Combinatorial and high-throughput screening of materials libraries: review of state of the art

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.

Advances in the structural engineering of layered bismuth-based semiconductors for visible light-driven photocatalytic water splitting

Hydrogen production via the photocatalytic water splitting reaction on semiconductors presents a promising avenue to directly achieve solar energy conversion and storage. Bismuth-based semiconductors with layered structures, a hierarchical arrangement of components stacked in the form of two-dimensional extended layers where the atoms within each layer are typically strongly bonded, while the interactions between the layers are relatively weak, have emerged as an important series of photocatalyst candidates. In this review, we focus on the new emerging layered bismuth-based semiconductors with structures in Sillén, Aurivillius, Sillén-Aurivillius and bismuth chromate systems primarily employed in the photocatalytic water splitting reaction. From a crystal structure-oriented view, we delve into discussions on how the component and unit of a crystal structure influence the intrinsic properties, including light absorption and photogenerated charge transfer and separation, of materials as well as the corresponding photocatalytic performance of the water splitting reaction. The strategies for modulating the ferroelectricity and surface modification of these layered bismuth-based semiconductors are also involved. We also discuss the limitations of these materials accompanied by a forward-looking perspective, and we hope to provide some insights from the view of rational material design and engineering for the fabrication of high-efficiency photocatalytic water splitting systems.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

Unleashing the Power of PET-RAFT Polymerization: Journey from Porphyrin-Based Photocatalysts to Combinatorial Technologies and Advanced Bioapplications

The emergence of photoinduced energy/electron transfer-reversible addition-fragmentation chain transfer polymerization (PET-RAFT) not only revolutionized the field of photopolymerization but also accelerated the development of porphyrin-based photocatalysts and their analogues. The continual expansion of the monomer family compatible with PET-RAFT polymerization enhances the range of light radiation that can be harnessed, providing increased flexibility in polymerization processes. Furthermore, the versatility of PET-RAFT polymerization extends beyond its inherent capabilities, enabling its integration with various technologies in diverse fields. This integration holds considerable promise for the advancement of biomaterials with satisfactory bioapplications. As researchers delve deeper into the possibilities afforded by PET-RAFT polymerization, the collaborative efforts of individuals from diverse disciplines will prove invaluable in unleashing its full potential. This Review presents a concise introduction to the fundamental principles of PET-RAFT, outlines the progress in photocatalyst development, highlights its primary applications, and offers insights for future advancements in this technique, paving the way for exciting innovations and applications.

A high throughput synthetic workflow for solid state synthesis of oxides

High-throughput synthetic methods are well-established for chemistries involving liquid- or vapour-phase reagents and have been harnessed to prepare arrays of inorganic materials. The versatile but labour-intensive sub-solidus reaction pathway that is the backbone of the functional and electroceramics materials industries has proved more challenging to automate because of the use of solid-state reagents. We present a high-throughput sub-solidus synthesis workflow that permits rapid screening of oxide chemical space that will accelerate materials discovery by enabling simultaneous expansion of explored compositions and synthetic conditions. This increases throughput by using manual steps where actions are undertaken on multiple, rather than individual, samples which are then further combined with researcher-hands-free automated processes. We exemplify this by extending the BaYxSn1-xO3-x/2 solid solution beyond the reported limit to a previously unreported composition and by exploring the Nb-Al-P-O composition space showing the applicability of the workflow to polyanion-based compositions beyond oxides.

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.

Towards scanning nanostructure X-ray microscopy

This article demonstrates spatial mapping of the local and nanoscale structure of thin film objects using spatially resolved pair distribution function (PDF) analysis of synchrotron X-ray diffraction data. This is exemplified in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid-handling system. A software implementation is presented of the whole protocol, including an approach for automated data acquisition and analysis using the atomic PDF method. The protocol software can handle semi-automated data reduction, normalization and modeling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions, it is possible to build images of different contrast features chosen by the user, giving insights into different aspects of the local structure.

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.

Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment

Machine learning approaches have introduced an urgent need for large datasets of materials properties. However, for mechanical properties, current high-throughput measurement methods typically require complex robotic instrumentation, with enormous capital costs that are inaccessible to most experimentalists. A quantitative high-throughput method using only common lab equipment and consumables with simple fabrication steps is long desired. Here, we present such a technique that can measure bulk mechanical properties in soft materials with a common laboratory centrifuge, multiwell plates, and microparticles. By applying a homogeneous force on the particles embedded inside samples in the multiwell plate using centrifugation, we show that our technique measures the fracture stress of gels with similar accuracy to a rheometer. However, our method has a throughput on the order of 10³ samples per run and is generalizable to virtually all soft material systems. We hope that our method can expedite materials discovery and potentially inspire the future development of additional high-throughput characterization methods.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Insights into Chemically Fueled Supramolecular Polymers

Supramolecular polymerization can be controlled in space and time by chemical fuels. A nonassembled monomer is activated by the fuel and subsequently self-assembles into a polymer. Deactivation of the molecule either in solution or inside the polymer leads to disassembly. Whereas biology has already mastered this approach, fully artificial examples have only appeared in the past decade. Here, we map the available literature examples into four distinct regimes depending on their activation/deactivation rates and the equivalents of deactivating fuel. We present increasingly complex mathematical models, first considering only the chemical activation/deactivation rates (i.e., transient activation) and later including the full details of the isodesmic or cooperative supramolecular processes (i.e., transient self-assembly). We finish by showing that sustained oscillations are possible in chemically fueled cooperative supramolecular polymerization and provide mechanistic insights. We hope our models encourage the quantification of activation, deactivation, assembly, and disassembly kinetics in future studies.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Prediction of dielectric constants of ABO3-type perovskites using machine learning and first-principles calculations

In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO₃-type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic materials were extracted from the Materials Project. The chemical, structural, and physical descriptors were generated and trained using the gradient-boosting-based regressor after feature engineering. The prediction accuracies were 0.83 and 0.67 (R²) and 0.12 and 0.26 (root mean square error) for the electronic and ionic contributions to the dielectric constant, respectively. The constructed surrogate model was then employed to predict the dielectric constants of the ABO₃-type perovskites (216 structures), whose thermodynamic stabilities were satisfactory. The predicted values were validated using DFPT calculations. The constructed database was further used to develop a surrogate model for the prediction of dielectric constants. The final R² prediction accuracies reached 0.79 (electronic) and 0.67 (ionic).

Catalytic Activity Trends of Pyrite Transition Metal Dichalcogenides for Oxygen Reduction and Evolution

Transition metal dichalcogenides (TMDs) have been considered as promising materials for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysis. While there have been numerous studies focusing on layered...

Prospect of making XPS a high-throughput analytical method illustrated for a CuxNi1−xOy combinatorial material library

Combinatorial material science crucially depends on robust, high-throughput characterization methods. While X-ray photoelectron spectroscopy (XPS) may provide detailed information about chemical and electronic properties, it is a time-consuming technique and, therefore, is not viewed as a high-throughput method. Here we present preliminary XPS data of 169 measurement spots on a combinatorial 72 × 72 cm² Cu_xNi_1−xO_y compositional library to explore how characterization and evaluation routines can be optimized to improve throughput in XPS for combinatorial studies. In particular, two quantification approaches are compared. We find that a simple integration (of XPS peak regions) approach is suited for fast evaluation of, in the example system, the [Cu]/([Cu] + [Ni]) ratio. Complementary to that, the time-consuming (XPS peak-) fit approach provides additional insights into chemical speciation and oxidation state changes, without a large deviation of the [Cu]/([Cu] + [Ni]) ratio. This insight suggests exploiting the fast integration approach for ‘real time’ analysis during XPS data collection, paving the way for an ‘on-the-fly’ selection of points of interest (i.e., areas on the sample where sudden composition changes have been identified) for detailed XPS characterization. Together with the envisioned improvements when going from laboratory to synchrotron-based excitation sources, this will shorten the analysis time sufficiently for XPS to become a realistic characterization option for combinatorial material science.

Methods for fast quantification of XPS data of a Cu_xNi_1−xO_y combinatorial material library were evaluated in a step towards high-throughput analysis.

Data-Driven Methods for Accelerating Polymer Design

Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.

Decoding the conductance of disordered nanostructures: a quantum inverse problem

Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the scatters from the conductance spectrum alone, is not an easy task. In the presence of impurities, even advanced techniques of inversion can become particularly challenging. This article extends the applicability of a methodology we proposed capable of extracting composition information about a nanoscale sensing device using the conductance spectrum. The inversion tool decodes the conductance spectrum to yield the concentration and nature of the disorders responsible for conductance fluctuations in the spectra. We present the method for simple one-dimensional systems like an electron gas with randomly distributed delta functions and a linear chain of atoms. We prove the generality and robustness of the method using materials with complex electronic structures like hexagonal boron nitride, graphene nanoribbons, and carbon nanotubes. We also go on to probe distribution of disorders on the sublattice structure of the materials using the proposed inversion tool.

A time-space conversion method for material synthesis research

Research on material synthesis is mostly performed through batch by batch testing with each corresponding to a set of parameters and a reaction time. Concurrent experiments that allow for multiple loadings throughout an inhomogeneous reaction zone provide a way to obtain high-throughput results. Here, a time-space conversion method is proposed. By sequentially passing a number of identical objects through a reaction zone, a significant diversity of reactions in one batch can be achieved depending on the spatial distribution and changes with time of the reaction zone. In particular, when the reaction zone is steady, the evolution of a reaction can be associated with the objects at their corresponding reaction stage. This greatly improves the efficiency and accuracy of research on material synthesis kinetics. This method may initiate a new wave of material synthesis research and accelerate the development of material science.

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High-throughput time-space conversion method by adding a moving rate

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Improving the efficiency and accuracy of research on material synthesis kinetics

A microfluidic-enabled combinatorial formulation and integrated inkjet printing platform for evaluating functionally graded material blends

Sample library preparation is a central step in the process of evaluating materials with the general aim of efficient library formulation while minimizing resource consumption. We demonstrate here the first implementation of a microfluidic-enabled thin film sample library formulation platform with integrated inkjet printing capability for directly patterning these libraries with reduced material wastage. System development and general performance screening protocol for these patterned thin films are described. We study the combinatorial formulation capabilities of this system by focusing on some practical case studies for probing the electrical conductivity in organic, biocompatible and electroactive polymer/additive (PEDOT:PSS/DMSO and PEDOT:PSS/EG) blends. Functionally-graded thin film libraries are prepared by mixing ink components and directly dispensing the processed blends into programmed geometries using the integrated platform. Electrical and morphological characterization of these printed thin film libraries is conducted to validate the formulation efficacy of the platform. Interrogating these printed libraries, we were able to iteratively identify the location of conductivity maxima for the studied blends and corroborate the morphological basis of this enhancement with established theories.

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.

Bio-instructive materials on-demand - combinatorial chemistry of peptoids, foldamers, and beyond

Combinatorial chemistry allows for the rapid synthesis of large compound libraries for high throughput screenings in biology, medicinal chemistry, or materials science. Especially compounds from a highly modular design are interesting for the proper investigation of structure-to-activity relationships. Permutations of building blocks result in many similar but unique compounds. The influence of certain structural features on the entire structure can then be monitored and serve as a starting point for the rational design of potent molecules for various applications. Peptoids, a highly diverse class of bioinspired oligomers, suit perfectly for combinatorial chemistry. Their straightforward synthesis on a solid support using repetitive reaction steps ensures easy handling and high throughput. Applying this modular approach, peptoids are readily accessible, and their interchangeable side-chains allow for various structures. Thus, peptoids can easily be tuned in their solubility, their spatial structure, and, consequently, their applicability in various fields of research. Since their discovery, peptoids have been applied as antimicrobial agents, artificial membranes, molecular transporters, and much more. Studying their three-dimensional structure, various foldamers with fascinating, unique properties were discovered. This non-comprehensive review will state the most interesting discoveries made over the past years and arouse curiosity about what may come.

Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

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.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

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.

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Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED - a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.

Sequence-controlled and sequence-defined polypeptoids via the Ugi reaction: synthesis and sequence-driven properties

Ugi reaction offers opportunities to facilely access unprecedented sequence control and sequence-driven properties in polypeptoids.

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.

Information-Based Design of Polymeric Drug Formulation Additives

Tailor-made copolymers are designed based on a peptide-poly(ethylene glycol) (QFFLFFQ-PEG) conjugate as a blueprint, to solubilize the photosensitizer meta-tetra(hydroxyphenyl)chlorin (m-THPC). The relevant functionalities of the parent peptide-PEG are mimicked by employing monomer pairs that copolymerize in a strictly alternating manner. While styrene (S) or 4-vinylbenzyl-phthalimide (VBP) provide aromatic moieties like Phe, the aliphatic isobutyl side chain of Leu4 is mimicked by maleic anhydride (MA) that reacts after polymerization with isobutylamine to give the isobutylamide-carboxyl functional unit (iBuMA). A set of copolymer-PEG solubilizers is synthesized by controlled radical polymerization, systematically altering the length of the functional segment (DP_n = 2, 4, 6) and the side chain functionalization (iBuMA, iPrMA, MeMA). The m-THPC hosting and release properties of P[S-alt-iBuMA]₆-PEG reached higher payload capacities and more favored release rates than the parent peptide-PEG conjugate. Interestingly, P[S-alt-RMA]_n-PEG mimics the sensitivity of the peptide-PEG solubilizer well, where the exchange of Leu4 residue by Val and Ala significantly reduces the drug loading by 92%. A similar trend is found with P[S-alt-RMA]_n-PEG as the exchange of iBu → iPr → Me reduces the payload capacity up to 78%.

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 Approaches for the Discovery of Supramolecular Organic Cages

The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High-throughput computation-only and experiment-only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high-throughput approaches that have been developed and applied in the discovery of supramolecular organic cages.

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations

Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers, and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape.

Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

We explore LiNiO₂-based cathode materials with two-element substitutions by an ab initio simulation-based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during the charge-discharge cycles; the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all of the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of the crystal structure change.

A novel gradient composition spreading and nanolayer stacking process for combinatorial thin-film materials library fabrication

A novel magnetron sputtering process is proposed to fabricate a combinatorial thin-film materials library with highly precise composition spreading. In order to produce a gradient composition spreading for a specific target, a moving shutter is used to cover the deposition substrate step by step with a fixed step-length. By rotating the substrate and repeating the step-by-step masked deposition with different targets in turn, a heterogeneous precursor structure is obtained with alternate stacking of different material layers, each of which is in a step-by-step wedge-shaped thickness cross section. By controlling the thickness of each layer at the nanometer scale, a multilayer structure is formed to facilitate the interlayer diffusion between different precursor layers. It may also define the boundaries of individual sample pixels, resulting in improved composition spreading resolutions for the prepared materials library. A combinatorial magnetron sputtering system is designed with reciprocating rectangular targets, a narrow slit between the substrate and the target, and a quartz crystal microbalance feedback to control the deposition uniformity, resulting in a variation better than 3% across a 76 × 76 mm substrate. Three individual deposition chambers are designed in an annular distribution with 90° angle between each other. Moreover, a step-by-step moving shutter and a rotating substrate holder are incorporated. Combinatorial materials libraries with more than 10 000 individual compositions could be obtained using this system. A Ti-Zr-Ni ternary alloy library was fabricated for demonstration in which the sheet resistance spreading diagram of the Ti-Zr-Ni library was studied as a function of the composition.

Corrosion-Free EMF Measurements of Zinc-Based Intermetallic Compounds at Ambient Temperature

Material development requires in many cases information about the necessary stability of the materials against oxidation, which is encoded in the chemical activity of the constituting elements. Determination of the chemical activity is tedious, especially for metallic materials at or close to ambient temperature. To determine the chemical activity of Zn at ambient temperature, electromotive force (EMF) measurements on the intermetallic compounds ZnPd, ZnPt and Cu₅Zn₈ within their respective homogeneity range were conducted. The single‐phase nature of the samples was confirmed by powder X‐ray diffraction, light microscopy as well as SEM/EDX analysis. To exclude oxidation, and therefore faulty determination of the electrochemical potentials, a method was developed to conduct the electrochemical measurements under non‐corrosive conditions in inert atmosphere. Corrosion by the electrolyte was avoided using anhydrous dimethylformamide as aprotic solvent. From the EMF the respective intrinsic activities of zinc in the corresponding intermetallic compounds was determined. Measurements on Cu₅Zn₈ and comparison to available data in literature verified the developed method allowing to retrieve thermodynamic data of ZnPd and ZnPt for the first time at ambient temperature. The herein developed and easy‐to‐use methodology is applicable to a wide range of metallic material by choosing appropriate compositions of the electrolyte and has the potential to boost material development.

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.

Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space necessitates an accelerated discovery pipeline that supersedes traditional “trial-and-error” experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications.

A microarray platform designed for high-throughput screening the reaction conditions for the synthesis of micro/nanosized biomedical materials

Materials research usually relies on lengthy and largely trial-and-error methods, high-throughput technology has thereby emerged as an alternative method which is proven to be a simple, rapid, accurate and sensitive technique. Here, we presented a microfluidic platform with a set of 6 × 6 microarray chips for high-throughput synthesis and rapid screening the reaction conditions of biomedical materials. The core design of this platform is to generate concentration gradient inside microarray chips. Considering that calcium phosphates (CaP) are the most important inorganic constituents of biological hard tissues, different phases of calcium phosphates particles were synthesized with various morphogenesis when the reaction conditions such as Ca/P concentration ratio, NaOH concentration were screened using our platform. And this platform is universal and expected to apply to other systems for high-throughput screening and synthesis.

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A microarray platform was designed with high-throughput synthesis and screening functions for biomedical materials.

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Calcium phosphates with different morphologies were synthesized by this platform through generating Ca/P mole ratio gradient and NaOH concentration gradient.

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Scale-up experiments verified the reliability, accuracy and practicality of the designed microarray platform.

Getting Beyond the Toy Domain. Meditations on David Deamer's "Assembling Life"

David Deamer has written another book, Assembling Life, on the origin of life. It is unapologetically polemic, presenting Deamer's view that life originated in fresh water hydrothermal fields on volcanic islands on early Earth, arguing that this provided a unique environment not just for organic chemistry but for the self-assembling structure that drive that chemistry and form the basis of structure in life. It is worth reading, it is an advance in the field, but is it convincing? I argue that the Origin of Life field as a whole is unconvincing, generating results in Toy Domains that cannot be scaled to any real world scenario. I suggest that, by analogy with the history of artificial intelligence and solar astronomy, we need much more scale, and fundamentally new ideas, to take the field forward.

Peptide-Assisted Design of Peptoid Sequences: One Small Step in Structure and Distinct Leaps in Functions

Using peptide sequences for the design of functional peptoids is demonstrated for a peptide-based formulation additive that was specifically tailored to solubilize the photosensitizer meta-tetra(hydroxyphenyl)-chlorin. A set of peptoid-block-poly(ethylene glycol) solubilizers with systematic sequence variations are synthesized to reveal contributions of side-chain sequence and backbone functionalities on drug hosting and release properties. The drug payload sensitively depends on the side-chain patterns, and the best performing peptoid sequence reaches 3-times higher capacity than the corresponding peptide. The peptoid backbone not only acts as a neutral scaffold but also impacts the drug release kinetics compared to the analogues peptide, by reducing the capability to assist drug transfer to blood plasma protein models.