jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinning Shao | Yue Liu | Jiaqi Yan | Ze‐Yi Yan | Yangyang Wu | Zhongying Ru | Jia‐Yu Liao | Xiaoye Miao | Linghui Qian
Source: Journal of Chemical Information and Modeling (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Prediction of Maximum Absorption Wavelength Using Deep Neural Networks Fluorescent molecules are important tools in biological detection, and numerous efforts have been made to develop compounds to meet the desired photophysical properties. For example, tuning the wavelength allows an appropriate penetration depth with minimal interference from the autofluorescence/scattering for a better signal-to-noise contrast. However, there are limited guidelines to rationally design or computationally predict the optical properties from first principles, and factors like the solvent effects will make it more complicated. Herein, we established a database (<b>SMFluo1</b>) of 1181 solvated small-molecule fluorophores covering the ultraviolet-visible-near-infrared absorption window and developed new machine learning models based on deep neural networks for accurately predicting photophysical parameters. The optimal system was applied to 120 out-of-sample compounds, and it exhibited remarkable accuracy with a mean relative error of 1.52%. In this new paradigm, a deep learning algorithm is promising to complement conventional theoretical and experimental studies of fluorophores and to greatly accelerate the discovery of new dyes. Due to its simplicity and efficiency, data from newly developed fluorophores can be easily supplemented to this system to further improve the accuracy across various dye families. Computer science ; Artificial neural network ; Absorption (acoustics) ; Biological system ; Near-infrared spectroscopy ; Fluorescence ; Wavelength ; Interference (communication) ; Ultraviolet ; Artificial intelligence ; Deep learning ; Materials science ; Optics ; Optoelectronics ; Telecommunications ; Physics Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Nanoplatforms for cancer theranostics
Authors: Yang Wang | Cheng‐Ru Wu | Siyuan Zhao | Jian Wang | Bingfeng Zu | Minfang Han | Qing Du | Meng Ni | Kui Jiao
Source: Applied Energy (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell Solid oxide fuel cell ; Durability ; Deposition (geology) ; Materials science ; Algorithm ; Multi-objective optimization ; Sensitivity (control systems) ; Parametric statistics ; Genetic algorithm ; Process engineering ; Coupling (piping) ; Pareto principle ; Power (physics) ; Computer science ; Mathematical optimization ; Engineering ; Electronic engineering ; Chemistry ; Machine learning ; Physics ; Mathematics ; Thermodynamics ; Composite material Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Xiaozheng Chen | Nils Kroell | Malte Althaus | Thomas Pretz | Roland Pomberger | Kathrin Greiff
Source: Resources Conservation and Recycling (article, 2022-10-20)
Field: Chemistry — Analytical Chemistry

Enabling mechanical recycling of plastic bottles with shrink sleeves through near-infrared spectroscopy and machine learning algorithms Shrink sleeves interfere with the mechanical recycling of plastic bottles because of their poor sortability during near-infrared (NIR)-based sorting. This study aims to identify reasons for their poor sortability and to propose solutions to overcome them. Five machine learning (ML) algorithms (decision tree, random forest, support vector machine, partial least squares, and convolutional neural network) are trained on NIR spectra of sleeved and non-sleeved materials and evaluated on different test datasets to investigate the influences of different product designs and post-consumer effects on the classification process. The results show that the sortability of sleeved plastic bottles can be significantly improved by (i) avoiding printing or coloring sleeves black and sleeves with large-area printing and high shrinkage; (ii) adding sleeved bottle spectra to the ML training data; (iii) choosing optimal ML algorithms with suitable hyperparameters. With these improvements, the NIR-based sorting and thus mechanical recycling of sleeved bottles could increase greatly. Sorting ; Algorithm ; Hyperparameter ; Decision tree ; Papermaking ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Cultural Heritage Materials Analysis
Authors: Xuan Zhang | Qing-Rong Zheng | Hongzhou He
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2022-08-01)
Field: Chemistry — Inorganic Chemistry

Machine-learning-based prediction of hydrogen adsorption capacity at varied temperatures and pressures for MOFs adsorbents Adsorption ; Hydrogen storage ; Hydrogen ; Thermodynamics ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis
Authors: Jung Woo Leem | Hee‐Jae Jeon | Yuhyun Ji | Sang Mok Park | Yunsang Kwak | Jongwoo Park | Kee‐Young Kim | Seongwan Kim | Young L. Kim
Source: ACS Central Science (article, 2022-04-13)
Field: Materials Science — Biomaterials

Edible Matrix Code with Photogenic Silk Proteins Counterfeit medicines are a healthcare security problem, posing not only a direct threat to patient safety and public health but also causing heavy economic losses. Current anticounterfeiting methods are limited due to the toxicity of the constituent materials and the focus of secondary packaging level protections. We introduce an edible, imperceptible, and scalable matrix code of information representation and data storage for pharmaceutical products. This matrix code is digestible as it is composed of silk fibroin genetically encoded with fluorescent proteins produced by ecofriendly, sustainable silkworm farming. Three distinct fluorescence emission colors are incorporated into a multidimensional parameter space with a variable encoding capacity in a format of matrix arrays. This code is smartphone-readable to extract a digitized security key augmented by a deep neural network for overcoming fabrication imperfections and a cryptographic hash function for enhanced security. The biocompatibility, photostability, thermal stability, long-term reliability, and low bit error ratio of the code support the immediate feasibility for dosage-level anticounterfeit measures and authentication features. The edible code affixed to each medicine can serve as serialization, track and trace, and authentication at the dosage level, empowering every patient to play a role in combating illicit pharmaceuticals. Computer science ; Computer security ; Cryptography ; Authentication (law) ; Code (set theory) ; Encoding (memory) ; Artificial intelligence Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Antimicrobial Peptides and Activities
Authors: Stephan Thaler | Maximilian Stupp | Julija Zavadlav
Source: The Journal of Chemical Physics (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

Deep coarse-grained potentials via relative entropy minimization Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations of unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside the training data domain, and the corresponding free energy surface is sensitive to errors in the transition regions. The standard alternative to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work, we demonstrate, for benchmark problems of liquid water and alanine dipeptide, that RE training is more data efficient, due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation, while maintaining accuracy. Thus, our findings support the use of training objectives beyond FM, as a promising direction for improving CG NN potential's accuracy and reliability. Computer science ; Minification ; Artificial neural network ; Energy minimization ; Benchmark (surveying) ; Entropy (arrow of time) ; Kullback–Leibler divergence ; Sensitivity (control systems) ; Algorithm ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Julia Razlivina | Nikita Serov | Olga E. Shapovalova | Vladimir V. Vinogradov
Source: Small (article, 2022-01-14)
Field: Materials Science — Materials Chemistry

DiZyme: Open‐Access Expandable Resource for Quantitative Prediction of Nanozyme Catalytic Activity Enzymes suffer from high cost, complex purification, and low stability. Development of low-cost artificial enzymes of comparative or higher effectiveness is desired. Given its complexity, it is desired to presume their activities prior to experiments. While computational approaches demonstrate success in modeling nanozyme activities, they require assumptions about the system to be made. Machine learning (ML) is an alternative approach towards data-driven material property prediction achieving high performance even on multicomponent complex systems. Despite the growing demand for customized nanozymes, there is no open access nanozyme database. Here, a user-friendly expandable database of >300 existing inorganic nanozymes is developed by data collection from >100 articles. Data analysis is performed to reveal the features responsible for catalytic activities of nanozymes, and new descriptors are proposed for its ML-assisted prediction. A random forest regression (RFR) model for evaluation of nanozyme peroxidase activity is developed and optimized by correlation-based feature selection and hyperparameter tuning, achieving performance up to R<sup>2</sup> = 0.796 for K<sub>cat</sub> and R<sup>2</sup> = 0.627 for K<sub>m</sub> . Experiment-confirmed unknown nanozyme activity prediction is also demonstrated. Moreover, the DiZyme expandable, open-access resource containing the database, predictive algorithm, and visualization tool is developed to boost novel nanozyme discovery worldwide (https://dizyme.net). Resource (disambiguation) ; Catalysis ; Computer science ; Materials science ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Annette Trunschke
Source: Catalysis Science & Technology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures. Perspective (graphical) ; Workflow ; Catalysis ; Computer science ; Data science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Materials science ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Bahram Vaghefinazari | Ewa Wierzbicka | Peter Visser | R. Posner | R. Arrabal | E. Matykina | M. Mohedano | Carsten Blawert | Mikhail L. Zheludkevich | Sviatlana V. Lamaka
Source: Materials (review, 2022-11-28)
Field: Materials Science — Materials Chemistry

Chromate-Free Corrosion Protection Strategies for Magnesium Alloys—A Review: Part III—Corrosion Inhibitors and Combining Them with Other Protection Strategies Owing to the unique active corrosion protection characteristic of hexavalent chromium-based systems, they have been projected to be highly effective solutions against the corrosion of many engineering metals. However, hexavalent chromium, rendered a highly toxic and carcinogenic substance, is being phased out of industrial applications. Thus, over the past few years, extensive and concerted efforts have been made to develop environmentally friendly alternative technologies with comparable or better corrosion protection performance to that of hexavalent chromium-based technologies. The introduction of corrosion inhibitors to a coating system on magnesium surface is a cost-effective approach not only for improving the overall corrosion protection performance, but also for imparting active inhibition during the service life of the magnesium part. Therefore, in an attempt to resemble the unique active corrosion protection characteristic of the hexavalent chromium-based systems, the incorporation of inhibitors to barrier coatings on magnesium alloys has been extensively investigated. In Part III of the Review, several types of corrosion inhibitors for magnesium and its alloys are reviewed. A discussion of the state-of-the-art inhibitor systems, such as iron-binding inhibitors and inhibitor mixtures, is presented, and perspective directions of research are outlined, including <i>in silico</i> or computational screening of corrosion inhibitors. Finally, the combination of corrosion inhibitors with other corrosion protection strategies is reviewed. Several reported highly protective coatings with active inhibition capabilities stemming from the on-demand activation of incorporated inhibitors can be considered a promising replacement for hexavalent chromium-based technologies, as long as their deployment is adequately addressed. Corrosion ; Hexavalent chromium ; Chromate conversion coating ; Materials science ; Magnesium ; Coating ; Corrosion inhibitor ; Environmentally friendly ; Chromium ; Metallurgy ; Nanotechnology Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Magnesium Oxide Properties and Applications
Authors: Yuanlong Cui | Jie Zhu | Hui Tong | Ran Zou
Source: iScience (review, 2022-12-19)
Field: Materials Science — Materials Chemistry

Advanced perspectives on MXene composite nanomaterials: Types synthetic methods, thermal energy utilization and 3D-printed techniques MXene, 2D material, can be synthesized as single flake with 1 nm thickness by using phase change material, polymer and graphene oxide. Meanwhile, the MXene and its composite derivative materials have been applied widely in electro-to-thermal conversion, photo-to-thermal conversion, thermal energy storage, and 3D printing ink aspects. Furthermore, the forward-looking utilization of the MXene nanomaterials in hydrogen energy storage, radio frequency field application, CO<sub>2</sub> capture and remediation of environmental pollution, is explored. This article reveals that the efficiencies of the photo-to-thermal and electro-to-thermal energy conversions with the MXene nanomaterials could reach about 80-90%. In parallel, it is demonstrated that the MXene printed ink has the excellent rheological property and high viscosity and stability of liquid, which contribute to arranging the multi-dimensional architectures with functional materials and controlling the flow rate of the MXene ink in the range of 0.03-0.15 mL/min for speedily printing and various printing structures. Nanomaterials ; Materials science ; Nanotechnology ; Thermal stability ; 3D printing ; Composite number ; Graphene ; Inkwell ; Energy storage ; Polymer ; Chemical engineering ; Composite material MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; Advanced Sensor and Energy Harvesting Materials
Authors: Decheng Lu | Huijuan Zhao | Xinying Zhang | Yingying Chen | Lingyan Feng
Source: Biosensors (review, 2022-10-02)
Field: Materials Science — Materials Chemistry

New Horizons for MXenes in Biosensing Applications Over the last few decades, biosensors have made significant advances in detecting non-invasive biomarkers of disease-related body fluid substances with high sensitivity, high accuracy, low cost and ease in operation. Among various two-dimensional (2D) materials, MXenes have attracted widespread interest due to their unique surface properties, as well as mechanical, optical, electrical and biocompatible properties, and have been applied in various fields, particularly in the preparation of biosensors, which play a critical role. Here, we systematically introduce the application of MXenes in electrochemical, optical and other bioanalytical methods in recent years. Finally, we summarise and discuss problems in the field of biosensing and possible future directions of MXenes. We hope to provide an outlook on MXenes applications in biosensing and to stimulate broader interests and research in MXenes across different disciplines. MXenes ; Biosensor ; Nanotechnology ; Bioanalysis ; Biochemical engineering ; Biocompatible material ; Computer science ; Materials science ; Engineering ; Biomedical engineering MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; 2D Materials and Applications
Authors: Nadimul Haque Faisal | Rehan Ahmed | Nazmi Sellami | Anil Prathuru | James Njuguna | Federico Venturi | Tanvir Hussain | Hamed Yazdani Nezhad | Nirmal Kumar Katiyar | Saurav Goel | Hari M. Upadhyaya | Shrikant Joshi | Firdaus Muhammad‐Sukki | Radhakrishna Prabhu | Tapas K. Mallick | William G. Whittow | Spyros Kamnis
Source: Advanced Engineering Materials (review, 2022-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal Spray Coatings for Electromagnetic Wave Absorption and Interference Shielding: A Review and Future Challenges This review aims to consolidate scattered literature on thermally sprayed coatings with nonionizing electromagnetic (EM) wave absorption and shielding over specific wavelengths potentially useful in diverse applications (e.g., microwave to millimeter wave, solar selective, photocatalytic, interference shielding, thermal barrier‐heat/emissivity). Materials EM properties such as electric permittivity, magnetic permeability, electrical conductivity, and dielectric loss are critical due to which a material can respond to absorbed, reflected, transmitted, or may excite surface electromagnetic waves at frequencies typical of electromagnetic radiations. Thermal spraying is a standard industrial practice used for depositing coatings where the sprayed layer is formed by successive impact of fully or partially molten droplets/particles of a material exposed to high or moderate temperatures and velocities. However, as an emerging novel application of an existing thermal spray techniques, some special considerations are warranted for targeted development involving relevant characterization. Key potential research areas of development relating to material selection and coating fabrication strategies and their impact on existing practices in the field are identified. The study shows a research gap in the feedstock materials design and doping, and their complex selection covered by thermally sprayed coatings that can be critical to advancing applications exploiting their electromagnetic properties. Materials science ; Electromagnetic shielding ; Microwave ; Coating ; Emissivity ; Electromagnetic radiation ; Permittivity ; Optoelectronics ; Thermal barrier coating ; Absorption (acoustics) ; Composite material ; Dielectric ; Thermal conductivity ; Engineering physics ; Optics ; Telecommunications ; Computer science Electromagnetic wave absorption materials ; Flame retardant materials and properties ; High-Temperature Coating Behaviors
Authors: Zi Wang | Yahui Xiao | Kun Liao | Tiantian Li | Hao Song | Haoshuo Chen | S. M. Zia Uddin | Dun Mao | Feifan Wang | Zhiping Zhou | Bo Yuan | Wei Jiang | Nicolas K. Fontaine | Amit Agrawal | Alan E. Willner | Xiaoyong Hu | Tingyi Gu
Source: Nanophotonics (review, 2022-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface on integrated photonic platform: from mode converters to machine learning Integrated photonic circuits are created as a stable and small form factor analogue of fiber-based optical systems, from wavelength-division multiplication transceivers to more recent mode-division multiplexing components. Silicon nanowire waveguides guide the light in a way that single and few mode fibers define the direction of signal flow. Beyond communication tasks, on-chip cascaded interferometers and photonic meshes are also sought for optical computing and advanced signal processing technology. Here we review an alternative way of defining the light flow in the integrated photonic platform, using arrays of subwavelength meta-atoms or metalines for guiding the diffraction and interference of light. The integrated metasurface system mimics free-space optics, where on-chip analogues of basic optical components are developed with foundry compatible geometry, such as low-loss lens, spatial-light modulator, and other wavefront shapers. We discuss the role of metasurface in integrated photonic signal processing systems, introduce the design principles of such metasurface systems for low loss compact mode conversion, mathematical operation, diffractive optical systems for hyperspectral imaging, and tuning schemes of metasurface systems. Then we perceive reconfigurability schemes for metasurface framework, toward optical neural networks and analog photonic accelerators. Converters ; Photonics ; Nanomaterials ; Mode (computer interface) ; Computer science ; Metamaterial ; Integrated optics ; Nanotechnology ; Materials science ; Electronic engineering ; Optoelectronics ; Engineering ; Electrical engineering ; Human–computer interaction ; Voltage Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research