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Dynamic restructuring of bimetallic catalysts plays a crucial role in their catalytic activity and selectivity. In particular, catalyst pretreatment with species such as carbon monoxide and oxygen has been shown to be an effective strategy for tuning the surface composition and morphology. Mechanistic and kinetic understanding of such restructuring is fundamental to the chemistry and engineering of surface active sites but has remained challenging due to the large structural, chemical, and temporal degrees of freedom. Here, we combine time-resolved temperature-programmed infrared reflection absorption spectroscopy, ab initio thermodynamics, and machine-learning molecular dynamics to uncover previously unidentified timescale and kinetic parameters of in situ restructuring in Pd/Au(111), a highly relevant model system for dilute Pd-in-Au nanoparticle catalysts. The key innovation lies in utilizing CO not only as a chemically sensitive probe of surface Pd but also as an agent that induces restructuring of the surface. Upon annealing in vacuum, as-deposited Pd islands became encapsulated by Au and partially dissolved into the subsurface, leaving behind isolated Pd monomers on the surface. Subsequent exposure to 0.1 mbar CO enabled Pd monomers to repopulate the surface up to 373 K, above which complete Pd dissolution occurred by 473 K, with apparent activation energies of 0.14 and 0.48 eV, respectively. These restructuring processes occurred over the span of ∼1000 s at a given temperature. Such a minute-timescale dynamics not only elucidates the fluxional nature of alloy catalysts but also presents an opportunity to fine-tune the surface under moderate temperature and pressure conditions.

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

We report a synthesis method for highly monodisperse Cu-Pt alloy nanospheres. Small and large Cu-Pt particles with a Cu:Pt ratio of 1:1 can be obtained through colloidal synthesis at 300 °C. The fresh particles have a Pt-rich surface and a Cu-rich core and can be converted into an intermetallic phase after annealing at 800 °C under H2. First, we demonstrated the stability of fresh particles under redox conditions at 400 °C, as the Pt-rich surface prevents substantial oxidation of Cu. Then, a combination of in situ scanning transmission electron microscopy, in situ X-ray absorption spectroscopy, and CO oxidation measurements of the intermetallic CuPt phase before and after redox treatments at 800 °C showed promising activity and stability for CO oxidation. Full oxidation of Cu was prevented after exposure to O2 at 800 °C. The activity and structure of the particles was only slightly changed after exposure to O2 at 800 °C and were recovered after re-reduction at 800 °C. Additionally, the intermetallic CuPt phase showed enhanced catalytic properties compared to the fresh particles with a Pt-rich surface or pure Pt particles of the same size. Thus, the incorporation of Pt with Cu does not lead to a rapid deactivation and degradation of the material, as seen with other bimetallic systems. This work provides a synthesis route to control the design of Cu-Pt nanostructures and underlines the promising properties of these alloys (intermetallic and non-intermetallic) for heterogeneous catalysis.

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and open the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models' accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models., 32 pages, 4 figures

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.

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of systems and accelerates computational understanding and design.

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.

An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency., This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space, and separate downstream parts map the latent space to predictions of basin class labels and potential energies. The resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D M\"uller Brown model, a 5D three-well model, and alanine dipeptide in vacuum. This approach enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders., Comment: 10 pages, 8 figures, presented in MRS 2020 Fall

Understanding the factors that govern gas absorption in ionic liquids is critical to the development of high-capacity solvents for catalysis, electrochemistry, and gas separations. Here, we report experimental probes of liquid structure that provide insights into how free volume impacts the O

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface, at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

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.

Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts.

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

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.

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive, as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows systematic reduction of the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness and demonstrate it on realistic electrolyte materials.

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.

This work presents Neural Equivariant Interatomic Potentials (NequIP), a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs SE(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors, and energy conversion devices. Computational modeling can accurately predict material properties but, in order to be reliable, requires accurate descriptions of electron and phonon states and their interactions. While first-principles methods are capable of describing the energy spectrum of each carrier, using them to compute transport properties is still a formidable task, both computationally demanding and memory intensive, requiring integration of fine microscopic scattering details for estimation of macroscopic transport properties. To address this challenge, we present Phoebe—a newly developed software package that includes the effects of electron–phonon, phonon–phonon, boundary, and isotope scattering in computations of electrical and thermal transport properties of materials with a variety of available methods and approximations. This open source C++ code combines MPI-OpenMP hybrid parallelization with GPU acceleration and distributed memory structures to manage computational cost, allowing Phoebe to effectively take advantage of contemporary computing infrastructures. We demonstrate that Phoebe accurately and efficiently predicts a wide range of transport properties, opening avenues for accelerated computational analysis of complex crystals.

Restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out, as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution.

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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.

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.

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 the one obtained using self-diffusion coefficient analysis and agrees well with experimental electrophoretic NMR measurements, which accounts 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. Additionally, we formulate a way to compute the effective lithium charge that corresponds to and agrees well with electrophoretic measurements and show that lithium effectively carries a negative charge in a remarkably wide range of chemistries and concentrations. The generality of our observation has significant implications for the energy storage community, emphasizing the need to reconsider the potential of these systems as next generation battery electrolytes.

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 understanding of the oxidation mechanism allows to formulate 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. Design of materials based on dynamical and transport properties, important for batteries and catalysts, involves systematic exploration of many structural and compositional variations and hence requires very fast and accurate computations of dynamical phenomena. I will present recent progress and challenges in “ex-machina” materials design, a new paradigm in which an automated closed-loop machine learning algorithm constructs non-parametric Bayesian force fields that combine first-principles accuracy with internal quantitative uncertainty and prior information of physical symmetries. We apply this method, implemented in the FLARE framework [1], to large-scale dynamics simulations of alloys, ion conductors, catalysts and 2D materials. [1] J. Vandermause, S. B. Torrisi, S. Batzner, A. M. Kolpak, B. Kozinsky, “On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events”, (2019), arXiv:1904.02042

Recent works on ionic liquid electrolyte systems motivate the present study of transport regimes where strong species interactions result in significant correlations and deviations from ideal solution behaviour. In order to obtain a complete description of transport in these systems we use rigorous concentrated solution theory coupled with molecular dynamics simulations, beyond the commonly used uncorrelated Nernst-Einstein equation. As a case study, we investigate the NaFSI - Pyr13\FSI room temperature ionic liquid electrolyte. When fully accounting for intra- and inter-species correlation, an anomalously low and even negative transference number emerges for NaFSI molar fractions lower than 0.2, emphasising that strong ion-ion coupling in the electrolyte can significantly impact the rate performance of the cell. 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 a variant of unsupervised single-linkage clustering algorithm. Using this combination of numerical techniques we examine the microscopic mechanisms responsible for the trade-off between key electrolyte transport properties, previously overlooked in both computational and experimental studies.

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.

Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes and energy density of Li-ion batteries but the controlling degradation mechanisms are difficult to characterize and remain poorly understood. We investigate the oxidative decomposition mechanisms governing high voltage stability of multi-component organic electrolytes using computational techniques of quantum chemistry. The intrinsic oxidation potential is modeled using vertical ionization potentials (IP) of ensembles of anion-solvent clusters generated using molecular dynamics. In some cases, the IP of the solvent-anion complex is significantly lower than that of each individual component. This effect is found to originate from the oxidation-driven charge transfer complex formation between the anion and the solvent. We propose a simple model to quantitatively understand this phenomenon and validate it for 16 combinations of common anions (4,5-dicyano-2-(trifluoromethyl)imidazolium, bis-(trifluoromethane solfonimmide), tetrafluroborate, hexafluorophosphate) and solvents (dimethyl sulfoxide, dimethoxyethane, propylene carbonate, acetonitrile). This new understanding of the microscopic details of oxidation allows us to interpret trends in published experimental and computational results and to formulate design rules for rapidly assessing stability of electrolyte compositions.

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

The model and analysis methods developed in this work are generally applicable to any polymer electrolyte/cation-anion combination, but we focus on the currently most prominent polymer electrolyte material system: poly(ethylene) oxide/Li- bis(trifluoromethane) sulfonamide (PEO + LiTFSI). The obtained results are surprising and challenge the conventional understanding of ionic transport in polymer electrolytes: the investigation of a technologically relevant salt concentration range (1 - 4 M) revealed the central role of the anion in coordinating and hindering Li ion movement. Our results provide insights into correlated ion dynamics, at the same time enabling rational design of better PEO-based electrolytes. In particular, we report the following novel observations. 1. Strong binding of the Li cation with the polymer competes with significant correlation of the cation with the salt anion. 2. The appearance of cation-anion clusters, especially at high concentration. 3. The asymmetry in the composition (and therefore charge) of such clusters; specifically, we find the tendency for clusters to have a higher number of anions than cations.

We report a peak dimensionless figure-of-merit (ZT) of ∼1 at 700 °C in a nanostructured p-type Nb0.6Ti0.4FeSb0.95Sn0.05 composition. Even though the power factor of the Nb0.6Ti0.4FeSb0.95Sn0.05 composition is improved by 25%, in comparison to the previously reported p-type Hf0.44Zr0.44Ti0.12CoSb0.8Sn0.2, the ZT value is not increased due to a higher thermal conductivity. However, the higher power factor of the Nb0.6Ti0.4FeSb0.95Sn0.05 composition led to a 15% increase in the power output of a thermoelectric device in comparison to a device made from the previous best material Hf0.44Zr0.44Ti0.12CoSb0.8Sn0.2. The n-type material used to make the unicouple device is the best reported nanostructured Hf0.25Zr0.75NiSn0.99Sb0.01 composition with the lowest hafnium (Hf) content. Both the p- and n-type nanostructured samples are prepared by ball milling the arc melted ingot and hot pressing the finely ground powders. Moreover, the raw material cost of the Nb0.6Ti0.4FeSb0.95Sn0.05 composition is more than six times lower compared to the cost of the previous best p-type Hf0.44Zr0.44Ti0.12CoSb0.8Sn0.2. This cost reduction is crucial for these materials to be used in large-scale quantities for vehicle and industrial waste heat recovery applications.

We present a first-principles study of the temperature- and density-dependent intrinsic electrical resistivity of graphene. We use density-functional theory and density-functional perturbation theory together with very accurate Wannier interpolations to compute all electronic and vibrational properties and electron-phonon coupling matrix elements; the phonon-limited resistivity is then calculated within a Boltzmann-transport approach. An effective tight-binding model, validated against first-principles results, is also used to study the role of electron-electron interactions at the level of many-body perturbation theory. The results found are in excellent agreement with recent experimental data on graphene samples at high carrier densities and elucidate the role of the different phonon modes in limiting electron mobility. Moreover, we find that the resistivity arising from scattering with transverse acoustic phonons is 2.5 times higher than that from longitudinal acoustic phonons. Last, high-energy, optical, and zone-boundary phonons contribute as much as acoustic phonons to the intrinsic electrical resistivity even at room temperature and become dominant at higher temperatures., 7 pages 5 figures

A common approach to understanding surface reaction mechanisms in rechargeable lithium-based battery systems involves spectroscopic characterization of the product mixtures and matching of spectroscopic features to spectra of pure candidate reference compounds. This strategy, however, requires separate chemical synthesis and accurate characterization of potential reference compounds. It also assumes that atomic structures are the same in the actual product mixture as in the reference samples. We propose an alternative approach that uses first-principles computations of spectra of the possible reaction products and by-products present in advanced battery systems. We construct a library of computed Raman spectra for possible products, achieving excellent agreement with reference experimental data, targeting solid-electrolyte interphase in Li-ion cells and discharge products of Li-air cells. However, the solid-state crystalline structure of Li(Na) metal-organic compounds is often not known, making the spectra computations difficult. We develop and apply a novel technique of simplifying spectra calculations by using dimer-like representations of the solid state structures. On the basis of a systematic investigation, we demonstrate that molecular dimers of Li(Na)-based organometallic material provide relevant information about the vibrational properties of many possible solid reaction products. Such an approach should serve as a basis to extend existing spectral libraries of molecular structures relevant for understanding the link between atomic structures and measured spectroscopic data of materials in novel battery systems.

Structural properties of Li 7 − x La 3 Ta x Zr 2 − x O 12 garnets with x = 0–2 were clarified by means of Rietveld analysis using results of X-ray diffraction and neutron diffraction at room temperature and at low temperature. In this work the controversy between Awaka [1] and Murugan [2] concerning the crystal structure of Li 7 La 3 Zr 2 O 12 was solved. It was shown that the tetragonally derived garnet structure of space group I 4 1 / acd described by Awaka [1] is the thermodynamically stable structure for Li 7 La 3 Zr 2 O 12 . In the three-dimensional sub-network of this structure, lithium is ordered and occupies all octahedral sites as well as one third of the tetrahedral sites. Li 7 − x La 3 Ta x Zr 2 − x O 12 garnets with x = 0.125–2 crystallize in the garnet structure, space group Ia 3 ¯ d . As the tantalum content increases, the lattice parameter at room temperature decreases from a = 12.9833(1) A for Li 6.875 La 3 Ta 0.125 Zr 1.875 O 12 down to a = 12.81224(7) A for Li 5 La 3 Ta 2 O 12 . In Li 6.5 La 3 Ta 0.5 Zr 1.5 O 12 garnet, lithium atoms are statistically partitioned among octahedral sites (occ.: 0.80(2)) and tetrahedral sites (occ.: 0.56(4)). In the cases of ordered Li 7 La 3 Zr 2 O 12 tetragonally derived garnet and statistically disordered Li 6.5 La 3 Ta 0.5 Zr 1.5 O 12 garnet, lithium partitioning remains unchanged as temperature decreases.