Machine learning-accelerated design and synthesis of polyelemental heterostructures

Synthesis of Sulfonated Phenylsilsesquioxanes Guided by Machine Learning

Sulfonated octaphenylsilsesquioxane (SPOSS) has garnered significant interest due to its unique structural properties of containing the -SO3H group and its wide range of applications. This study introduces a novel approach to the synthesis of SPOSS, leveraging machine learning algorithms to explore new recipes and achieve higher -SO3H functionality. The focus was on synthesizing SPOSS with 2, 4, 6, and 8-SO3H functional groups on the phenyl group, marked as SPOSS-2, SPOSS-4, SPOSS-6, and SPOSS-8, respectively. The successful synthesis of SPOSS-8 was achieved by 5 training outputs based on the recipes of 21 sets of low-functionality (<4) SPOSS. The structure of SPOSS was confirmed using Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and time-of-flight mass spectrometry (MALDI-TOF MS). Machine learning analysis revealed that K2SO4 is an important additive to improve the functionality of SPOSS. A synthetic mechanism was proposed and validated that K2SO4 participated in the reaction to generate sulfur trioxide (SO3), a sulfonating agent with high reactivity. SPOSS shows thermal stability superior to octaphenylsilsesquioxane (OPS) according to thermogravimetric analysis (TGA) and TG-FTIR.

Real-Time Observation for MoS2 Growth Kinetics and Mechanism Promoted by the Na Droplet

While the molten salt-catalyzed chemical vapor deposition (CVD) technique is recognized for its effectiveness in producing large-area transition metal chalcogenides, understanding their growth mechanisms involving alkali metals remains a challenge. Here, we investigate the kinetics and mechanism of sodium-catalyzed molybdenum disulfide (MoS2) growth and etching through image analysis conducted using an integrated CVD microscope. Sodium droplets, agglomerated via the thermal decomposition of the sodium cholate dispersant, catalyze the precipitation of supersaturated MoS2 laminates and induce growth despite fragmentation during this process. Triangular MoS2 crystals display a distinct self-exhausting exponential behavior and slow growth of thermodynamically favorable crystallographic faces, exhibiting a sulfur-dominant pressure. The growth and etching processes are facilitated by the scooting of sodium droplets along grain edges, displaying comparable rates. Leveraging these kinetics makes it possible to engineer atypical MoS2 shapes. This combined microscope not only enhances the understanding of growth mechanisms but also contributes to the facile development of next-generation nanomaterials.

Effect of composition and architecture on the thermodynamic behavior of AuCu nanoparticles

The chemical and physical properties of nanomaterials ultimately rely on their crystal structures, chemical compositions and distributions. In this paper, a series of AuCu bimetallic nanoparticles with well-defined architectures and variable compositions has been addressed to explore their thermal stability and thermally driven behavior by molecular dynamics simulations. By combination of energy and Lindemann criteria, the solid-liquid transition and its critical temperature were accurately identified. Meanwhile, atomic diffusion, bond order, and particle morphology were examined to shed light on thermodynamic evolution of the particles. Our results reveal that composition-dependent melting point of AuCu nanoparticles significantly departs from the Vegard's law prediction. Especially, chemically disordered (ordered) alloy nanoparticles exhibited markedly low (high) melting points in comparison with their unary counterparts, which should be attributed to enhancing (decreasing) atomic diffusivity in alloys. Furthermore, core-shell structures and heterostructures demonstrated a mode transition between the ordinary melting and the two-stage melting with varying Au content. AuCu alloyed nanoparticles presented the evolution tendency of chemical ordering from disorder to order before melting and then to disorder during melting. Additionally, as the temperature increases, the shape transformation was observed in AuCu nanoparticles with heterostructure or L10 structure owing to the difference in thermal expansion coefficients of elements and/or of crystalline orientations. Our findings advance the fundamental understanding on thermodynamic behavior and stability of metallic nanoparticles, offering theoretical insights for design and application of nanosized particles with tunable properties.

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Strain and Electric Field Engineering of G-ZnO/SnXY (X, Y = S, Se) S-Scheme Heterostructures for Photocatalyst and Electronic Device Applications: A Hybrid DFT Calculation

Using hybrid density functional theory calculations, we systematically study the biaxial strain and electric field modulated electronic properties of g-ZnO/SnS2, g-ZnO/SnSe2, and g-ZnO/SnSSe S-scheme van der Waals heterostructures (vdWHs). g-ZnO/SnS2 and g-ZnO/SnSSe are found to be promising photocatalysts for water splitting with high solar-to-hydrogen efficiencies, even under acidic, alkaline, and high-stress conditions. The strain effect on the bandgaps of g-ZnO/SnXY is explained in detail according to the correlation between geometry structure and orbital hybridization of SnXY, which could help understand the strain-induced band structure evolutions in other SnXY (X, Y = S, Se)-based vdWHs. It is surprising that under an external electric field, g-ZnO/SnS2, g-ZnO/SnSe2, and g-ZnO/SnSSe can offer the occupied nearly free-electron (NFE) states. In many materials, NFE states are usually unoccupied and is not conducive to the charge transport. The NFE state in g-ZnO/SnSe2 is the most sensitive to the electric field and might be promising electron transport channel in nanoelectronic devices. g-ZnO/SnSe2 might also have application potential in gas sensors and high-temperature superconductors.

Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

Physical and chemical parameters determining the formation of gold-sp metal (Al, Ga, In, and Pb) nanoalloys

Alloying is a key step towards the fabrication of advanced and unique nanomaterials demanded by the next generation of nanotechnology solutions. In particular, the alloys of Au with the sp-metals are expected to have several appealing plasmonic and electronic properties for a wide range of applications in optics, catalysis, nanomedicine, sensing and quantum devices. However, little is known about the thermodynamic and synthetic factors leading to the successful alloying of Au and sp-metals at the nanoscale. In this work, Au-M nanoalloys, with M = Al, Ga, In, or Pb, have been synthesized by a green and single step laser ablation in liquid (LAL) approach in two environments (pure ethanol and anhydrous acetone). To delve deeper into the key parameters leading to successful alloying under the typical operating conditions of LAL, a multiparametric analysis was performed considering the mixing enthalpy from DFT calculations and other alloying descriptors such as the Hume-Rothery parameters. The results showed that the dominant factors for alloying change dramatically with the oxidative ability of the synthesis environment. In this way, the tendency of the four sp metals to alloy with gold was accurately predicted (R2 > 0.99) using only two and three parameters in anhydrous and non-anhydrous environments, respectively. These results are important to produce nanoalloys using LAL and other physical methods because they contribute to the understanding of factors leading to element mixing at the nanoscale under real synthetic conditions, which is crucial for guiding the realization of next-generation multifunctional metallic nanostructures.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

Topological learning in multiclass data sets

We specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multiclass data set. As a by-product, a topological classifier is defined that uses an open subcovering of the data set. This subcovering can be used to construct a simplicial complex whose topological features (e.g., Betti numbers) provide information about the classification problem. We use these topological constructs to study the impact of topological complexity on learning in feedforward deep neural networks (DNNs). We hypothesize that topological complexity is negatively correlated with the ability of a fully connected feedforward deep neural network to learn to classify data correctly. We evaluate our topological classification algorithm on multiple constructed and open-source data sets. We also validate our hypothesis regarding the relationship between topological complexity and learning in DNN's on multiple data sets.

Accurate prediction of the optical properties of nanoalloys with both plasmonic and magnetic elements

The alloying process plays a pivotal role in the development of advanced multifunctional plasmonic materials within the realm of modern nanotechnology. However, accurate in silico predictions are only available for metal clusters of just a few nanometers, while the support of modelling is required to navigate the broad landscape of components, structures and stoichiometry of plasmonic nanoalloys regardless of their size. Here we report on the accurate calculation and conceptual understanding of the optical properties of metastable alloys of both plasmonic (Au) and magnetic (Co) elements obtained through a tailored laser synthesis procedure. The model is based on the density functional theory calculation of the dielectric function with the Hubbard-corrected local density approximation, the correction for intrinsic size effects and use of classical electrodynamics. This approach is built to manage critical aspects in modelling of real samples, as spin polarization effects due to magnetic elements, short-range order variability, and size heterogeneity. The method provides accurate results also for other magnetic-plasmonic (Au-Fe) and typical plasmonic (Au-Ag) nanoalloys, thus being available for the investigation of several other nanomaterials waiting for assessment and exploitation in fundamental sectors such as quantum optics, magneto-optics, magneto-plasmonics, metamaterials, chiral catalysis and plasmon-enhanced catalysis.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs

We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation.

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core-Shell Nanospheres

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrOx core-shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33-1/9.09, and Ce/Zr from 1/0.22-1/0.35), which directly results in a lower conversion temperature (T50 approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.

Accelerating the prediction of stable materials with machine learning

Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials. In this Review, we summarize the most recent advancements in applying ML methodologies in predicting materials stability, focusing particularly on predictions of zero- and finite-temperature stability. We also highlight the need for more ML development in predictions of other thermodynamic knobs, such as pressure and surface/interfacial energy, which practically impact materials stability.

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.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Rethinking the pathway to sustainable fire retardants

Flame retardants are currently used in a wide range of industry sectors for saving lives and property by mitigating fire hazards. The growing fire safety requirements for materials boost an escalating demand for consumption of fire retardants. This has significantly driven both the industry and scientific community to pursue sustainable fire retardants, but what makes a sustainable flame retardant? Here an overview of recent advances in sustainable flame retardants is offered, and their renewable raw materials, green synthesis and life cycle assessments are highlighted. A discussion on key challenges that hinder the innovation of fire retardants and design principles for creating truly sustainable yet cost-effective fire retardants are also presented. This short work is expected to help drive the development of sustainable, cost-effective fire retardants, and expedite the creation of a more sustainable and safer society.

Molecular Thin Films Enable the Synthesis and Screening of Nanoparticle Megalibraries Containing Millions of Catalysts

Megalibraries are centimeter-scale chips containing millions of materials synthesized in parallel using scanning probe lithography. As such, they stand to accelerate how materials are discovered for applications spanning catalysis, optics, and more. However, a long-standing challenge is the availability of substrates compatible with megalibrary synthesis, which limits the structural and functional design space that can be explored. To address this challenge, thermally removable polystyrene films were developed as universal substrate coatings that decouple lithography-enabled nanoparticle synthesis from the underlying substrate chemistry, thus providing consistent lithography parameters on diverse substrates. Multi-spray inking of the scanning probe arrays with polymer solutions containing metal salts allows patterning of >56 million nanoreactors designed to vary in composition and size. These are subsequently converted to inorganic nanoparticles via reductive thermal annealing, which also removes the polystyrene to deposit the megalibrary. Megalibraries with mono-, bi-, and trimetallic materials were synthesized, and nanoparticle size was controlled between 5 and 35 nm by modulating the lithography speed. Importantly, the polystyrene coating can be used on conventional substrates like Si/SiO_x, as well as substrates typically more difficult to pattern on, such as glassy carbon, diamond, TiO_2, BN, W, or SiC. Finally, high-throughput materials discovery is performed in the context of photocatalytic degradation of organic pollutants using Au-Pd-Cu nanoparticle megalibraries on TiO₂ substrates with 2,250,000 unique composition/size combinations. The megalibrary was screened within 1 h by developing fluorescent thin-film coatings on top of the megalibrary as proxies for catalytic turnover, revealing Au_0.53Pd_0.38Cu_0.09-TiO₂ as the most active photocatalyst composition.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction

Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO₂RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO₂RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO₂RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO₂RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO₂RR.

Conversion of Classical Light Emission from a Nanoparticle-Strained WSe2 Monolayer into Quantum Light Emission via Electron Beam Irradiation

Solid-state single photon emitters (SPEs) within atomically thin transition metal dichalcogenides (TMDs) have recently attracted interest as scalable quantum light sources for quantum photonic technologies. Among TMDs, WSe₂ monolayers (MLs) are promising for the deterministic fabrication and engineering of SPEs using local strain fields. The ability to reliably produce isolatable SPEs in WSe₂ is currently impeded by the presence of numerous spectrally overlapping states that occur at strained locations. Here nanoparticle (NP) arrays with precisely defined positions and sizes are employed to deterministically create strain fields in WSe₂ MLs, thus enabling the systematic investigation and control of SPE formation. Using this platform, electron beam irradiation at NP-strained locations transforms spectrally overlapped sub-bandgap emission states into isolatable, anti-bunched quantum emitters. The dependence of the emission spectra of WSe₂ MLs as a function of strain magnitude and exposure time to electron beam irradiation is quantified and provides insight into the mechanism for SPE production. Excitons selectively funnel through strongly coupled sub-bandgap states introduced by electron beam irradiation, which suppresses spectrally overlapping emission pathways and leads to measurable anti-bunched behavior. The findings provide a strategy to generate isolatable SPEs in 2D materials with a well-defined energy range.

Structural evolution under physical and chemical stimuli of metastable Au-Fe nanoalloys obtained by laser ablation in liquid

Metastable alloy nanoparticles are investigated for their variety of appealing properties exploitable for photonics, magnetism, catalysis and nanobiotechnology. Notably, nanophases out of thermodynamic equilibrium feature a complex "ultrastructure" leading to a dynamic evolution of composition and atomic arrangement in response to physical-chemical stimuli. In this manuscript, metastable Au-Fe alloy nanoparticles were produced by laser ablation in liquid, an emerging versatile synthetic approach for freezing multielement nanosystems in non-equilibrium conditions. The Au-Fe nanoalloys were characterized through electron microscopy, elemental analysis, X-ray diffraction and Mössbauer spectroscopy. The dynamics of the structure of the Au-Fe system was tracked at high temperature under vacuum and atmospheric conditions, evidencing the intrinsic transformative nature of the metastable nanoalloy produced by laser ablation in liquid. This dynamic structure is relevant to possible application in several fields, from photocatalysis to nanomedicine, as demonstrated through an experiment of magnetic resonance imaging in biological fluids.

Materials informatics for developing new restorative dental materials: A narrative review

Materials informatics involves the application of computational methodologies to process and interpret scientific and engineering data concerning materials. Although this concept has been well established in the fields of biology, drug discovery, and classic materials research, its application in the field of dental materials is still in its infancy. This narrative review comprehensively summarizes the advantages, limitations, and future perspectives of materials informatics from 2003 to 2022 for exploring the optimum compositions in developing new materials using artificial intelligence. The findings indicate that materials informatics, which is a recognized and established concept in the materials science field, will accelerate the process of restorative materials development and contribute to produce new insights into dental materials research.

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.

Recent Developments in Plasmonic Alloy Nanoparticles: Synthesis, Modelling, Properties and Applications

Despite the traditional plasmonic materials are counted on one hand, there are a lot of possible combinations leading to alloys with other elements of the periodic table, in particular those renowned for magnetic or catalytic properties. It is not a surprise, therefore, that nanoalloys are considered for their ability to open new perspectives in the panorama of plasmonics, representing a leading research sector nowadays. This is demonstrated by a long list of studies describing multiple applications of nanoalloys in photonics, photocatalysis, sensing and magneto-optics, where plasmons are combined with other physical and chemical phenomena. In some remarkable cases, the amplification of the conventional properties and even new effects emerged. However, this field is still in its infancy and several challenges must be overcome, starting with the synthesis (control of composition, crystalline order, size, processability, achievement of metastable phases and disordered compounds) as well as the modelling of the structure and properties (accuracy of results, reliability of structural predictions, description of disordered phases, evolution over time) of nanoalloys. To foster the research on plasmonic nanoalloys, here we provide an overview of the most recent results and developments in the field, organized according to synthetic strategies, modelling approaches, dominant properties and reported applications. Considering the several plasmonic nanoalloys under development as well as the large number of those still awaiting synthesis, modelling, properties assessment and technological exploitation, we expect a great impact on the forthcoming solutions for sustainability, ultrasensitive and accurate detection, information processing and many other fields.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Site-selective modification of metallic nanoparticles

Surface patterning of inorganic nanoparticles through site-selective functionalization with mixed-ligand shells or additional inorganic material is an intriguing approach to developing tailored nanomaterials with potentially novel and/or multifunctional properties. The unique physicochemical properties of such nanoparticles are likely to impact their behavior and functionality in biological environments, catalytic systems, and electronics applications, making it vital to understand how we can achieve and characterize such regioselective surface functionalization. This Feature Article will review methods by which chemists have selectively modified the surface of colloidal nanoparticles to obtain both two-sided Janus particles and nanoparticles with patchy or stripey mixed-ligand shells, as well as to achieve directed growth of mesoporous oxide materials and metals onto existing nanoparticle templates in a spatially and compositionally controlled manner. The advantages and drawbacks of various techniques used to characterize the regiospecificity of anisotropic surface coatings are discussed, as well as areas for improvement, and future directions for this field.

Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer

ABSTRACT

In the past two decades, the emergence of nanomaterials for biomedical applications has shown tremendous promise for changing the paradigm of all aspects of disease management. Nanomaterials are particularly attractive for being a modularly tunable system; with the ability to add functionality for early diagnostics, drug delivery, therapy, treatment and monitoring of patient response. In this review, a survey of the landscape of different classes of nanomaterials being developed for applications in diagnostics and imaging, as well as for the delivery of prophylactic vaccines and therapeutics such as small molecules and biologic drugs is undertaken; with a particular focus on COVID-19 diagnostics and vaccination. Work involving bio-templated nanomaterials for high-resolution imaging applications for early cancer detection, as well as for optimal cancer treatment efficacy, is discussed. The main challenges which need to be overcome from the standpoint of effective delivery and mitigating toxicity concerns are investigated. Subsequently, a section is included with resources for researchers and practitioners in nanomedicine, to help tailor their designs and formulations from a clinical perspective. Finally, three key areas for researchers to focus on are highlighted; to accelerate the development and clinical translation of these nanomaterials, thereby unleashing the true potential of nanomedicine in healthcare.