters. The novel simplified approach for computing electronic transport properties is described, which achieves good accuracy and transferability while greatly reducing complexity and computation cost compared to the existing methods. The first-principles calculations of the electron-phonon coupling demonstrate that the energy dependence of the electron relaxation time varies significantly with chemical composition and carrier concentration, suggesting that it is necessary to go beyond the commonly used approximations to screen and optimize materials' composition, carrier concentration, and microstructure. The new method is verified using high accuracy computations and validated with experimental data before applying it to screen and discover promising compositions in the space of half-Heusler alloys. By analyzing data trends the effective electron mass is identified as the single best general descriptor determining material's performance. The Lorenz number is computed from first principles and the universality of the Wiedemann-Franz law in thermoelectrics is discussed., This is the pre-peer reviewed version of the following article: Adv. Energy Mater. 2018, 1800246, which has been published in final form at doi:10.1002/aenm.201800246. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving

We use rigorous group-theoretic techniques and molecular dynamics to investigate the connection between structural symmetry and ionic conductivity in the garnet family of solid Li-ion electrolytes. We identify new ordered phases and order-disorder phase transitions that are relevant for conductivity optimization. Ionic transport in this materials family is controlled by the frustration of the Li sublattice caused by incommensurability with the host structure at non-integer Li concentrations, while ordered phases explain regions of sharply lower conductivity. Disorder is therefore predicted to be optimal for ionic transport in this and other conductor families with strong Li interaction., Comment: 6 pages, 4 figures, and supplementary information

The ab initio $GW$ method is considered as the most accurate approach for calculating the band gaps of semiconductors and insulators. Yet its application to transition metal oxides (TMOs) has been hindered by the failure of traditional approximations developed for conventional semiconductors. In this work, we examine the effects of these approximations on the values of band gaps for ZnO, Cu$_2$O, and TiO$_2$. In particular, we explore the origin of the differences between the two widely used plasmon-pole models. Based on the comparison of our results with the experimental data and previously published calculations, we discuss which approximations are suitable for TMOs and why., Comment: This is an author-created, un-copyedited version of an article published in Journal of Physics: Condensed Matter. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/0953-8984/26/47/475501

Here we report the thermoelectric properties of NbCoSn-based n-type half-Heuslers (HHs) that were obtained through arc melting, ball milling, and hot pressing process. With 10% Sb substitution at the Sn site, we obtained enhanced n-type properties with a maximum power factor reaching ∼35 μW cm−1 K−2 and figure of merit (ZT) value ∼0.6 in NbCoSn0.9Sb0.1. The ZT is doubled compared to the previous report. In addition, the specific power cost ($ W−1) is decreased by ∼68% comparing to HfNiSn-based n-type HH because of the elimination of Hf.

First-principles calculations have been performed to investigate electronic band structures, vibrational characters, and related transport properties of pnictogen-substituted skutterudites filled with alkaline-earth elements ( MxCo4A6B6, where M = Ca, Sr, or Ba, A = Ge or Sn, B = Se or Te, and x = 0.5 or 1). Electronic transport properties related to thermoelectricity, including the Seebeck coefficient and the electrical conductivity, are computed by using the Boltzmann transport formalism within the constant-relaxation-time approximation. The results are compared against the corresponding properties of the unfilled pnictogen-substituted ternary skutterudites ( CoA1.5B1.5) to identify the effects of filling to estimate the potential for thermoelectric applications. The changes in the ionic character of the interatomic bonding between the Group 14 (A) and Group 16 (B) elements, which was suspected to be a major scattering source in unfilled pnictogen-substituted ternary skutterudites, are probed by analyzi...

The near-exponential expansion in computing resources over the last few decades has enabled a rapid increase in the capabilities of computational science, including applications to materials research. In order to harness the available resources and accelerate the field of materials design, it is critically important to develop robust and reusable automation software for preparing and performing multistep computational workflows, starting with crystal structures and ending with material properties. In the domain of first-principles calculations of crystalline materials, we highlight emerging tools for automated symmetry analysis of the atomic and electronic structure. With automation capabilities in hand, the ever-increasing amount of data also becomes a serious bottleneck in terms of organization, analysis, and reproducibility. We describe some of the progress and strategic challenges in the development of a general infrastructure for coupling computational automation with data management, emphasizing data reproducibility and provenance capture.