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Abstract This work presents a new method for processing single-crystal semiconductors designed by a computational method to lower the process temperature. This research study is based on a CALPHAD approach (ThermoCalc) to theoretically design processing parameters by utilizing theoretical phase diagrams. The targeted material composition consists of Bi–Se2–Te–Sb (BSTS). The semiconductor alloy contains three phases, hexagonal, rhombohedral-1, and rhombohedral-2 crystal structures, that are presented in the phase field of the theoretical pseudo-binary phase diagram. The semiconductor is also evaluated by applying Hume–Rothery rules along with the CALPHAD approach. Thermodynamic modelling suggests that single-crystals of BSTS can be grown at significantly lower temperatures and this is experimentally validated by low-temperature growth of single crystalline samples followed by exfoliation, compositional analysis, and diffraction.

In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of ``Turing test'' by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This data encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days’ worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The data was logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate data. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world.

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

As the thickness of a three-dimensional (3D) topological insulator (TI) becomes comparable to the penetration depth of surface states, hybridization turns their gapless Dirac electronic structure into a gapped spectrum. Here, authors show that 2D topological states can exist in the 3D TI BiSbTeSe2 when the Fermi level is inside the hybridization gap.

Summary: Learn how to build a Closed-loop Spectroscopy Lab: Light-mixing demo (CLSLab:Light) to perform color matching via RGB LEDs and a light sensor for under 100 USD and less than an hour of setup. Our tutorial covers ordering parts, verifying prerequisites, software setup, sensor mounting, testing, and an optimization algorithm comparison tutorial. We use secure IoT-style communication via MQTT, MicroPython firmware on a pre-soldered Pico W microcontroller, and the self-driving-lab-demo Python package. A video tutorial is available at https://youtu.be/D54yfxRSY6s.For complete details on the use and execution of this protocol, please refer to Baird et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A novel design and fabrication technique for complex microwave waveguide components has been developed. Open cellular structures, characterized by effective medium theory, are used as a replacement for conventional highly-conductive materials. The fabrication is enabled by advancements in additive manufacturing and metal deposition techniques. Components that can be fabricated by vat polymerization followed by electroless deposition of copper, are specifically addressed. The open cellular structure allows fluid penetration to the internal surfaces, allowing for more uniform deposition of metal throughout the part. This makes it feasible to fabricate electrically conductive, complex, multi-function, monolithic parts, something which has proven to be difficult with conventional fabrication techniques, even with recent developments in additive manufacturing. Effective conductivity values are reported for cellular structures that can be practically fabricated. The characterization as a meta surface, not only allows for the easy adoption into existing design work flows, but also enables the exploration of new designs, since the properties of the material can be easily manipulated by changing the fine details of the cellular mesh. Because of the extra degrees of freedom offered in the new process, it is possible to improve performance of secondary properties such as: size, weight, and thermal management characteristics.

Abstract In this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (CrabNet), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that CrabNet’s performance matches or exceeds current best-practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet’s architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by its design. We feel confident that CrabNet and its attention-based framework will be of keen interest to future materials informatics researchers.

Abstract Electrochromic materials and devices are enabling a variety of advanced technologies. Gel-based organic electrochromic molecules such as ethyl viologen diperchlorate are attractive options due to their simple device design and low cost processing options relative to the more expensive and complex transition metal oxide films. However, electrochromic devices are subject to extensive cycling in which failure and fatigue can eventually occur. This work presents the lifetime cycling performance of ethyl viologen diperchlorate-based electrochromic devices using two different anodic compounds, hydroquinone and ferrocene, which are cycled at different voltages, 3 V and 1.2 V, respectively. Multiple devices are cycled until failure with periodic device characterization via UV–Vis spectroscopy, electrical resistance and power measurement, and transition duration measurement. Devices with hydroquinone can transition quickly. Cycle times are $$\sim$$ ∼ 30 s in these samples, however, these samples also typically fail before 3000 cycles. On the other hand, devices using ferrocene transition more slowly (total cycle time $$\sim$$ ∼ 2 min), but have superior cycling performance with all samples surviving beyond 10,000 cycles while complying with ASTM E2141-14 standard.

Summary: Human analysis of research data is slow and inefficient. In recent years, machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification. In this work, we seek to further improve binary classification models for high-throughput identification of different microstructural morphologies. We utilize a dataset with limited observations (133 dendritic structures, 444 non-dendritic) and employ data augmentation via rotation and translation to enhance the dataset six-fold. Then, transfer learning is carried out using pre-trained networks VGG16, InceptionV3, and Xception achieving only moderate F1 scores (0.801–0.822). We hypothesize that feature engineering could yield better results than transfer learning alone. To test this, we employ a new nature-inspired feature optimization algorithm, the Binary Red Deer Algorithm (BRDA), to carry out binary classification and observe F1 scores in the range of 0.96.

Materials discovery via machine learning has become an increasingly popular method due to its ability to rapidly predict materials properties in a time-efficient and low-cost manner. However, one limitation in this field is the lack of benchmark datasets, particularly those that encompass the size, tasks, material systems, and data modalities present in the materials informatics literature. This makes it difficult to identify optimal machine learning model choices including algorithm, model architecture, data splitting, and data featurization for a given task. Here, we attempt to address this lack of benchmark datasets by assembling a unique repository of 50 different datasets for materials properties. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. Data were extracted from 16 publications. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits. For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method. These benchmark data can serve as a basis for a more diverse benchmark dataset in the future to further improve their effectiveness in the comparison of machine learning models.

This study evaluates performance of passive thermal management system of high power Li-ion batteries in a cold environment. Two different phase change materials (PCM) consisting of pure paraffin and paraffin–graphite composites are considered and compared with a battery module in absence of PCM material. Battery modules are partially discharged for a period and then subjected to different cold time periods representing short and long vehicle stops during winter. Battery performance parameters such as capacity, power, and temperature along with thermophysical properties of each module are recorded, calculated, and compared for different scenarios. Thermal management systems moderate temperature rise during discharge and slow the temperature loss during cold stops but also slow the temperature rise after cold stops. Results show that after a short (10 min) cold stop the paraffin wax offers no advantage by keeping the module warm. Both paraffin and no PCM modules had comparable energy values. On the other hand, during long (2 hours) cold stops a significant detriment (15% reduced energy vs 6% reduced energy) was observed in PCM modules by delaying battery warm up when compared to modules with no PCM. Commercial PCM modules with graphite/paraffin composites performed in-between PCM and no PCM modules (9% reduced energy) after long cold soaks but had no advantage for short cold soaks (2.5% energy reduction) due to higher heat dissipation. Keywords: Thermal management system, Phase change material, Lithium-ion battery

Summary: Plastic pollution is entering the world's oceans at alarming rates and is expected to outweigh fish populations by 2050. This plastic waste originates from land-based applications, like consumer product packaging, and is composed of high-durability polyolefins. These conventional plastics possess desirable properties, including high chemical stability, moisture barrier, and thermoplastic characteristics. Unfortunately, if these materials reach marine environments, they fragment into microplastics that cannot be biologically assimilated. The aim of this review is to investigate commercial polymers that are biodegradable in marine environments but have comparable product stability and moisture barrier properties to polyolefins. Among commercially available biopolymers, thermoplastic starches (TPS) and polyhydroxyalkanoates (PHAs) have been shown to biodegrade in marine environments. Moreover, these biopolymers are thermoplastics and possess similar thermoforming properties to polyolefins. At present, TPS and PHAs have limitations, including chemical instability, limited moisture barrier properties, and high production costs. To replace conventional polymers with PHAs and TPS, these properties must be improved.

Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.

Searchable, interactive, databases of material properties, particularly those relating to functional materials (magnetics, thermoelectrics, photovoltaics, etc.) are curiously missing from discussions of machine-learning and other data-driven methods for advancing new materials discovery. Here we discuss the manual aggregation of experimental data from the published literature for the creation of interactive databases that allow the original experimental data as well additional metadata to be visualized in an interactive manner. The databases described involve materials for thermoelectric energy conversion, and for the electrodes of Li-ion batteries. The data can be subject to machine-learning, accelerating the discovery of new materials.

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al., Phys. Rev. X 4, 011019 (2014)], and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning-based recommendation engine (http://thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre-screening about 25 000 known materials and also evaluates the feasibility of user-designed compounds. We show this engine can identify interesting chemistries very different from known thermoelectrics. Specifically, we describe the experimental characterization of one example set of compounds derived from our engine, RE12Co5Bi (RE = Gd, Er), which exhibits surprising thermoelectric performance given its unprecedentedly high loading with metallic d and f block elements and warrants further investigation as a new thermoelectric material platform. We show that our engine predicts this family of materials to have low thermal and high electrical conductivities, but modest Seebeck coefficient, all of which are confirmed experimentally. We note that the engine also predicts materials that may simultaneously optimize all three properties entering into zT; we selected RE12Co5Bi for this study due to its interesting chemical composition and known facile synthesis.

Reduced early transition metal oxides/metal composites have been identified here as interesting thermoelectric materials. Numerous compositions in the Nb-rich portion of the WO3–Nb2O5 system have been studied, in composite formulations with elemental W. Spark plasma sintering (SPS) has been employed to achieve rapid preparation and consolidation of composite materials containing W metal precipitates with characteristic length scales that range from under 20 nm to a few microns, that exhibit thermal conductivities that are constant from 300 K to 1000 K, approximately 2.5 W m−1 K−1. Thermoelectric properties of these n-type materials were measured, and the highest-performing compositions were found to reach figure of merit zT values close to 0.1 at 950 K. The measurements point to higher zT values at yet-higher temperatures.