Abstract The screening of novel materials with good performance and the modelling of quantitative structure-activity relationships (QSARs), among other issues, are hot topics in the field of materials science. Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations. Thus, it is imperative to develop a new method of accelerating the discovery and design process for novel materials. Recently, materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy. In this review, we first outline the typical mode of and basic procedures for applying machine learning in materials science, and we classify and compare the main algorithms. Then, the current research status is reviewed with regard to applications of machine learning in material property prediction, in new materials discovery and for other purposes. Finally, we discuss problems related to machine learning in materials science, propose possible solutions, and forecast potential directions of future research. By directly combining computational studies with experiments, we hope to provide insight into the parameters that affect the properties of materials, thereby enabling more efficient and target-oriented research on materials discovery and design.
Abstract The elimination of lead in piezoelectric applications remains challenging. Since the advances in the piezoelectricity were found in the perovskite family in 2000, studies into lead-free piezoelectric materials have grown exponentially in the fields of condensed matter physics and materials science. In this review, we highlighted the compelling physical properties of lead-free piezoelectric perovskite materials and summarized their state-of-the-art progress, with an emphasis on recent advances in the piezoelectric effect. We mainly introduced the unique advances in lead-free perovskites piezoelectric bulk materials, along with the descriptions of phase boundaries, domain configurations, and piezoelectric effects, and then the main physical mechanisms of high piezoelectricity were summarized. In particular, the applications of lead-free materials were also introduced and evaluated. Finally, challenge and perspective are featured on the basis of their current developments. This review provides an overview of the development of lead-free piezoelectric perovskite materials in the past fifteen years along with future prospects, which may inspire material design toward practical applications based on their unique properties.
A multifaceted future for wind power Modern wind turbines already represent a tightly optimized confluence of materials science and aerodynamic engineering. Veers et al. review the challenges and opportunities for further expanding this technology, with an emphasis on the need for interdisciplinary collaboration. They highlight the need to better understand atmospheric physics in the regions where taller turbines will operate as well as the materials constraints associated with the scale-up. The mutual interaction of turbine sites with one another and with the evolving features of the overall electricity grid will furthermore necessitate a systems approach to future development. Science, this issue p. eaau2027 BACKGROUND A growing global population and an increasing demand for energy services are expected to result in substantially greater deployment of clean energy sources. Wind energy is already playing a role as a mainstream source of electricity, driven by decades of scientific discovery and technology development. Additional research and exploration of design options are needed to drive innovation to meet future demand and functionality. The growing scale and deployment expansion will, however, push the technology into areas of both scientific and engineering uncertainty. This Review explores grand challenges in wind energy research that must be addressed to enable wind energy to supply one-third to one-half, or even more, of the world’s electricity needs. ADVANCES Drawing from a recent international workshop, we identify three grand challenges in wind energy research that require further progress from the scientific community: (i) improved understanding of the physics of atmospheric flow in the critical zone of wind power plant operation, (ii) materials and system dynamics of individual wind turbines, and (iii) optimization and control of fleets of wind plants comprising hundreds of individual generators working synergistically within the larger electric grid system. These grand challenges are interrelated, so progress in each domain must build on concurrent advances in the other two. Characterizing the wind power plant operating zone in the atmosphere will be essential to designing the next generation of even-larger wind turbines and achieving dynamic control of the machines. Enhanced forecasting of the nature of the atmospheric inflow will subsequently enable control of the plant in the manner necessary for grid support. These wind energy science challenges bridge previously separable geospatial and temporal scales that extend from the physics of the atmosphere to flexible aeroelastic and mechanical systems more than 200 m in diameter and, ultimately, to the electrical integration with and support for a continent-sized grid system. OUTLOOK Meeting the grand research challenges in wind energy science will enable the wind power plant of the future to supply many of the anticipated electricity system needs at a low cost. The interdependence of the grand challenges requires expansion of integrated and cross-disciplinary research efforts. Methods for handling and streamlining exchange of vast quantities of information across many disciplines (both experimental and computational) will also be crucial to enabling successful integrated research. Moreover, research in fields related to computational and data science will support the research community in seeking to further integrate models and data across scales and disciplines. The cascade of scales underlying wind energy scientific grand challenges. Length scales from weather systems at a global level down the boundary layer of a wind turbine airfoil and time scales from seasonal fluctuations in weather to subsecond dynamic control and balancing of electrical generation and demand must be understood and managed. ILLUSTRATION: JOSH BAUER AND BESIKI KAZAISHVILI, NREL Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.
An autonomous laboratory for thin film discovery is used to optimize the doping and annealing of organic semiconductors. Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.
With the continuous development and progress of materials science, increasingly more attention has been paid to the new technology of powder synthesis and material preparation. The hydrothermal method is a promising liquid phase preparation technology that has developed rapidly during recent years. It is widely used in many fields, such as the piezoelectric, ferroelectric, ceramic powder, and oxide film fields. The hydrothermal method has resulted in many new methods during the long-term research process, such as adding other force fields to the hydrothermal condition reaction system. These force fields mainly include direct current, electric, magnetic (autoclaves composed of non-ferroelectric materials), and microwave fields. Among them, the microwave hydrothermal method, as an extension of the hydrothermal reaction, cleverly uses the microwave temperature to compensate for the lack of temperature in the hydrothermal method, allowing better practical application. This paper reviews the development of the hydrothermal and microwave hydrothermal methods, introduces their reaction mechanisms, and focuses on the practical application of the two methods.
Human immune system acts as a pivotal role in the tissue homeostasis and disease progression. Immunomodulatory biomaterials that can manipulate innate immunity and adaptive immunity hold great promise for a broad range of prophylactic and therapeutic purposes. This review is focused on the design strategies and principles of immunomodulatory biomaterials from the standpoint of materials science to regulate macrophage fate, such as activation, polarization, adhesion, migration, proliferation, and secretion. It offers a comprehensive survey and discussion on the tunability of material designs regarding physical, chemical, biological, and dynamic cues for modulating macrophage immune response. The range of such tailorable cues encompasses surface properties, surface topography, materials mechanics, materials composition, and materials dynamics. The representative immunoengineering applications selected herein demonstrate how macrophage‐immunomodulating biomaterials are being exploited for cancer immunotherapy, infection immunotherapy, tissue regeneration, inflammation resolution, and vaccination. A perspective on the future research directions of immunoregulatory biomaterials is also provided.
James R. Damewood, Jessica Karaguesian, Jaclyn R. Lunger
et al.
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation. Expected final online publication date for the Annual Review of Materials Research, Volume 53 is July 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Abstract Liquid-liquid phase separation plays an important role in many natural and technological processes. Herein, we implement lateral microphase separation at the surface of oil micro-droplets suspended in water to prepare a range of discrete floating protein/polymer continuous two-dimensional (2D) heterostructures with variable interfacial domain structures and dynamics. We show that gel-like domains of bovine serum albumin (BSA) co-exist with fluid-like polyvinyl alcohol (PVA) regions at the oil droplet surface to produce floating heterostructures comprising a 2D phase-separated protein mesh or an array of discrete mobile protein rafts depending on the conditions employed. Enzymes are embedded in the discontinuous BSA domains to produce droplet-supported microphase-separated 2D reaction scaffolds that can be tuned for interfacial catalysis. Taken together, our work has general implications for the structural and functional augmentation of oil droplet interfaces and contributes to the surface engineering and functionality of droplet-based micro-reactors.
Shailendra P. Joshi, Ashley Bucsek, Darren C. Pagan
et al.
The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length & time scales in the mesoscale between atomistic & continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, several gaps remain in this framework as it relates to advanced structural materials:(1) limited availability & access to high-fidelity experimental & computational datasets, (2) lack of co-design of experiments & simulation aimed at computational model validation,(3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, & (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation & cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic. The proposal is to create a hub for Mesoscale Experimentation and Simulation co-Operation (hMESO)-that will (I) provide curation and sharing of models, data, & codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, & (III) provide a platform for education & workforce development. It will engage experimental & computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, & large-scale cyberinfrastructure initiatives.
The integration of artificial intelligence into various domains is rapidly increasing, with Large Language Models (LLMs) becoming more prevalent in numerous applications. This work is included in an overall project which aims to train an LLM specifically in the field of materials science. To assess the impact of this specialized training, it is essential to establish the baseline performance of existing LLMs in materials science. In this study, we evaluated 15 different LLMs using the MaScQA question answering (Q&A) benchmark. This benchmark comprises questions from the Graduate Aptitude Test in Engineering (GATE), tailored to test models' capabilities in answering questions related to materials science and metallurgical engineering. Our results indicate that closed-source LLMs, such as Claude-3.5-Sonnet and GPT-4, perform the best with an overall accuracy of ~84%, while the open-source models, Llama3-70b and Phi3-14b, top at ~56% and ~43%, respectively. These findings provide a baseline for the raw capabilities of LLMs on Q&A tasks applied to materials science, and emphasize the substantial improvement that could be brought to open-source models via prompt engineering and fine-tuning strategies. We anticipate that this work could push the adoption of LLMs as valuable assistants in materials science, demonstrating their utility in this specialized domain and related sub-domains.
Nawaf Alampara, Mara Schilling-Wilhelmi, Kevin Maik Jablonka
Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning systems increasingly transform scientific fields, the question of how to effectively evaluate these systems becomes crucial for ensuring reliable progress. In this review, we examine the current state and future directions of evaluation frameworks for machine learning in science. We organize the review around a broadly applicable framework for evaluating machine learning systems through the lens of statistical measurement theory, using materials science as our primary context for examples and case studies. We identify key challenges common across machine learning evaluation such as construct validity, data quality issues, metric design limitations, and benchmark maintenance problems that can lead to phantom progress when evaluation frameworks fail to capture real-world performance needs. By examining both traditional benchmarks and emerging evaluation approaches, we demonstrate how evaluation choices fundamentally shape not only our measurements but also research priorities and scientific progress. These findings reveal the critical need for transparency in evaluation design and reporting, leading us to propose evaluation cards as a structured approach to documenting measurement choices and limitations. Our work highlights the importance of developing a more diverse toolbox of evaluation techniques for machine learning in materials science, while offering insights that can inform evaluation practices in other scientific domains where similar challenges exist.
Daniel Apai, Rory Barnes, Matthew M. Murphy
et al.
The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitative Habitability Science Working Group. We reviewed past studies of habitability, but find that the lack of a universally valid definition of life prohibits a universally applicable definition of habitability. A more nuanced approach is needed. We introduce a quantitative habitability assessment framework (QHF) that enables self-consistent, probabilistic assessment of the compatibility of two models: First, a habitat model, which describes the probability distributions of key conditions in the habitat. Second, a viability model, which describes the probability that a metabolism is viable given a set of environmental conditions. We provide an open-source implementation of this framework and four examples as a proof of concept: (a) Comparison of two exoplanets for observational target prioritization; (b) Interpretation of atmospheric O2 detection in two exoplanets; (c) Subsurface habitability of Mars; and (d) Ocean habitability in Europa. These examples demonstrate that our framework can self-consistently inform astrobiology research over a broad range of questions. The proposed framework is modular so that future work can expand the range and complexity of models available, both for habitats and for metabolisms.
Kaori Sánchez-Carrillo, David Quintanar-Guerrero, Miguel José-Yacamán
et al.
L-lysine functionalized gold nanoparticles (AuNPs-Lys) have been widely used for the detection of worldwide interest analytes. In this work, a colorimetric assay for the detection of the carcinogen aflatoxin B1 (AFB1) based on the aggregation of AuNPs-Lys in the presence of copper ions was developed. For this purpose, AuNPs were synthesized in citrate aqueous solution, functionalized, and further characterized by UV–Vis, fluorescence, Fourier transform infrared spectroscopy (FTIR), nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), and transmission electron microscopy (TEM). In general, AuNPS-Lys (~2.73 × 1011 particles) offered a clear colorimetric response in the presence of AFB1 and Cu2+ ions showing linearity in the range of 6.25 to 200 ng AFB1/mL, with a detection limit of 4.18 ng AFB1/mL via photometric inspection. Moreover, the performance of the proposed methodology was tested using the 991.31 AOAC official procedure based on monoclonal antibodies in maize samples artificially contaminated with AFB1. There was a good agreement between the measured AFB1 concentrations in both assays, the average recoveries for the colorimetric and immunoaffinity assays were between 91.2–98.4% and 96.0–99.2%, respectively. These results indicated that the colorimetric assay could be used as a rapid, eco-friendly, and cost-effective platform for the quantification of AFB1 in maize-based products.
With the advent of self-driving labs promising to synthesize large numbers of new materials, new automated tools are required for checking potential duplicates in existing structural databases before a material can be claimed as novel. To avoid duplication, we rigorously define the novelty metric of any periodic material as the smallest distance to its nearest neighbor among already known materials. Using ultra-fast structural invariants, all such nearest neighbors can be found within seconds on a typical computer even if a given crystal is disguised by changing a unit cell, perturbing atoms, or replacing chemical elements. This real-time novelty check is demonstrated by finding near-duplicates of the 43 materials produced by Berkeley's A-lab in the world's largest collections of inorganic structures, the Inorganic Crystal Structure Database and the Materials Project. To help future self-driving labs successfully identify novel materials, we propose navigation maps of the materials space where any new structure can be quickly located by its invariant descriptors similar to a geographic location on Earth.
Davi M Fébba, Kingsley Egbo, William A. Callahan
et al.
Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these advancements, their application is constrained to labs with automated instruments and control software, leaving much of materials science reliant on manual processes. Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit using ChatGPT-4. Through iterative refinement, we achieved effective instrument management with minimal human intervention. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements. Finally, we integrated this AI-crafted instrument control software with a high-performance stochastic optimization algorithm to facilitate rapid and automated extraction of electronic device parameters related to semiconductor charge transport mechanisms from current-voltage (IV) measurement data. This integration resulted in a comprehensive open-source toolkit for semiconductor device characterization and analysis using IV curve measurements. We demonstrate the application of these tools by acquiring, analyzing, and parameterizing IV data from a Pt/Cr$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$ heterojunction diode, a novel stack for high-power and high-temperature electronic devices. This approach underscores the powerful synergy between LLMs and the development of instruments for scientific inquiry, showcasing a path for further acceleration in materials science.