Observing and modeling the sequential pairwise reactions that drive solid-state ceramic synthesis

Solid-state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing ceramic synthesis routes is usually a laborious, trial-and-error process, as heterogeneous mixtures of powder precursors often evolve through a complicated series of reaction intermediates. Here, we show that phase evolution from multiple precursors can be modeled as a sequence of pairwise interfacial reactions, with thermodynamic driving forces that can be efficiently calculated using ab initio methods. Using the synthesis of the classic high-temperature superconductor YBa$_2$Cu$_3$O$_{6+x}$ (YBCO) as a representative system, we rationalize how replacing the common BaCO$_3$ precursor with BaO$_2$ redirects phase evolution through a kinetically-facile pathway. Our model is validated from in situ X-ray diffraction and in situ microscopy observations, which show rapid YBCO formation from BaO$_2$ in only 30 minutes. By combining thermodynamic modeling with in situ characterization, we introduce a new computable framework to interpret and ultimately design synthesis pathways to complex ceramic materials

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.Comment: 35 pages, 13 figures, under revie

On the successes and opportunities for discovery of metal oxide photoanodes for solar fuels generators

The importance of metal oxide photoanodes in solar fuels technology has garnered concerted efforts in photoanode discovery in recent decades, which complement parallel efforts in development of analytical techniques and optimization strategies using standard photoanodes such as TiO₂, Fe2O₃ and BiVO₄. Theoretical guidance of high throughput experiments has been particularly effective in dramatically increasing the portfolio of metal oxide photoanodes, motivating a new era of photoanode development where the characterization and optimization techniques developed on traditional materials are applied to nascent photoanodes that exhibit visible light photoresponse. The compendium of metal oxide photoanodes presented in the present work can also serve as the basis for further technique development, with a primary goal to establish workflows for discovery of materials that perform better against the critical criteria of operational stability, visible light photoresponse, and photovoltage suitable for tandem absorber architectures

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental science, from which we can develop new insights into materials synthesis for both robot and human chemists

Cellular barcoding of protozoan pathogens for within-host population dynamics and in vivo drug discovery

The obligate intracellular apicomplexan parasite Toxoplasma gondii has broad infectious ability causing disease in humans and animals, some of which can be fatal. Existing treatments for T. gondii infections have notable side effects, and the emergence of resistance to first-line therapies is a growing concern. Understanding the fundamental aspects of T. gondii biology necessitates studying in vivo host-pathogen interactions. However, tracking parasite populations without artificially influencing infection dynamics has posed significant challenges. To address this, we propose a cellular barcoding technique combined with Next Generation Sequencing (NGS) technology to genetically identify and assess the representation of parasite populations. This approach can be applied not only to T. gondii but also to T. brucei and holds potential for future application to other pathogens. Using our cellular barcoding methodology, we conducted population dynamics studies to investigate T. gondii colonisation of the brain parenchyma. Surprisingly, we discovered that the blood-brain barrier (BBB) allows relatively unrestricted traversal by T. gondii, imposing a less stringent bottleneck than anticipated. Moreover, we observed the dynamic nature of chronic T. gondii infection, as brain cyst numbers continued to decrease over several months. Furthermore, we employed the cellular barcoding methodology to facilitate multiplexed in vivo drug screening. Through this approach, we successfully identified small molecule fragments with anti-parasitic effects. Our proof-of-concept data supports the use of this screening platform for iterative drug molecule development. Additionally, in concurrent studies, one of the identified hit fragments exhibited selective inhibition of translation in T. gondii compared to HEK293 cells, prompting further characterisation efforts.Open Acces

2020 roadmap on solid-state batteries

Li-ion batteries have revolutionized the portable electronics industry and empowered the electric vehicle (EV) revolution. Unfortunately, traditional Li-ion chemistry is approaching its physicochemical limit. The demand for higher density (longer range), high power (fast charging), and safer EVs has recently created a resurgence of interest in solid state batteries (SSB). Historically, research has focused on improving the ionic conductivity of solid electrolytes, yet ceramic solids now deliver sufficient ionic conductivity. The barriers lie within the interfaces between the electrolyte and the two electrodes, in the mechanical properties throughout the device, and in processing scalability. In 2017 the Faraday Institution, the UK's independent institute for electrochemical energy storage research, launched the SOLBAT (solid-state lithium metal anode battery) project, aimed at understanding the fundamental science underpinning the problems of SSBs, and recognising that the paucity of such understanding is the major barrier to progress. The purpose of this Roadmap is to present an overview of the fundamental challenges impeding the development of SSBs, the advances in science and technology necessary to understand the underlying science, and the multidisciplinary approach being taken by SOLBAT researchers in facing these challenges. It is our hope that this Roadmap will guide academia, industry, and funding agencies towards the further development of these batteries in the future

Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials

With the advent of machine learning (ML) in the field of Materials Science, it has become obvious that trained models are limited by the amount and quality of the data used for training. Where researchers do not have access to the breadth and depth of labeled data that fields like image processing and natural language processing enjoy. In the specific application of materials discovery, there is the issue of continuity in atomistic datasets. Often if one relies on experimental data mined from literature and patents this data is only available for the most favorable of atomistic data. This ultimately leads to bias in the training dataset. In providing a solution, this research focuses on investigating the deployment of ML models trained on synthetic data and the development of a language-based approach for synthetically generating training datasets. It has been applied to three material science-related problems to prove these approaches work. The first problem was the prediction of dielectric properties, the second problem was the synthetic generation of chemical reaction datasets, and the third problem was the synthetic generation of quantum material datasets. All three applications proved successful and demonstrated the ability to generate continuous datasets that resolve the issue of dataset bias. This first study investigated the synthetic generation of complex dielectric properties of granular powders and their ability to train a ML network. The neural network was trained using a supervised learning approach and a common backpropagation. The network was double-validated using experimental data collected from a coaxial airline experiment. The second study demonstrated the synthetic generation of a chemical reaction database. An artificial intelligence model based on a Variational Autoencoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions that were assembled into the synthetic dataset. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set. The third study investigated a similar variational autoencoder approach to the second study but with the application of generating a synthetic dataset for quantum materials focusing on quantum sensing applications. The specific quantum sensors of interest are two-level quantum molecules that exhibit dipole blockade. This study offers an improved sampling algorithm by continuously feeding newly generated materials into a sampling algorithm to help generate a more normally distributed dataset. This technique was able to generate over 1,000,000 new quantum materials from a small dataset of only 8,000 materials. From the generated dataset it was identified that several iodine-containing molecules are candidate quantum sensor materials for future studies