A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm2MnNiO6 (double perovskite structure), of the form (Bi,Sm)2MnNiO6. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

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.

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 180,902 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80,000 unique quinary oxide and 67,000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and direct band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 240 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena that yield the type of complex data relationships that merit and benefit from neural network-type modelling.

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.

Solar-driven oxygen evolution is a critical technology for renewably synthesizing hydrogenand carboncontaining fuels in solar fuel generators. New photoanode materials are needed to meet efficiency and stability requirements, motivating materials explorations for semiconductors with (i) band-gap energy in the visible spectrum and (ii) stable operation in aqueous electrolyte at the electrochemical potential needed to evolve oxygen from water. Motivated by the oxygen evolution competency of many Mn-based oxides, the existence of several Bi-containing ternary oxide photoanode materials, and the variety of known oxide materials combining these elements with Sm, we explore the Bi−Mn−Sm oxide system for new photoanodes. Through the use of a ferri/ferrocyanide redox couple in high-throughput screening, BiMn2O5 and its alloy with Sm are identified as photoanode materials with a nearideal optical band gap of 1.8 eV. Using density functional theory-based calculations of the mullite BiMnMnO5 phase, we identify electronic analogues to the well-known BiVO4 photoanode and demonstrate excellent Pourbaix stability above the oxygen evolution Nernstian potential from pH 4.5 to 15. Our suite of experimental and computational characterization indicates that BiMn2O5 is a complex oxide with the necessary optical and chemical properties to be an efficient, stable solar fuel photoanode.

Solar-driven oxygen evolution is a critical technology for renewably synthesizing hydrogenand carboncontaining fuels in solar fuel generators. New photoanode materials are needed to meet efficiency and stability requirements, motivating materials explorations for semiconductors with (i) band-gap energy in the visible spectrum and (ii) stable operation in aqueous electrolyte at the electrochemical potential needed to evolve oxygen from water. Motivated by the oxygen evolution competency of many Mn-based oxides, the existence of several Bi-containing ternary oxide photoanode materials, and the variety of known oxide materials combining these elements with Sm, we explore the Bi−Mn−Sm oxide system for new photoanodes. Through the use of a ferri/ferrocyanide redox couple in high-throughput screening, BiMn2O5 and its alloy with Sm are identified as photoanode materials with a nearideal optical band gap of 1.8 eV. Using density functional theory-based calculations of the mullite BiMnMnO5 phase, we identify electronic analogues to the well-known BiVO4 photoanode and demonstrate excellent Pourbaix stability above the oxygen evolution Nernstian potential from pH 4.5 to 15. Our suite of experimental and computational characterization indicates that BiMn2O5 is a complex oxide with the necessary optical and chemical properties to be an efficient, stable solar fuel photoanode.

Solar-driven oxygen evolution is a critical technology for renewably synthesizing hydrogen-and carboncontaining fuels in solar fuel generators.New photoanode materials are needed to meet efficiency and stability requirements, motivating materials explorations for semiconductors with (i) band-gap energy in the visible spectrum and (ii) stable operation in aqueous electrolyte at the electrochemical potential needed to evolve oxygen from water.Motivated by the oxygen evolution competency of many Mn-based oxides, the existence of several Bi-containing ternary oxide photoanode materials, and the variety of known oxide materials combining these elements with Sm, we explore the Bi-Mn-Sm oxide system for new photoanodes.Through the use of a ferri/ferrocyanide redox couple in high-throughput screening, BiMn 2 O 5 and its alloy with Sm are identified as photoanode materials with a nearideal optical band gap of 1.8 eV.Using density functional theory-based calculations of the mullite Bi 3+ Mn 3+ Mn 4+ O 5 phase, we identify electronic analogues to the well-known BiVO 4 photoanode and demonstrate excellent Pourbaix stability above the oxygen evolution Nernstian potential from pH 4.5 to 15.Our suite of experimental and computational characterization indicates that BiMn 2 O 5 is a complex oxide with the necessary optical and chemical properties to be an efficient, stable solar fuel photoanode.

Widespread deployment of solar fuel generators requires the development of efficient and scalable functional materials, especially for photoelectrocatalysis of the oxygen evolution reaction.

The High Throughput Experimentation (HTE) project of the Joint Center for Artificial Photosynthesis (JCAP, http://solarfuelshub.org/) performs accelerated discovery of new earth-abundant photoabsorbers and electrocatalysts. Through collaboration within the DOE solar fuels hub and with the broader research community, the new materials will be utilized in devices that efficiently convert solar energy, water and carbon dioxide into transportation fuels. JCAP-HTE builds high-throughput pipelines for the synthesis, screening and characterization of photoelectrochemical materials. In addition to a summary of these pipelines, we will describe several new screening instruments for high throughput (photo-)electrochemical measurements. These instruments are not only optimized for screening against solar fuels requirements, but also provide new tools for the broader combinatorial materials science community. We will also describe the high throughput discovery, follow-on verification, and device implementation of a new quaternary metal oxide catalyst. This rapid technology development from discovery to device implementation is a hallmark of the multi-faceted JCAP research effort.

High-throughput screening is a powerful approach for identifying new functional materials in unexplored material spaces. With library synthesis capable of producing 10(5) to 10(6) samples per day, methods for material screening at rates greater than 1 Hz must be developed. For the discovery of new solar light absorbers, this throughput cannot be attained using standard instrumentation. Screening certain properties, such as the bandgap, are of interest only for phase pure materials, which comprise a small fraction of the samples in a typical solid-state material library. We demonstrate the utility of colorimetric screening based on processing photoscanned images of combinatorial libraries to quickly identify distinct phase regions, isolate samples with desired bandgap, and qualitatively identify samples that are suitable for complementary measurements. Using multiple quaternary oxide libraries containing thousands of materials, we compare colorimetric screening and UV-vis spectroscopy results, demonstrating successful identification of compounds with bandgap suitable for solar applications.

To achieve higher reliability, safety, and fault-tolerance, many mission-critical detection and decision systems implement consensus algorithms that force the system's underlying sensor networks to reach the states of consensus and unanimous decision among the sensor nodes. Most consensus algorithms presented in the literature utilize local averaging (for continuous values) and majority voting (for discrete values) operators combined with iterative message passing and other similar nearest-neighbor information propagation schemes. Although very simple to implement, such schemes can be very prone to noise because individual detection and decision errors can be amplified and propagated many times throughout the network. For this reason, in this paper we propose a novel consensus algorithm for binary systems that requires each sensor node to participate in message propagation only if its input exceeds a predetermined threshold. This algorithm is a solution to what we call The Chinese Generals Problem, a wide generalization of The Byzantine Generals Problem by Lamport et al [1]. The threshold function used in the algorithm leads to an adjustable network-wide threshold level that defines the minimum number of nodes initially reporting positive detection required in order for all the nodes to reach the correct consensus.

A binary block interference channel (BIC) is model of binary channels with memory that allows for a mathematically tractable computation of channel capacity. One can easily imagine interconnecting such channels into a network that allows point-to-point communication between any two nodes in the network. Given a pair of network nodes, finding the path with the highest capacity is quite trivial if we can assume that all participating nodes in any path connecting the two nodes can perform coding at arbitrary complexity such that at each link capacity is achieved. However, even if the complexity assumption is not taken into account, in most real-life networks (such as the current Internet), only a minimum amount of coding is performed at the link layer. In most networks, coding is performed five or six layers up in the OSI network model, i.e., on either the presentation or the application layer. Under such realistic circumstances, finding the path with the highest capacity is no longer trivial. In this paper, we propose a solution based on a modified version of the Dijkstrapsilas Algorithm.

In this thesis, we introduce generalized network routing metrics that represent probability density parameters of the most popular communication channel models such as (a) the q-ary Symmetric Channel ( q-SC) (b) the q-ary Erasure Channel (q-EC); (c) the Gilbert-Elliot Channel (GEC); and (d) the constrained Additive White Gaussian Noise (AWGN). The GEC is a very important for modelling correlated errors in channels such as the ubiquitous TCP/IP links and the wireless fading channels. In this thesis, we prove that channel models (a)—(d) can be used as inputs to the Generalized Dijkstra's Algorithm without resulting in any routing loop. We also define our own generalized Dijkstra's algorithm that can solve a modified standard shortest path problem that features: (1) a subset of network nodes that are capable of reducing the accumulated path cost down to zero, and (2) a constraint that the cumulative cost of any feasible path must never exceed a prespecified maximum value. We call this modified problem the Gas Station Problem, and its solution the Gas Station Algorithm. The algorithm can be applied in many different areas such as: vehicle routing, project management, and most importantly, network communication. We investigate various auxilliary synchronization algorithms used in popular routing protocols. Synchronization is used by routers to ensure that all routers operate on an identical routing table—not a trivial task, considering network unreliabilities and possible malicious attacks. Our analysis produces a list of assumptions that guarantees synchronization. We also obtain the upper bounds to quantities such as transmission period, memory requirement, etc. In turn, these bounds can be used to rate network performance. Finally, in a related contribution, we analyze message synchronization where a message is retransmitted only if the number of identical messages received exceeds a certain threshold. We define the Chinese Generals Problem as the problem of identifying the set of assumptions under which synchronization is guaranteed. This threshold-base message passing algorithm has the benefits of a tunable gain and a higher noise resistance.

Many different types of modern wired and wireless communication links can be mathematically described as discrete- time Gilbert channels. In this extended abstract, we present an exact method of calculating the best path in a network of discrete- time Gilbert channels, each of which is defined as a Markov chain with two states. In the "Good" state of the chain, the channel produces no erasure, and in the "Bad" state of the chain, the channel produces an erasure. Our method relies on a modified version of the Dijkstra's algorithm, which we customize to operate on sets of Gilbert channel parameters, instead of real numbers. We prove that the Gilbert channels obeys a certain set of algebraic properties which makes it compatible with our algorithm.

In this paper, we extend the optimal worst-case QoS routing algorithm and metric definition given in [1]. We prove that in addition to the q-ary symmetric and q-ary erasure channel model, the necessary and sufficient conditions defined in [2] for the Generalized Dijkstra's Algorithm (GDA) can be used with a constrained non-negative-mean AWGN channel. The generalization allowed the computation of the worst-case QoS metric value for a given edge weight density. The worst-case value can then be used as the routing metric in networks where some nodes have error correcting capabilities. The result is an optimal worst-case QoS routing algorithm that uses the Generalized Dijkstra's Algorithm as a subroutine with a polynomial time complexity of O(V3).

Successful integration of pervasive sensor networks in mission critical applications depends on the ability of these networks to cope with and reasonably perform under the worst-case scenarios. One of the key performance measures is the network's ability to route information from the source node to the intended destination. This paper introduces a general framework with which worst-case routing performance of sensor networks can be evaluated and compared. Ultimately, our method can either be used as a design optimization tool, or a decision making tool to select and price contending sensor network designs

This paper presents a new class of q-ary erasure-correcting codes based on Latin and Sudoku squares of order q, and an iterative decoding algorithm similar to the one used for the Low Density Parity Check code. The algorithm works by assigning binary variables to the q-ary values, and by generalizing the definition of parity check operation to represent the constraints that define the Latin and Sudoku squares.