High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis

High-throughput experimentation provides efficient mapping of composition–property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe_2O_3, Cu_2V_2O_7, and BiVO_4. The applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra

Tracking materials science data lineage to manage millions of materials experiments and analyses

In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research

Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

High-throughput experimental methodologies are capable of synthesizing, screening and characterizing vast arrays of combinatorial material libraries at a very rapid rate. These methodologies strategically employ tiered screening wherein the number of compositions screened decreases as the complexity, and very often the scientific information obtained from a screening experiment, increases. The algorithm used for down-selection of samples from higher throughput screening experiment to a lower throughput screening experiment is vital in achieving information-rich experimental materials genomes. The fundamental science of material discovery lies in the establishment of composition–structure–property relationships, motivating the development of advanced down-selection algorithms which consider the information value of the selected compositions, as opposed to simply selecting the best performing compositions from a high throughput experiment. Identification of property fields (composition regions with distinct composition-property relationships) in high throughput data enables down-selection algorithms to employ advanced selection strategies, such as the selection of representative compositions from each field or selection of compositions that span the composition space of the highest performing field. Such strategies would greatly enhance the generation of data-driven discoveries. We introduce an informatics-based clustering of composition-property functional relationships using a combination of information theory and multitree genetic programming concepts for identification of property fields in a composition library. We demonstrate our approach using a complex synthetic composition-property map for a 5 at. % step ternary library consisting of four distinct property fields and finally explore the application of this methodology for capturing relationships between composition and catalytic activity for the oxygen evolution reaction for 5429 catalyst compositions in a (Ni–Fe–Co–Ce)O_x library

Combining reactive sputtering and rapid thermal processing for synthesis and discovery of metal oxynitrides

Recent efforts have demonstrated enhanced tailoring of material functionality with mixed anion materials, yet exploratory research with mixed anion chemistries is limited by the sensitivity of these materials to synthesis conditions. Synthesis of a particular metal oxynitride compound by traditional reactive annealing requires specific, limited ranges of both oxygen and nitrogen chemical potentials to establish equilibrium between the solid-state material and a reactive atmosphere. Using Ta–O–N as an example system, we describe a combination of reactive sputter deposition and rapid thermal processing (RTP) for synthesis of mixed anion inorganic materials. Heuristic optimization of reactive gas pressures to attain a desired anion stoichiometry is discussed, and the ability of RTP to enable amorphous to crystalline transitions without preferential anion loss is demonstrated through the controlled synthesis of nitride, oxide, and oxynitride phases

Combinatorial thin film composition mapping using three dimensional deposition profiles

Many next-generation technologies are limited by material performance, leading to increased interest in the discovery of advanced materials using combinatorial synthesis, characterization, and screening. Several combinatorial synthesis techniques, such as solution based methods, advanced manufacturing, and physical vapor deposition, are currently being employed for various applications. In particular, combinatorial magnetron sputtering is a versatile technique that provides synthesis of high-quality thin film composition libraries. Spatially addressing the composition of these thin films generally requires elemental quantification measurements using techniques such as energy-dispersive X-ray spectroscopy or X-ray fluorescence spectroscopy. Since these measurements are performed ex-situ and post-deposition, they are unable to provide real-time design of experiments, a capability that is required for rapid synthesis of a specific composition library. By using three quartz crystal monitors attached to a stage with translational and rotational degrees of freedom, we measure three-dimensional deposition profiles of deposition sources whose tilt with respect to the substrate is robotically controlled. We exhibit the utility of deposition profiles and tilt control to optimize the deposition geometry for specific combinatorial synthesis experiments

Benchmarking the Acceleration of Materials Discovery by Sequential Learning

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any “good” material, discovery of all “good” materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery

Tracking materials science data lineage to manage millions of materials experiments and analyses

In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes

Combinatorial (photo)electrochemical studies of the (Ni–Mn)O_x system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO_3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap. The photoactivity increases upon mixture with 10–60% Ni_6MnO_8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation

High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer

We have developed an on-the-fly scanning spectrometer operating in the UV-visible and near-infrared that can simultaneously perform transmission and total reflectance measurements at the rate better than 1 sample per second. High throughput optical characterization is important for screening functional materials for a variety of new applications. We demonstrate the utility of the instrument for screening new light absorber materials by measuring the spectral absorbance, which is subsequently used for deriving band gap information through Tauc plot analysis