Unsupervised discovery of solid-state lithium ion conductors.
ABSTRACT: Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10-4-10-1?S?cm-1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.
Project description:Low lithium-ion migration barriers have recently been associated with low average vibrational frequencies or phonon band centers, further helping identify descriptors for superionic conduction. To further explore this correlation, here we present the computational screening of ?14,000 Li-containing compounds in the Materials Project database using a descriptor based on lattice dynamics reported recently to identify new promising Li-ion conductors. An efficient computational approach was optimized to compute the average vibrational frequency or phonon band center of ?1,200 compounds obtained after pre-screening based on structural stability, band gap, and their composition. Combining a low computed Li phonon band center with large computed electrochemical stability window and structural stability, 18 compounds were predicted to be promising Li-ion conductors, one of which, Li<sub>3</sub>ErCl<sub>6</sub>, has been synthesized and exhibits a reasonably high room-temperature conductivity of 0.05-0.3 mS/cm, which shows the promise of Li-ion conductor discovery based on lattice dynamics.
Project description:Safe and robust batteries are urgently requested today for power sources of electric vehicles. Thus, a growing interest has been noted for fabricating those with solid electrolytes. Materials search by density functional theory (DFT) methods offers great promise for finding new solid electrolytes but the evaluation is known to be computationally expensive, particularly on ion migration property. In this work, we proposed a Bayesian-optimization-driven DFT-based approach to efficiently screen for compounds with low ion migration energies ([Formula: see text]. We demonstrated this on 318 tavorite-type Li- and Na-containing compounds. We found that the scheme only requires ~30% of the total DFT-[Formula: see text] evaluations on the average to recover the optimal compound ~90% of the time. Its recovery performance for desired compounds in the tavorite search space is ~2× more than random search (i.e., for [Formula: see text]?<?0.3?eV). Our approach offers a promising way for addressing computational bottlenecks in large-scale material screening for fast ionic conductors.
Project description:Transparent conductors based on few-layer graphene (FLG) intercalated with ferric chloride (FeCl(3)) have an outstandingly low sheet resistance and high optical transparency. FeCl(3)-FLGs outperform the current limit of transparent conductors such as indium tin oxide, carbon-nanotube films, and doped graphene materials. This makes FeCl(3)-FLG materials the best transparent conductor for optoelectronic devices.
Project description:Understanding Li diffusion in solid conductors is essential for the next generation Li batteries. Here we show that density-based clustering of the trajectories computed using molecular dynamics simulations helps elucidate the Li diffusion mechanism within the Li7La3Zr2O12 (LLZO) crystal lattice. This unsupervised learning method recognizes lattice sites, is able to give the site type, and can identify Li hopping events. Results show that, while the cubic LLZO has a much higher hopping rate compared to its tetragonal counterpart, most of the Li hops in the cubic LLZO do not contribute to the diffusivity due to the dominance of back-and-forth type jumps. The hopping analysis and local Li configuration statistics give evidence that Li diffusivity in cubic LLZO is limited by the low vacancy concentration. The hopping statistics also shows uncorrelated Poisson-like diffusion for Li in the cubic LLZO, and correlated diffusion for Li in the tetragonal LLZO in the temporal scale. Further analysis of the spatio-temporal correlation using site-to-site mutual information confirms the weak site dependence of Li diffusion in the cubic LLZO as the origin for the uncorrelated diffusion. This work puts forward a perspective on combining machine learning and information theory to interpret results of molecular dynamics simulations.
Project description:Super-ionic conductor materials have great potential to enable novel technologies in energy storage and conversion. However, it is not yet understood why only a few materials can deliver exceptionally higher ionic conductivity than typical solids or how one can design fast ion conductors following simple principles. Using ab initio modelling, here we show that fast diffusion in super-ionic conductors does not occur through isolated ion hopping as is typical in solids, but instead proceeds through concerted migrations of multiple ions with low energy barriers. Furthermore, we elucidate that the low energy barriers of the concerted ionic diffusion are a result of unique mobile ion configurations and strong mobile ion interactions in super-ionic conductors. Our results provide a general framework and universal strategy to design solid materials with fast ionic diffusion.
Project description:Organic mixed conductors have garnered significant attention in applications from bioelectronics to energy storage/generation. Their implementation in organic transistors has led to enhanced biosensing, neuromorphic function, and specialized circuits. While a narrow class of conducting polymers continues to excel in these new applications, materials design efforts have accelerated as researchers target new functionality, processability, and improved performance/stability. Materials for organic electrochemical transistors (OECTs) require both efficient electronic transport and facile ion injection in order to sustain high capacity. In this work, we show that the product of the electronic mobility and volumetric charge storage capacity (µC*) is the materials/system figure of merit; we use this framework to benchmark and compare the steady-state OECT performance of ten previously reported materials. This product can be independently verified and decoupled to guide materials design and processing. OECTs can therefore be used as a tool for understanding and designing new organic mixed conductors.
Project description:The demand for transparent conductors is expected to grow rapidly as electronic devices, such as touch screens, displays, solid state lighting and photovoltaics become ubiquitous in our lives. Doped metal oxides, especially indium tin oxide, are the commonly used materials for transparent conductors. As there are some drawbacks to this class of materials, exploration of alternative materials has been conducted. There is an interest in films of carbon nanomaterials such as, carbon nanotubes and graphene as they exhibit outstanding properties. This article reviews the synthesis and assembly of these films and their post-treatment. These processes determine the film performance and understanding of this platform will be useful for future work to improve the film performance.
Project description:Many superionic mixed ionic-electronic conductors with a liquid-like sublattice have been identified as high efficiency thermoelectric materials, but their applications are limited due to the possibility of decomposition when subjected to high electronic currents and large temperature gradients. Here, through systematically investigating electromigration in copper sulfide/selenide thermoelectric materials, we reveal the mechanism for atom migration and deposition based on a critical chemical potential difference. Then, a strategy for stable use is proposed: constructing a series of electronically conducting, but ion-blocking barriers to reset the chemical potential of such conductors to keep it below the threshold for decomposition, even if it is used with high electric currents and/or large temperature differences. This strategy not only opens the possibility of using such conductors in thermoelectric applications, but may also provide approaches to engineer perovskite photovoltaic materials and the experimental methods may be applicable to understanding dendrite growth in lithium ion batteries.
Project description:Soft stretchable sensors rely on polymers that not only withstand large deformations while retaining functionality but also allow for ease of application to couple with the body to capture subtle physiological signals. They have been applied towards motion detection and healthcare monitoring and can be integrated into multifunctional sensing platforms for enhanced human machine interface. Most advances in sensor development, however, have been aimed towards active materials where nearly all approaches rely on a silicone-based substrate for mechanical stability and stretchability. While silicone use has been advantageous in academic settings, conventional silicones cannot offer self-healing capability and can suffer from manufacturing limitations. This review aims to cover recent advances made in polymer materials for soft stretchable conductors. New developments in substrate materials that are compliant and stretchable but also contain self-healing properties and self-adhesive capabilities are desirable for the mechanical improvement of stretchable electronics. We focus on materials for stretchable conductors and explore how mechanical deformation impacts their performance, summarizing active and substrate materials, sensor performance criteria, and applications.
Project description:Looking for solid state electrolytes with fast lithium ion conduction is an important prerequisite for developing all-solid-state lithium secondary batteries. By combining the simulation techniques in different levels of accuracy, e.g. the bond-valence (BV) method and the density functional theory (DFT), a high-throughput design and optimization scheme is proposed for searching fast lithium ion conductors as candidate solid state electrolytes for lithium rechargeable batteries. The screening from more than 1000 compounds is performed through BV-based method, and the ability to predict reliable tendency of the Li(+) migration energy barriers is confirmed by comparing with the results from DFT calculations. ?-Li3PS4 is taken as a model system to demonstrate the application of this combination method in optimizing properties of solid electrolytes. By employing the high-throughput DFT simulations to more than 200 structures of the doping derivatives of ?-Li3PS4, the effects of doping on the ionic conductivities in this material are predicted by the BV calculations. The O-doping scheme is proposed as a promising way to improve the kinetic properties of this materials, and the validity of the optimization is proved by the first-principles molecular dynamics (FPMD) simulations.