Combinatorial and High-Throughput Materials Science

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

Printing of In Situ Functionalized Mesoporous Silica with Digital Light Processing for Combinatorial Sensing

Combinatorial sensing is especially important in the context of modern drug development to enable fast screening of large data sets. Mesoporous silica materials offer high surface area and a wide range of functionalization possibilities. By adding structural control, the combination of structural and functional control along all length scales opens a new pathway that permits larger amounts of analytes being tested simultaneously for complex sensing tasks. This study presents a fast and simple way to produce mesoporous silica in various shapes and sizes between 0.27-6 mm by using light-induced sol-gel chemistry and digital light processing (DLP). Shape-selective functionalization of mesoporous silica is successfully carried out either after printing using organosilanes or in situ while printing through the use of functional mesopore template for the in situ functionalization approach. Shape-selective adsorption of dyes is shown as a demonstrator toward shape selective screening of potential analytes.

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.

OH End-Capped Silicone as an Effective Nucleating Agent for Polylactide-A Robotizing Method for Evaluating the Mechanical Characteristics of PLA/Silicone Blends

Current research on materials engineering focuses mainly on bio-based materials. One of the most frequently studied materials in this group is polylactide (PLA), which is a polymer derived from starch. PLA does not have a negative impact on the natural environment and additionally, it possesses properties comparable to those of industrial polymers. The aim of the work was to investigate the potential of organosilicon compounds as modifiers of the mechanical and rheological properties of PLA, as well as to develop a new method for conducting mechanical property tests through innovative high-throughput technologies. Precise dosing methods were utilized to create PLA/silicone polymer blends with varying mass contents, allowing for continuous characterization of the produced blends. To automate bending tests and achieve comprehensive characterization of the blends, a self-created workstation setup has been used. The tensile properties of selected blend compositions were tested, and their ability to withstand dynamic loads was studied. The blends were characterized through various methods, including rheological (MFI), X-ray (XRD), spectroscopic (FTIR), and thermal properties analysis (TG, DSC, HDT), and they were evaluated using microscopic methods (MO, SEM) to examine their structures.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process

During data-driven process condition optimization on a laboratory scale, only a small-size data set is accessible and should be effectively utilized. On the other hand, during process development, new operations are frequently inserted or current operations are modified. These accessible data sets are somewhat related but not exactly the same type. In this study, we focus on the prediction of the quality of the interface between an insulator and GaN as a semiconductor for the potential application of GaN power semiconductor devices. The quality of the interface was represented as the interface state density, Dit, and the inserted operation to the process was the ultraviolet (UV)/O3-gas treatment. Our retrospective evaluation of model-building approaches for Dit prediction from a process condition revealed that for the UV/O3-treated interfaces, data of interfaces without the treatment contributed to performance improvement. Such performance improvement was not observed when using a data set of Si as the semiconductor. As a modeling method, the automatic relevance vector-based Gaussian process regression with the prior distribution of the length-scale parameters exhibited a relatively high predictive performance and represented a reasonable uncertainty of prediction as reflected by the distance to the training data set. This feature is a prerequisite for a potential application of Bayesian optimization. Furthermore, hyperparameters in the prior distribution of the length-scales could be optimized by leave-one-out cross-validation.

Effects of Rare Earth Metal Promotion over Zeolite-Supported Fe-Cu-Based Catalysts on the Light Olefin Production Performance in Fischer-Tropsch Synthesis

Fischer-Tropsch synthesis (FTS), a significant reaction for effective H₂ utilization, is a promising approach for direct production of light olefins from syngas (H₂ + CO). For the FT-Olefin process, an efficient catalyst restricting the product distribution of FTS to light olefins is required. Aligned with this goal, we synthesized 24 catalysts comprising Fe and Cu in combination with rare earth metals (La, Ce, Nd, Ho, Er) and zeolite supports (ultrastable Y and mordenite). FT-Olefin performances of these catalysts were screened using a high-throughput test system at atmospheric pressure, and then promising catalysts were tested under high pressure in a conventional test system. Results show that Nd increases selectivity to light olefins and Ho suppresses C₅+ and coke formation. It is also demonstrated that zeolite-metal interaction, leading to a mixture of both acidic and basic sites, is significant in increasing light olefin production. The mordenite-supported 20 wt % Fe, 0.5 wt % Cu, and 0.5 wt % Ho catalyst provides the highest light olefin yield with the lowest coke and heavier hydrocarbon selectivity.

Reaction-Free Concentration Gradient Generation in Spatially Nonuniform AC Electric Fields

The ability to generate stable, spatiotemporally controllable concentration gradients is critical for both electrokinetic and biological applications such as directional wetting and chemotaxis. Electrochemical techniques for generating solution and surface gradients display benefits such as simplicity, controllability, and compatibility with automation. Here, we present an exploratory study for generating microscale spatiotemporally controllable gradients using a reaction-free electrokinetic technique in a microfluidic environment. Methanol solutions with ionic fluorescein isothiocyanate (FITC) molecules were used as an illustrative electrolyte. Spatially nonuniform alternating current (AC) electric fields were applied using hafnium dioxide (HfO₂)-coated Ti/Au electrode pairs. Results from spatial and temporal analyses along with control experiments suggest that the FITC ion concentration gradient in bulk fluid (over 50 μm from the electrode) was established due to spatial variation of electric field density, and was independent of electrochemical reactions at the electrode surface. The established ion concentration gradients depended on both amplitudes and frequencies of the oscillating AC electric field. Overall, this work reports a novel approach for generating stable and spatiotemporally tunable gradients in a microfluidic chamber using a reaction-free electrochemical methodology.

nmfMapping: a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets

A cloud-hosted web-based software application, nmfMapping, for carrying out a non-negative matrix factorization of a set of powder diffraction or atomic pair distribution function datasets is described. This application allows structure scientists to find trends rapidly in sets of related data such as from in situ and operando diffraction experiments. The application is easy to use and does not require any programming expertise. It is available at https://pdfitc.org/.

Tool for Designing Breakthrough Discovery in Materials Science

A database of material property relationships, which serves as a scientific principles database, and a database search system are proposed and developed. The use of this database can support a broader research perspective, which is increasingly important in the era of automated computer-aided experimentation and machine learning of experimental and calculated data. Examples of the wider use of scientific principles in materials research are presented. The database and its advantages are described. An implementation of the proposed database and search system as a prototype software is reported. The usefulness of the database and search system is demonstrated by an example of a surprising but reasonable discovery.

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

Discovery of complex oxides via automated experiments and data science

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Statistical Design of Biocarbon Reinforced Sustainable Composites from Blends of Polyphthalamide (PPA) and Polyamide 4,10 (PA410)

A full factorial design with four factors (the ratio of polyphthalamide (PPA) and polyamide 4,10 (PA410) in the polymer matrix, content percent of biocarbon (BioC), the temperature at which it was pyrolyzed and the presence of a chain extender (CE)), each factor with two levels (high and low), was carried out to optimize the mechanical properties of the resulting composites. After applying a linear model, changes in tensile strength, elongation at break and impact energy were not statistically significant within the considered material space, while the ones in the flexural modulus, the tensile modulus, density and heat deflection temperature (HDT) were. The two most influential factors were the content of BioC and its pyrolysis temperature, followed by the content of PPA. The affinity of PPA with a high-temperature biocarbon and the affinity of PA410 with a lower-temperature biocarbon, appear to explain the mechanical properties of the resulting composites. The study also revealed that the addition of CE hindered the mechanical properties. By maximizing the flexural modulus, tensile modulus and HDT, while minimizing the density, the optimal composite predicted is an 80 [PPA:PA410 (25:75)] wt% polymer composite, with 20 wt% of a BioC, pyrolyzed at a calculated 823 °C.

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

Mixed oxide growth on combinatorial aluminium–gadolinium alloys — a thermodynamic and first-principles approach

Metal surfaces covered with oxides have attracted considerable scientific attention in various applications. In particular, anodic films fabricated by cost-effective anodizing have been widely used in nano-structured engineering to provide various surface functionalities. However, understanding of alloy film stability, having individual elements with widely varying structures and morphologies, is very limited due to lack of thermodynamic information and effects of electrolyte chemistry. This requires many tedious efforts on a trial and error basis in selecting suitable electrolytes that can produce the protective film at high efficiency on alloys having mixed chemistries. It is, therefore, crucial to develop a combination of high throughput theoretical analysis and automated rapid localized electrochemical probing that provides a fast and simple solution for electrolyte choice and paves the way to the remarkable expansion of industrial applications of oxides. Herein, we demonstrate that combinatorial Al–Gd alloys covering 1.0 to 10.0 at.% Gd can be oxidized into ultra-thin anodic films of controlled thickness through a selection of electrolyte based on thermodynamics (phosphate buffer with a pH of 8.20). We propose that growth of anodic films on alloys at high efficiency is possible if Gibbs free energy minimization criteria would be systematically contemplate.

Graphical abstract

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Finally, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

Accelerating organic solar cell material's discovery: high-throughput screening and big data

The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhance big data readiness. Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure-activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics.

Organocatalytic Combinatorial Synthesis of Quinazoline, Quinoxaline and Bis(indolyl)methanes

AIMS AND OBJECTIVE

An efficient and practical procedure for the synthesis of heterocyclic compounds such as quinazolines, quinoxalines and bis(indolyl)methanes was developed using 3,5-bis(trifluoromethyl) phenyl ammonium hexafluorophosphate (BFPHP) as a novel organocatalyst.

MATERIALS AND METHODS

All of the obtained products are known compounds and identified by IR, 1HNMR, 13CNMR and melting points.

RESULT

Various products were obtained in good to excellent yields under reaction conditions.

CONCLUSION

The BFPHP organocatalyst demonstrates a novel class of non-asymmetric organocatalysts, which has gained much attention in green chemistry.

High-throughput development of highly active catalyst system to convert bioethanol to 1,3-butadiene

The development of highly active catalysts for the conversion of ethanol to 1,3-butadiene using high-throughput catalyst preparation and evaluation systems.

Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an "Electronic Olfaction" Unit

Information about the surrounding atmosphere at a real timescale significantly relies on available gas sensors to be efficiently combined into multisensor arrays as electronic olfaction units. However, the array's performance is challenged by the ability to provide orthogonal responses from the employed sensors at a reasonable cost. This issue becomes more demanded when the arrays are designed under an on-chip paradigm to meet a number of emerging calls either in the internet-of-things industry or in situ noninvasive diagnostics of human breath, to name a few, for small-sized low-powered detectors. The recent advances in additive manufacturing provide a solid top-down background to develop such chip-based gas-analytical systems under low-cost technology protocols. Here, we employ hydrolytically active heteroligand complexes of metals as ink components for microplotter patterning a multioxide combinatorial library of chemiresistive type at a single chip equipped with multiple electrodes. To primarily test the performance of such a multisensor array, various semiconducting oxides of the p- and n-conductance origins based on pristine and mixed nanocrystalline MnO_x, TiO₂, ZrO₂, CeO₂, ZnO, Cr₂O₃, Co₃O₄, and SnO₂ thin films, of up to 70 nm thick, have been printed over hundred μm areas and their micronanostructure and fabrication conditions are thoroughly assessed. The developed multioxide library is shown to deliver at a range of operating temperatures, up to 400 °C, highly sensitive and highly selective vector signals to different, but chemically akin, alcohol vapors (methanol, ethanol, isopropanol, and n-butanol) as examples at low ppm concentrations when mixed with air. The suggested approach provides us a promising way to achieve cost-effective and well-performed electronic olfaction devices matured from the diverse chemiresistive responses of the printed nanocrystalline oxides.

Rational Design of Semiconductor-Based Chemiresistors and their Libraries for Next-Generation Artificial Olfaction

Artificial olfaction based on gas sensor arrays aims to substitute for, support, and surpass human olfaction. Like mammalian olfaction, a larger number of sensors and more signal processing are crucial for strengthening artificial olfaction. Due to rapid progress in computing capabilities and machine-learning algorithms, on-demand high-performance artificial olfaction that can eclipse human olfaction becomes inevitable once diverse and versatile gas sensing materials are provided. Here, rational strategies to design a myriad of different semiconductor-based chemiresistors and to grow gas sensing libraries enough to identify a wide range of odors and gases are reviewed, discussed, and suggested. Key approaches include the use of p-type oxide semiconductors, multinary perovskite and spinel oxides, carbon-based materials, metal chalcogenides, their heterostructures, as well as heterocomposites as distinctive sensing materials, the utilization of bilayer sensor design, the design of robust sensing materials, and the high-throughput screening of sensing materials. In addition, the state-of-the-art and key issues in the implementation of electronic noses are discussed. Finally, a perspective on chemiresistive sensing materials for next-generation artificial olfaction is provided.

Multi-channel fiber optical spectrometer for high-throughput characterization of photoluminescence properties

High-throughput experiment can significantly accelerate the materials research efficiency. Thanks to national efforts, the Materials Genome Initiative further promotes the development of high-throughput experimental technology. A multi-channel fiber optical spectrometer has been designed and developed by us for high-throughput characterization of photoluminescence (PL) properties. It can quickly and automatically detect the PL spectrum, Commission International de l'Eclairage chromaticity, and PL intensity over time for luminescent materials under a given condition. The multi-channel fiber optical spectrometer synergistically combines a sample library holder, multiple modular excitation sources, multiple spectrometers, and Coral software, so it can measure and analyze multiple samples simultaneously. The number of channels in the multi-channel fiber optical spectrometer can be added or subtracted as required. Various modular light-emitting diode or laser diode excitation sources with the wavelength from 370 nm to 980 nm and corresponding filters can be provided according to the measurement need of different luminescent materials. The monitoring wavelength of the currently used fiber optical spectrometer is from 300 nm to 1000 nm. For example, the PL spectral measurement of 54 samples in a {6 × 9} array is completed in only about 30 min by using a representative triple-channel fiber optical spectrometer. The designed multi-channel fiber optical spectrometer facility not only makes PL measurements faster and more intuitive but is also easy to popularize for wide users.

A Review on New 3-D Printed Materials' Geometries for Catalysis and Adsorption: Paradigms from Reforming Reactions and CO2 Capture

"Bottom-up" additive manufacturing (AM) is the technology whereby a digitally designed structure is built layer-by-layer, i.e., differently than by traditional manufacturing techniques based on subtractive manufacturing. AM, as exemplified by 3D printing, has gained significant importance for scientists, among others, in the fields of catalysis and separation. Undoubtedly, it constitutes an enabling pathway by which new complex, promising and innovative structures can be built. According to recent studies, 3D printing technologies have been utilized in enhancing the heat, mass transfer, adsorption capacity and surface area in CO2 adsorption and separation applications and catalytic reactions. However, intense work is needed in the field to address further challenges in dealing with the materials and metrological features of the structures involved. Although few studies have been performed, the promise is there for future research to decrease carbon emissions and footprint. This review provides an overview on how AM is linked to the chemistry of catalysis and separation with particular emphasis on reforming reactions and carbon adsorption and how efficient it could be in enhancing their performance.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.

A Scalable High-Throughput Deposition and Screening Setup Relevant to Industrial Electrocatalysis

The identification and optimization of electrode materials is of great importance in the study of (flow and solid state) batteries, industrial electrocatalysis and analytical devices such as sensors. To identify useful materials from a virtually unbound set of metals, alloys and semiconductors, high-throughput techniques are of vital importance. In this paper we present a high-throughput setup that consists of 64 parallel plate electrochemical flow cells, with the anode and cathode compartments separated by a membrane. These cells can be operated sequentially or batch-wise in parallel, using a matrix-addressing approach that allows for scaling up to larger electrode matrices with minimal instrumentation cost. The setup was validated for the preparation and screening of electrode materials under hydrodynamic conditions at industrially relevant current densities, which showed that it could be used to identify optimal catalysts and the robustness of catalyst preparation. The results of the small scale experiments followed theoretical predictions and were used to optimize larger scale experiments.

High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

High throughput experimentation in heterogeneous catalysis provides an efficient solution to the generation of large datasets under reproducible conditions. Knowledge extraction from these datasets has mostly been performed using statistical methods, targeting the optimization of catalyst formulations. The combination of advanced machine learning methodologies with high-throughput experimentation has enormous potential to accelerate the predictive discovery of novel catalyst formulations that do not exist with current statistical design of experiments. This perspective describes selective examples ranging from statistical design of experiments for catalyst synthesis to genetic algorithms applied to catalyst optimization, and finally random forest machine learning using experimental data for the discovery of novel catalysts. Lastly, this perspective also provides an outlook on advanced machine learning methodologies as applied to experimental data for materials discovery.

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g. development of robust, physically meaningful multiscale material representations) to social (e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.

Visualizing Scientists' Cognitive Representation of Materials Data through the Application of Ontology

The introduction of data science as a viable new approach to research has led toward the establishment of materials informatics. However, issues relating to the infrastructure of data collection and organization in materials science have hindered the development of materials informatics. Issues related to data quality, conflicting terminologies between subfields, and inconsistent recording practices make it difficult to share data and implement data science. Furthermore, one can consider that scientific discoveries have occurred via the rules that are unconsciously defined by the scientist's mind, which has made scientific discovery an unintentional process. Here, ontology is proposed as a new way to structure databases as well as model scientific understandings of data. By implementing ontology during the database creation process, it not only becomes possible to define and visualize the experiences and knowledge held by researchers but also provides a way of creating a field-wide standard of defining data, the ability to incorporate data semantics, a method to increase the solid choice of descriptors for determining the materials' properties, and the space to merge databases in a more interactive and coherent manner. Ontology can also help improve database management by providing a way to incorporate new scientific discoveries into existing databases, which can have a positive effect on the search for new materials and material design.

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.

Cleaning Matters!

Translation of a manual process to high throughput for research and development requires special consideration. One important and often unreported aspect is the establishment of an efficient cleaning routine. This becomes significant, as precious time and, in particular, material would be lost, that is, when low-quality high-throughput experimentation is involved. We present a fully automated cleaning routine of the challenging synthesis of cadmium selenide quantum dots. Manual, semiautomated, and fully automated cleaning protocols were executed and compared in terms of spectral similarities of the synthesized colloids. Only the fully automated protocol enabled true 24/7 operation.

Parameter Optimization in High-Throughput Testing for Structural Materials

High-throughput screenings are established evaluation methods in the development of functional materials and pharmaceutical active ingredients. The transfer of this approach to the development of structural materials requires extensive adaptations. In addition to the investigation of new test procedures for the determination of material properties and the treatment of metallic materials, the design of experiments is a research focus. Based on given descriptor target values, the statistical design of experiments determines investigations and treatments for the investigation of these materials. In this context, process parameters also have to be determined, as these have a major influence on the later material properties, especially during the treatment of samples. In this article, a method is presented which determines the process parameters iteratively. The validation of the calculated process parameters takes place based on differential scanning calorimetry used as the furnace for the heat treatment of small batches and particle-oriented peening as the characterization method.

High-Throughput Characterization of Structural and Photoelectrochemical Properties of a Bi–Mo–W–O Thin-Film Materials Library

A Bi–W–Mo–O thin-film materials library was fabricated by combinatorial reactive magnetron sputtering. The composition spread was investigated using high-throughput methods to determine crystalline phases, composition, morphology, optical properties, and photoelectrochemical performance. The aurivillius phase (Bi2O2)2+ (BiM(W1−NMoN)M−1O3M+1)2− is the predominantly observed crystal structure, indicating that the thin films in the library are solid solutions. With increasing amounts of Mo ≙ 7–22% the diffraction peak at 2θ = 28° ≙ [131] shifts due to lattice distortion, the photoelectrochemical activity is increasing up to a wavelength of 460 nm with an incident photon to current efficiency (IPCE) of 4.5%, and the bandgap decreases. A maximum photocurrent density of 31 μA/cm2 was measured for Bi31W62Mo7Oz at a bias potential of 1.23 V vs. RHE (0.1 M Na2SO4).

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially resolved characterization of structure and properties. Because of the maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment and designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data-analysis and -visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

Early Years of High-Throughput Experimentation and Combinatorial Approaches in Catalysis and Materials Science

This is a report on the early years of combinatorial materials science and technology. High-throughput technologies (HTTs) are found in life- and materials-science laboratories. Although HTTs have long been the standard in life sciences in academia as well as in industry, HTTs in materials science have become the standard in industry but not in academia. In life science, successful drugs developed with HTTs have been reported, but there is no information on successful materials developed with HTTs that have made it to the market. Some initial development of HTTs in materials science is summarized, especially early applications of artificial intelligence. This outlook attempts to summarize the development of combinatorial materials sciences from the early years to today.

An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods

High-throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many technological applications. However, there are very few publications on the reproducibility of the HTE results obtained across different laboratories for the same materials system, and on the associated sample and data exchange standards. Here, we report a comparative study of Zn-Sn-Ti-O thin films materials using high-throughput experimental methods at National Institute of Standards and Technology (NIST) and National Renewable Energy Laboratory (NREL). The thin film sample libraries were synthesized by combinatorial physical vapor deposition (cosputtering and pulsed laser deposition) and characterized by spatially resolved techniques for composition, structure, thickness, optical, and electrical properties. The results of this study indicate that all these measurement techniques performed at two different laboratories show excellent qualitative agreement. The quantitative similarities and differences vary by measurement type, with 95% confidence interval of 0.1-0.2 eV for the band gap, 24-29 nm for film thickness, and 0.08 to 0.37 orders of magnitude for sheet resistance. Overall, this work serves as a case study for the feasibility of a High-Throughput Experimental Materials Collaboratory (HTE-MC) by demonstrating the exchange of high-throughput sample libraries, workflows, and data.

Computational Screening of Layered Materials for Multivalent Ion Batteries

Batteries based on multivalent ion (such as Al³⁺, Ca²⁺, and Mg²⁺) intercalation materials have attracted extensive research interest due to their impressive capacity improvement and cost reduction compared with Li-ion batteries. However, the materials for state-of-the-art multivalent ion batteries still suffer from drawbacks such as sluggish ion mobility, poor rate performance, and low cyclic stability, bringing challenges for the design and investigation of new materials. Layered cathode materials are widely applied in current commercial batteries due to their outstanding ionic conductivity and structural stability, which may also hold the key for the cathodes of multivalent batteries. Therefore, combining database screening and density functional theory computations, we evaluated the layered compounds in Materials Project database by theoretical capacity, thermodynamic stability, experimental availability, voltage, volume variation, electronic conductivity, and ionic migration barrier and achieved over 20 kinds of layered cathode materials for multivalent batteries. Through Mg ion substitution for Ca sites, we further achieved several kinds of cathode materials for Mg-ion batteries with ideal stability, voltage, and ion diffusion barriers. We hope the methodology and screened materials could promote the development of multivalent ion batteries.

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.

Catalyst discovery through megalibraries of nanomaterials

The nanomaterial landscape is so vast that a high-throughput combinatorial approach is required to understand structure-function relationships. To address this challenge, an approach for the synthesis and screening of megalibraries of unique nanoscale features (>10,000,000) with tailorable location, size, and composition has been developed. Polymer pen lithography, a parallel lithographic technique, is combined with an ink spray-coating method to create pen arrays, where each pen has a different but deliberately chosen quantity and composition of ink. With this technique, gradients of Au-Cu bimetallic nanoparticles have been synthesized and then screened for activity by in situ Raman spectroscopy with respect to single-walled carbon nanotube (SWNT) growth. Au₃Cu, a composition not previously known to catalyze SWNT growth, has been identified as the most active composition.

Single-Layered Microfluidic Network-Based Combinatorial Dilution for Standard Simplex Lattice Design

In this paper, we presented a straightforward strategy to generate 15 combinations of three samples based on an experimental simplex lattice design using a single-layer microfluidic network. First, we investigated the performances of the plain structural and the groove structural combinatorial devices by computational simulation (CFD-ACE+). The simulated output concentrations were extremely close to the desirable values within an absolute error of less than 1%. Based on the simulated designs, polydimethylsiloxane (PDMS) devices were fabricated with soft lithography and tested with fluorescent dye (sodium salt). The mixing results for 15 combinations showed good performance, with an absolute error of less than 4%. We also investigated two liquid handling methods (bottom⁻up and top⁻down) for high-throughput screening and assay. The liquid-handling methods were successfully accomplished by adding the systematic structured groove sets on the mixing channels.

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions.

Accelerating High-Throughput Screening for Structural Materials with Production Management Methods

High-throughput screenings are widely accepted for pharmaceutical developments for new substances and the development of new drugs with required characteristics by evolutionary studies. Current research projects transfer this principle of high-throughput testing to the development of metallic materials. In addition to new generating and testing methods, these types of high-throughput systems need a logistical control and handling method to reduce throughput time to get test results faster. Instead of the direct material flow found in classical high-throughput screenings, these systems have a very complex structure of material flow. The result is a highly dynamic system that includes short-term changes such as rerun stations, partial tests, and temporarily paced sequences between working systems. This paper presents a framework that divides the actions for system acceleration into three main sections. First, methods for special applications in high-throughput systems are designed or adapted to speed up the generation, treatment, and testing processes. Second, methods are needed to process trial plans and to control test orders, which can efficiently reduce waiting times. The third part of the framework describes procedures for handling samples. This reduces non-productive times and reduces order processing in individual lots.