Utilizing pulsed laser deposition lateral inhomogeneity as a tool in combinatorial material science

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Combinatorial synthesis of heteroepitaxial, multi-cation, thin-films via pulsed laser deposition coupled with in-situ, chemical and structural characterization

Combinatorial synthesis via a continuous composition spread is an excellent route to develop thin-film libraries as it is both time- and cost-efficient. Creating libraries of functional, multicomponent, complex oxide films requires excellent control over the synthesis parameters combined with high-throughput analytical feedback. A reliable, high-throughput, in-situ characterization analysis method is required to meet the crucial need to rapidly screen materials libraries. Here, we report on the combination of two in-situ techniques—(a) Reflection high-energy electron diffraction (RHEED) for heteroepitaxial characterization and a newly developed compositional analysis technique, low-angle x-ray spectroscopy (LAXS), to map the chemical composition profile of combinatorial heteroepitaxial complex oxide films deposited using a continuous composition spread method via pulsed laser deposition. This is accomplished using a unique state-of-the-art combinatorial growth system with a fully synchronized four-axis mechanical substrate stage without shadow masks, alternating acquisition of chemical compositional data using LAXS at various different positions on the $$\sim$$ ∼ 41 mm $$\times$$ × 41 mm range and sequential deposition of multilayers of SrTiO$$_3$$ 3 and $$\hbox {SrTi}_{0.8}\hbox {Ru}_{0.2}\hbox {O}_3$$ SrTi 0.8 Ru 0.2 O 3 on a 2-inch (50.8 mm) $$\hbox {LaAlO}_3$$ LaAlO 3 wafer in a single growth run. Rutherford backscattering spectrometry (RBS) is used to calibrate and validate the compositions determined by LAXS. This study shows the feasibility of combinatorial synthesis of heteroepitaxial, functional complex oxide films at wafer-scale via two essential in-situ characterization tools—RHEED for structural analysis or heteroepitaxy and LAXS for compositional characterization. This is a powerful technique for development of new films with optimized heteroepitaxy and composition.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Progress and prospects for accelerating materials science with automated and autonomous workflows

Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery.

Photoactive organic material discovery with combinatorial supramolecular assembly

Organic semiconductors have received substantial attention as active components in optoelectronic devices because of their processability and customizable properties. Tailoring the organic active layer in these devices to exhibit the desired optoelectronic properties requires understanding the complex and often subtle structure-property relationships governing their photophysical response to light. Both structural organization and molecular orbitals play pivotal roles, and their interactions with each other are difficult to anticipate based upon the structure of the components alone, especially in systems comprised of multiple components. In pursuit of design rules, there is a need to explore multicomponent systems combinatorially to access larger data sets, and supramolecularly to use error correcting, noncovalent assembly to achieve long-range order. This review will focus on the use of supramolecular chemistry to study combinatorial, hierarchical organic systems with emergent optoelectronic properties. Specifically, we will describe systems that undergo excited state deactivation by charge transfer (CT), singlet fission (SF), and Förster resonance energy transfer (FRET). Adopting combinatorial, supramolecular assembly to study emergent photophysics promises to rapidly accelerate progress in this research field.

Realization of Spatially Addressable Library by a Novel Combinatorial Approach on Atomic Layer Deposition: A Case Study of Zinc Oxide

Though the synthesis of libraries of multicomponent metal oxide systems is prevalent using the combinatorial approach, the combinatorial approach has been rarely realized in studying simple metal oxides, especially applied to the atomic layer deposition (ALD) technique. In this literature, a novel combinatorial approach technique is utilized within an ALD grown simple metal oxide to synthesize a "spatially addressable combinatorial library". The two key factors in gradients were defined during the ALD process: (1) the process temperature and (2) a nonuniform flow of pulsed gases inside a cross-flow reactor. To validate the feasibility of our novel combinatorial approach, a case study of zinc oxide (ZnO), a simple metal oxide whose properties are well-known, is performed. Because of the induced gradient, the ZnO (002) crystallite size was found to gradually vary across a 100 mm wafer (∼10-20 nm) with a corresponding increase in the normalized Raman E₂/A₁ peak intensity ratio. The findings agree well with the visible grain size observed from scanning electron microscope. The novel combinatorial approach provides a means of systematical interpretation of the combined effect of the two gradients, especially in the analysis of the microstructure of ZnO crystals. Moreover, the combinatorial library reveals that the process temperature, rather than the crystal size, plays the most significant role in determining the electrical conductivity of ZnO.

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV

Data mining tools have been known to be useful for analyzing large material data sets generated by high-throughput methods. Typically, the descriptors used for the analysis are structural descriptors, which can be difficult to obtain and to tune according to the results of the analysis. In this Research Article, we show the use of deposition process parameters as descriptors for analysis of a photovoltaics data set. To create a data set, solar cell libraries were fabricated using iron oxide as the absorber layer deposited using different deposition parameters, and the photovoltaic performance was measured. The data was then used to build models using genetic programing and stepwise regression. These models showed which deposition parameters should be used to get photovoltaic cells with higher performance. The iron oxide library fabricated based on the model predictions showed a higher performance than any of the previous libraries, which demonstrates that deposition process parameters can be used to model photovoltaic performance and lead to higher performing cells. This is a promising technique toward using data mining tools for discovery and fabrication of high performance photovoltaic materials.

Influence of the Cation Ratio on Optical and Electrical Properties of Amorphous Zinc-Tin-Oxide Thin Films Grown by Pulsed Laser Deposition

Continuous composition spread (CCS) methods allow fast and economic exploration of composition dependent properties of multielement compounds. Here, a CCS method was applied for room temperature pulsed laser deposition (PLD) of amorphous zinc-tin-oxide to gain detailed insight into the influence of the zinc-to-tin cation ratio on optical and electrical properties of this ternary compound. Our CCS approach for a large-area offset PLD process utilizes a segmented target and thus makes target exchange or movable masks in the PLD chamber obsolete. Cation concentrations of 0.08-0.82 Zn/(Zn + Sn) were achieved across single 50 × 50 mm(2) glass substrates. The electrical conductivity increases for increasing tin content, and the absorption edge shifts to lower energies. The free carrier concentration can be tuned from 10(20) to 10(16) cm(-3) by variation of the cation ratio from 0.1 to 0.5 Zn/(Zn + Sn).

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material process-structure-property relationships requires high-throughput data analysis techniques. This work explores the use of the Graph-based endmember extraction and labeling (GRENDEL) algorithm as a high-throughput method for analyzing structural data from combinatorial libraries, specifically, to determine phase diagrams and constituent phases from both x-ray diffraction and Raman spectral data. The GRENDEL algorithm utilizes a set of physical constraints to optimize results and provides a framework by which additional physics-based constraints can be easily incorporated. GRENDEL also permits the integration of database data as shown by the use of critically evaluated data from the Inorganic Crystal Structure Database in the x-ray diffraction data analysis. Also the Sunburst radial tree map is demonstrated as a tool to visualize material structure-property relationships found through graph based analysis.