Flexible fluid-based encapsulation platform for water-sensitive materials.

The next-generation semiconductors and devices, such as halide perovskites and flexible electronics, are extremely sensitive to water, thus demanding highly effective protection that not only seals out water in all forms (vapor, droplet, and ice), but simultaneously provides mechanical flexibility, durability, transparency, and self-cleaning. Although various solid-state encapsulation methods have been developed, no strategy is available that can fully meet all the above requirements. Here, we report a bioinspired liquid-based encapsulation strategy that offers protection from water without sacrificing the operational properties of the encapsulated materials. Using halide perovskite as a model system, we show that damage to the perovskite from exposure to water is drastically reduced when it is coated by a polymer matrix with infused hydrophobic oil. With a combination of experimental and simulation studies, we elucidated the fundamental transport mechanisms of ultralow water transmission rate that stem from the ability of the infused liquid to fill-in and reduce defects in the coating layer, thus eliminating the low-energy diffusion pathways, and to cause water molecules to diffuse as clusters, which act together as an excellent water permeation barrier. Importantly, the presence of the liquid, as the central component in this encapsulation method provides a unique possibility of reversing the water transport direction; therefore, the lifetime of enclosed water-sensitive materials could be significantly extended via replenishing the hydrophobic oils regularly. We show that the liquid encapsulation platform presented here has high potential in providing not only water protection of the functional device but also flexibility, optical transparency, and self-healing of the coating layer, which are critical for a variety of applications, such as in perovskite solar cells and bioelectronics.

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites.

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.

Synthesis and Characterization of Stable Cu-Pt Nanoparticles under Reductive and Oxidative Conditions.

We report a synthesis method for highly monodisperse Cu-Pt alloy nanoparticles. 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 were 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.

Fast uncertainty estimates in deep learning interatomic potentials.

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and material properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction, resulting in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.

Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System.

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.

Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport.

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.

Fundamental Limits to the Electrochemical Impedance Stability of Dielectric Elastomers in Bioelectronics.

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 10-8 S/m. When the passivated conductors are also composed of intrinsically stretchable materials, higher leakage currents can be detected. Scaling analyses suggest fundamental limitations to the electrical performances of interconnects made of stretchable materials.

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics.

The restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different compositions and morphologies at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-time scale 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.

Factors affecting cyclic durability of all-solid-state lithium batteries using poly(ethylene oxide)-based polymer electrolytes and recommendations to achieve improved performance.

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.

Effects of Sublattice Symmetry and Frustration on Ionic Transport in Garnet Solid Electrolytes.

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 noninteger 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.

Electron-phonon interactions and the intrinsic electrical resistivity of graphene.

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.

Computational Raman spectroscopy of organometallic reaction products in lithium and sodium-based battery systems.

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.

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields.

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-'Herringbone,' Au(110)-(1 × 2)-'Missing-Row,' and Au(100)-'Quasi-Hexagonal' reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials.

The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties, and ab initio calculations are too costly. In this work, we train a machine learning interaction potential on density functional theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants, and various surface properties inaccessible using DFT. We establish that there exists a weak Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.

3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers.

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

Learning local equivariant representations for large-scale atomistic dynamics.

A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints.

Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy, numerical stability, and transferability across chemical space. In this work, we introduce a set of nonlocal features of the density called the CIDER formalism, which we use to train a Gaussian process model for the exchange energy that obeys the critical uniform scaling rule for exchange. The resulting CIDER exchange functional is significantly more accurate than any semilocal functional tested here, and it has good transferability across main-group molecules. This work therefore serves as an initial step toward more accurate exchange functionals, and it also introduces useful techniques for developing robust, physics-informed XC models via ML.

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events.

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 Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt.

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.

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

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.