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We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release. [ABSTRACT FROM AUTHOR]

Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO4-like phase with both Cu2V2O7-like and CuV2O6-like phases, creating avenues for understanding complex photoelectrocatalysts. [ABSTRACT FROM AUTHOR]

Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques. Electrons and phonons give rise to important properties of materials. The machine learning framework Mat2Spec vastly accelerates their computational characterization, enabling discovery of materials for thermoelectrics and solar energy technologies. [ABSTRACT FROM AUTHOR]

The oxygen reduction electrocatalysis is a key reaction for energy technologies such as fuel cells and air breathing batteries for which development of stable and efficient electrocatalysts remains a substantial challenge, especially when excluding precious metals. Multi-component metal oxides are complex materials that offer opportunities for catalyst development and challenges for prediction of optimal compositions. We use high throughput synthesis and electrochemistry to explore metal oxide electrocatalysts containing 1 to 4 of the elements Mg, Ca, Mn, Fe, Ni, Y, In, and La. Mining the resulting dataset of 7798 unique metal oxide compositions guides additional activity and stability experiments that reveal multiple avenues for further catalyst development based on (i) stabilization of Mn through incorporation in Mn-Ni–Fe oxides, (ii) discovery of Ni–Fe-La oxide electrocatalysts with excellent stability, and (iii) discovery of Mn-Ni–Fe-La oxide electrocatalysts that optimize activity while retaining excellent operational stability. [ABSTRACT FROM AUTHOR]

We introduce CRYSTAL, a multi-agent AI system for crystal-structure phase mapping. CRYSTAL is the first system that can automatically generate a portfolio of physically meaningful phase diagrams for expert-user exploration and selection. CRYSTAL outperforms previous methods to solve the example Pd-Rh-Ta phase diagram, enabling the discovery of a mixed-intermetallic methanol oxidation electrocatalyst. The integration of multiple data-knowledge sources and learning and reasoning algorithms, combined with the exploitation of problem decompositions, relaxations, and parallelism, empowers AI to supersede human scientific data interpretation capabilities and enable otherwise inaccessible scientific discovery in materials science and beyond. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Many solar fuel generator designs involve illumination of a photoabsorber stack coated with a catalyst for the oxygen evolution reaction (OER). In this design, impinging light must pass through the catalyst layer before reaching the photoabsorber(s), and thus optical transmission is an important function of the OER catalyst layer. Many oxide catalysts, such as those containing elements Ni and Co, form oxide or oxyhydroxide phases in alkaline solution at operational potentials that differ from the phases observed in ambient conditions. To characterize the transparency of such catalysts during OER operation, 1031 unique compositions containing the elements Ni, Co, Ce, La, and Fe were prepared by a high throughput inkjet printing technique. The catalytic current of each composition was recorded at an OER overpotential of 0.33 V with simultaneous measurement of the spectral transmission. By combining the optical and catalytic properties, the combined catalyst efficiency was calculated to identify the optimal catalysts for solar fuel applications within the material library. The measurements required development of a new high throughput instrument with integrated electrochemistry and spectroscopy measurements, which enables various spectroelectrochemistry experiments. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond. [ABSTRACT FROM AUTHOR]

Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties. Design Type(s) classification objective • observation design Measurement Type(s) absorption spectrum Technology Type(s) ultraviolet-visible spectrophotometry Factor Type(s) metal oxide Sample Characteristic(s) Machine-accessible metadata file describing the reported data (ISA-Tab format) [ABSTRACT FROM AUTHOR]

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data. [ABSTRACT FROM AUTHOR]

The photocatalytic conversion of the greenhouse gas CO2 to chemical fuels such as hydrocarbons and alcohols continues to be a promising technology for renewable generation of energy. Major advancements have been made in improving the efficiencies and product selectiveness of currently known CO2 reduction electrocatalysts, nonetheless, materials discovery is needed to enable economically viable, industrial-scale CO2 reduction. We report here the largest CO2 photocathode search to date, starting with 68860 candidate materials, using a rational first-principles computation-based screening strategy to evaluate synthesizability, corrosion resistance, visible-light absorption, and compatibility of the electronic structure with fuel synthesis. The results confirm the observation of the literature that few materials meet the stringent CO2 photocathode requirements, with only 52 materials meeting all requirements. The results are well validated with respect to the literature, with 9 of these materials having been studied for CO2 reduction, and the remaining 43 materials are discoveries from our pipeline that merit further investigation. While the conversion of greenhouse CO2 to chemical fuels offers a promising renewable energy technology, there is a dire need for new materials. Here, authors report the largest CO2 photocathode search using a first-principles approach to identify both known and unreported candidate photocatalysts. [ABSTRACT FROM AUTHOR]