Refractive uses of layered and two-dimensional materials for integrated photonics

The scientific community has witnessed tremendous expansion of research on layered (i.e. two-dimensional, 2D) materials, with increasing recent focus on applications to photonics. Layered materials are particularly exciting for manipulating light in the confined geometry of photonic integrated circuits, where key material properties include strong and controllable light-matter interaction, and limited optical loss. Layered materials feature tunable optical properties, phases that are promising for electro-optics, and a panoply of polymorphs that suggest a rich design space for highly-nonperturbative photonic integrated devices based on phase-change functionality. All of these features are manifest in materials with band gap above the photonics-relevant near-infrared (NIR) spectral band ($\sim$ 0.5 - 1 eV), meaning that they can be harnessed in refractive (i.e. non-absorptive) applications.Comment: review paper. ACS Photonics (2020

Single-atom-layer traps in a solid electrolyte for lithium batteries

In order to fully understand the lithium-ion transport mechanism in solid electrolytes for batteries, not only the periodic lattice but also the non-periodic features that disrupt the ideal periodicity must be comprehensively studied. At present only a limited number of non-periodic features such as point defects and grain boundaries are considered in mechanistic studies. Here, we discover an additional type of non-periodic feature that significantly influences ionic transport; this feature is termed a “single-atom-layer trap” (SALT). In a prototype solid electrolyte Li0.33La0.56TiO3, the single-atom-layer defects that form closed loops, i.e., SALTs, are found ubiquitous by atomic-resolution electron microscopy. According to ab initio calculations, these defect loops prevent large volumes of materials from participating in ionic transport, and thus severely degrade the total conductivity. This discovery points out the urgency of thoroughly investigating different types of non-periodic features, and motivates similar studies for other solid electrolytes

Unsupervised discovery of solid-state lithium ion conductors

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.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

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Abstract Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties

Frustration in Super-Ionic Conductors Unraveled by the Density of Atomistic States

The frustration in super-ionic conductors enables their exceptionally high ionic conductivities, which are desired for many technological applications including batteries and fuel cells. A key challenge in the study of frustration is the difficulties in analyzing a large number of disordered atomistic configurations. Using lithium super-ionic conductors as model systems, we propose and demonstrate the density of atomistic states (DOAS) analytics to quantitatively characterize the onset and degree of disordering, reveal the energetics of local disorder, and elucidate how the frustration enhances diffusion through the broadening and overlapping of the energy levels of atomistic states. Furthermore, material design strategies aided by the DOAS are devised and demonstrated for new super-ionic conductors. The DOAS is generally applicable analytics for unraveling fundamental mechanisms in complex atomistic systems and guiding material design.https://doi.org/10.1002/anie.20221554

Discrepancies and error evaluation metrics for machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.https://doi.org/10.1038/s41524-023-01123-