Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic‐structure methods such as density‐functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by “learning” electronic‐structure data, ML‐based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase‐change materials for memory devices; nanoparticle catalysts; and carbon‐based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML‐based interatomic potentials in diverse areas of materials research.
DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.
Abstract Nanocelluloses are natural materials with at least one dimension in the nano-scale. They combine important cellulose properties with the features of nanomaterials and open new horizons for materials science and its applications. The field of nanocellulose materials is subdivided into three domains: biotechnologically produced bacterial nanocellulose hydrogels, mechanically delaminated cellulose nanofibers, and hydrolytically extracted cellulose nanocrystals. This review article describes today’s state regarding the production, structural details, physicochemical properties, and innovative applications of these nanocelluloses. Promising technical applications including gels/foams, thickeners/stabilizers as well as reinforcing agents have been proposed and research from last five years indicates new potential for groundbreaking innovations in the areas of cosmetic products, wound dressings, drug carriers, medical implants, tissue engineering, food and composites. The current state of worldwide commercialization and the challenge of reducing nanocellulose production costs are also discussed.
Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.
This Proceedings volume is dedicated to presenting the results of the EC THERAMIN (Thermal Treatment for Radioactive Waste Minimisation and Hazard Reduction) Project and other recent developments related to thermal treatment of radioactive waste. List of Conference and images are available in this pdf.
There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.MACHINE LEARNING: Dealing with small datasetsMachine learning can be useful for materials prediction if crude estimations of the outcome are integrated in the code. Machine learning has been attracting tremendous attention lately due to its predictive power; evidence suggests it is directly proportional to the size of the available datasets. Machine learning can be useful in predicting new materials and novel properties, but materials sets tend to be smaller and more diverse than other fields. Ying Zhang and Chen Ling from the Toyota Research Institute of North America report that these small datasets affect the freedom of the algorithms and thus limit their predictive capabilities. In order to counterbalance the effect, they suggest introducing in the code crude estimations of the targeted property, obtained by other means.
One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.
The ongoing SARS-CoV-2 pandemic highlights the importance of materials science in providing tools and technologies for antiviral research and treatment development. In this Review, we discuss previous efforts in materials science in developing imaging systems and microfluidic devices for the in-depth and real-time investigation of viral structures and transmission, as well as material platforms for the detection of viruses and the delivery of antiviral drugs and vaccines. We highlight the contribution of materials science to the manufacturing of personal protective equipment and to the design of simple, accurate and low-cost virus-detection devices. We then investigate future possibilities of materials science in antiviral research and treatment development, examining the role of materials in antiviral-drug design, including the importance of synthetic material platforms for organoids and organs-on-a-chip, in drug delivery and vaccination, and for the production of medical equipment. Materials-science-based technologies not only contribute to the ongoing SARS-CoV-2 research efforts but can also provide platforms and tools for the understanding, protection, detection and treatment of future viral diseases. Materials science provides tools and technologies for the protection against viral infections, as well as for the understanding, diagnosis, treatment and prevention of viral diseases. This Review discusses present and future directions in antiviral materials-science research, with a focus on COVID-19.
The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw-text of published articles onto structured database entries that allows for programmatic querying. To this end, we apply text-mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including: inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f1) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex ``meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data and functionality has been made freely available (https://github.com/materialsintelligence/matscholar), and we expect these results to accelerate the pace of future materials science discovery.
More is demanded from ophthalmic treatments using contact lenses, which are currently used by over 125 million people around the world. Improving the material of contact lenses (CLs) is a now rapidly evolving discipline. These materials are developing alongside the advances made in related biomaterials for applications such as drug delivery. Contact lens materials are typically based on polymer- or silicone-hydrogel, with additional manufacturing technologies employed to produce the final lens. These processes are simply not enough to meet the increasing demands from CLs and the ever-increasing number of contact lens (CL) users. This review provides an advanced perspective on contact lens materials, with an emphasis on materials science employed in developing new CLs. The future trends for CL materials are to graft, incapsulate, or modify the classic CL material structure to provide new or improved functionality. In this paper, we discuss some of the fundamental material properties, present an outlook from related emerging biomaterials, and provide viewpoints of precision manufacturing in CL development.
Abstract Microscopic imaging providing the real-space information of matter, plays an important role for understanding the correlations between structure and properties in the field of materials science. For the microscopic images of different kinds of objects at different scales, it is a time-consuming task to retrieve useful information on morphology, size, distribution, intensity etc. Alternatively, deep learning has shown great potential in the applications on complicated systems for its ability of extracting useful information automatically. Recently, researchers have utilized deep learning methods on imaging analysis to identify structures and retrieve the linkage between microstructure and performance. In this review, we summarize the recent progresses of the applications of deep learning analysis on microscopic imaging, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning probe microscopy (SPM). We present sequentially the basic concepts of deep learning methods, the review of the applications on imaging analysis, and our perspective on the future development. Based on the published results, a general workflow of deep learning analysis is put forward.
The skeletal system is an important support structure in the human body, and its homeostatic state is highly relevant to the development of a wide range of orthopaedic diseases. The search for key regulatory factors associated with skeletal development is essential for exploring potential therapeutic targets for bone diseases. Nerve Growth Factor (NGF), the first neurotrophic factor to be discovered, plays an important role in regulating immune cell function, influencing angiogenesis and participating in the physiological and pathological processes of bone homeostasis. Here, we mainly review the biological functions of NGF in the skeletal system and its molecular mechanisms, analyse the pathophysiological roles of the NGF signaling pathway in skeletal diseases such as osteoporosis, osteoarthritis, and fracture healing, and summarize the progress and challenges of the current clinical research on therapeutic strategies targeting NGF. In addition, we provide an overview of NGF and highlight the role of NGF in the regulation of bone formation and bone resorption. Therefore, by reviewing the literature related to NGF and bone diseases, this paper summarises the specific regulatory mechanisms of NGF in various bone diseases, which provides new perspectives and intervention targets for the treatment of skeletal diseases, especially in the field of diseases in which the effects of traditional treatments are limited. The therapeutic strategies targeting neurotrophic factors show broad prospects for clinical application.
Spatial intensity modulation in amplified laser beams, particularly hot spots, critically constrains attainable pulse peak power due to the damage threshold limitations of four-grating compressors. This study demonstrates that the double-smoothing grating compressor (DSGC) configuration effectively suppresses modulation through directional beam smoothing. Our systematic investigation validated the double-smoothing effect through numerical simulations and experimental measurements, with comprehensive spatiotemporal analysis revealing excellent agreement between numerical and practical pulse characteristics. Crucially, the DSGC enables a 1.74 times energy output boost compared to conventional compressors. These findings establish the DSGC as a pivotal advancement for next-generation ultrahigh-power laser systems, providing a viable pathway toward hundreds of PW output through optimized spatial energy redistribution.
Zakia H. Alhashem, Hasna Abdullah Alali, Shehab A. Mansour
et al.
The process of hydrogen peroxide decomposition, facilitated by copper oxide nanoparticles, produces reactive oxidants that possess the ability to oxidize multiple pollutants. CuO/Cu<sub>2</sub>O hybrid nanoparticles were successfully synthesized through a thermal decomposition route and applied as a heterogeneous catalytic oxidant for a fluorescent dye, namely Basic Violet 10 (BV10) dye. The microstructure and morphology of the prepared catalyst were evaluated via X-ray diffraction (XRD) and a field-emission scanning electron microscope (FE-SEM), respectively. The produced nanoparticles (NPs) were induced through ultraviolet light as a green photodecomposition technology. The system parameters were investigated, and the optimal initial NP concentration, H<sub>2</sub>O<sub>2</sub> concentration, and pH were assessed. The highest removal rate corresponding to 82% was achieved when 40 and 400 mg/L of NPs and H<sub>2</sub>O<sub>2</sub> were introduced, respectively. The system could operate at various pH values, and the alkaline pH (8.0) was efficient in proceeding with the oxidation system that overcomes the limitation of the homogeneous acidic Fenton catalyst. The introduced catalyst demonstrated consistent sustainability, achieving a notable removal rate of 68% even after six consecutive cycles of use. This innovative technique’s accomplishment examines the feasibility of utilizing copper as a replacement for iron in the Fenton reaction, demonstrating efficacy over an extended pH range. Finally, the temperature effectiveness of the reaction showed that the reaction is exothermic in nature, working at a low energy barrier (20.4 kJ/mol) and following the pseudo-second-order kinetic model.