Search for: All records

Abstract In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion. Graphical abstract 

Abstract Cesium‐based quasi‐2D halide perovskites (HPs) offer promising functionalities and low‐temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi‐2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high‐throughput automated experimentation, the phase complexities in the molecular space of quasi‐2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two‐stage decision‐making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary‐compositional quasi‐2D HP film realizing excellent optoelectronic functionalities is demonstrated. This work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems. 

Abstract Quasi‐2D metal halide perovskites (MHPs) are an emerging material platform for sustainable functional optoelectronics, but the uncontrollable, broad phase distribution remains a critical challenge for applications. Nevertheless, the basic principles for controlling phases in quasi‐2D MHPs remain poorly understood, due to the rapid crystallization kinetics during the conventional thin‐film fabrication process. Herein, a high‐throughput automated synthesis‐characterization‐analysis workflow is implemented to accelerate material exploration in formamidinium (FA)‐based quasi‐2D MHP compositional space, revealing the early‐stage phase growth behaviors fundamentally determining the phase distributions. Upon comprehensive exploration with varying synthesis conditions including 2D:3D composition ratios, antisolvent injection rates, and temperatures in an automated synthesis‐characterization platform, it is observed that the prominentn= 2 2D phase restricts the growth kinetics of 3D‐like phases—α‐FAPbI₃MHPs with spacer‐coordinated surface—across the MHP compositions. Thermal annealing is a critical step for proper phase growth, although it can lead to the emergence of unwanted local PbI₂crystallites. Additionally, fundamental insights into the precursor chemistry associated with spacer‐solvent interaction determining the quasi‐2D MHP morphologies and microstructures are demonstrated. The high‐throughput study provides comprehensive insights into the fundamental principles in quasi‐2D MHP phase control, enabling new control of the functionalities in complex materials systems for sustainable device applications. 

Abstract Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration‐corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub‐Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, a method for automated nanopore fabrication is utilized with real‐time atomic visualization to enhance the mechanistic understanding of beam‐induced transformations. Additionally, an electron beam simulation technique, Electron‐Beam Simulator (E‐BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti₃C₂T_x, the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E‐BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials. 

In the last several years, laboratory automation and high‐throughput synthesis and characterization have come to the forefront of the research community. The large datasets require suitable machine learning techniques to analyze the data effectively and extract the properties of the system. Herein, the binary library of metal halide perovskite (MHP) microcrystals, MA_xFA_1−xPbI_3−xBr_x, is explored via low‐dimensional latent representations of composition‐ and time‐dependent photoluminescence (PL) spectra. The variational autoencoder (VAE) approach is used to discover the latent factors of variability in the system. The variability of the PL is predominantly controlled by compositional dependence of the bandgap. At the same time, secondary factor of variability includes the phase separation associated with the formation of the double peaks. To overcome the interpretability limitations of standard VAEs, the workflow based on the translationally invariant variational (tVAEs) and conditional autoencoders (cVAEs) is introduced. tVAE discovers known factors of variation within the data, for example, the (unknown) shift of the peak due to the bandgap variation. Conversely, cVAEs impose known factor of variation, in this case anticipated bandgap. Jointly, the tVAE and cVAE allow to disentangle the underlying mechanisms present within the data that bring a deeper meaning and understanding within MHP systems. 

Abstract Mixed cesium‐ and formamidinium‐based metal halide perovskites (MHPs) are emerging as ideal photovoltaic materials due to their promising performance and improved stability. While theoretical predictions suggest that a larger composition ratio of Cs (≈30%) aids the formation of a pure photoactive α‐phase, high photovoltaic performances can only be realized in MHPs with moderate Cs ratios. In fact, elemental mixing in a solution can result in chemical complexities with non‐equilibrium phases, causing chemical inhomogeneities localized in the MHPs that are not traceable with global device‐level measurements. Thus, the chemical origin of the complexities and understanding of their effect on stability and functionality remain elusive. Herein, through spatially resolved analyses, the fate of local chemical structures, particularly the evolution pathway of non‐equilibrium phases and the resulting local inhomogeneities in MHPs is comprehensively explored. It is shown that Cs‐rich MHPs have substantial local inhomogeneities at the initial crystallization step, which do not fully convert to the α‐phase and thereby compromise the optoelectronic performance of the materials. These fundamental observations allow the authors to draw a complete chemical landscape of MHPs including nanoscale chemical mechanisms, providing indispensable insights into the realization of a functional materials platform. 

Abstract Ferroelectric materials exhibit spontaneous polarization that can be switched by electric field. Beyond traditional applications as nonvolatile capacitive elements, the interplay between polarization and electronic transport in ferroelectric thin films has enabled a path to neuromorphic device applications involving resistive switching. A fundamental challenge, however, is that finite electronic conductivity may introduce considerable power dissipation and perhaps destabilize ferroelectricity itself. Here, tunable microwave frequency electronic response of domain walls injected into ferroelectric lead zirconate titanate (PbZr_0.2Ti_0.8O₃) on the level of a single nanodomain is revealed. Tunable microwave response is detected through first‐order reversal curve spectroscopy combined with scanning microwave impedance microscopy measurements taken near 3 GHz. Contributions of film interfaces to the measured AC conduction through subtractive milling, where the film exhibited improved conduction properties after removal of surface layers, are investigated. Using statistical analysis and finite element modeling, we inferred that the mechanism of tunable microwave conductance is the variable area of the domain wall in the switching volume. These observations open the possibilities for ferroelectric memristors or volatile resistive switches, localized to several tens of nanometers and operating according to well‐defined dynamics under an applied field. 

Abstract Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band‐excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.