jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohayra Mortazavi | Evgeny V. Podryabinkin | Ivan S. Novikov | Timon Rabczuk | Xiaoying Zhuang | Alexander V. Shapeev
Source: Computer Physics Communications (article, 2020-09-05)
Field: Materials Science — Materials Chemistry

Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution Anharmonicity ; Thermal conductivity ; Interatomic potential ; Density functional theory ; Molecular dynamics ; Boltzmann equation ; Bottleneck ; Ab initio ; Embedded atom model ; Statistical physics ; Materials science ; Computer science ; Thermodynamics ; Physics ; Computational chemistry ; Chemistry ; Condensed matter physics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Satoru Takeshita | Shanyu Zhao | Wim J. Malfait | Matthias M. Koebel
Source: Angewandte Chemie International Edition (review, 2020-04-09)
Field: Chemistry — Spectroscopy

Chemistry of Chitosan Aerogels: Three‐Dimensional Pore Control for Tailored Applications Chitosan is an abundant biopolymer derived from food waste with attractive properties, particularly its high biocompatibility and easy chemical processability. Here, we review the rapidly expanding literature on chitosan-based porous materials with a focus on the gelation mechanisms, the three-dimensional multiscale structural control, and the diverse chemical functionality not accessible by other biopolymers. The properties vary widely: from supercritically dried, mesoporous chitosan aerogels to very light, freeze-dried macroporous scaffolds. Porous chitosan displays impressive performance at the laboratory scale, but the highly (meso)porous nature amplifies not only the beneficial functionality of chitosan, but also its drawbacks, resulting in serious barriers to industrialization. In order to facilitate technology transfer, we critically discuss the practical feasibility of chitosan aerogels in potential applications compared to conventional and other biopolymer-based porous or nonporous materials. Chitosan ; Chemistry ; Nanotechnology ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity ; Mesoporous Materials and Catalysis
Authors: James P. Horwath | Dmitri N. Zakharov | Rémi Mégret | Eric A. Stach
Source: npj Computational Materials (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images Abstract Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design. Deep learning ; Convolutional neural network ; Computer science ; Artificial intelligence ; Segmentation ; Process (computing) ; Image segmentation ; Focus (optics) ; Machine learning ; Artificial neural network ; Pattern recognition (psychology) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Matthew C. Leech | Anthony D. Garcia | Alessia Petti | Adrian P. Dobbs | Kevin Lam
Source: Reaction Chemistry & Engineering (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Organic electrosynthesis: from academia to industry The growing impetus to develop greener and more cost-efficient synthetic methods has prompted chemists to look for new ways to activate small organic molecules. In this review, we cover the most recent industrial developments in electrosynthesis. Electrosynthesis ; Cover (algebra) ; Nanotechnology ; Biochemical engineering ; Engineering ; Chemistry ; Materials science ; Electrochemistry ; Mechanical engineering Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Lijing Xie | Fangyuan Su | Longfei Xie | Xiaoqian Guo | Zhenbing Wang | Qingqiang Kong | Guohua Sun | Aziz Ahmad | Xiaoming Li | Zonglin Yi | Cheng-Meng Chen
Source: Materials Chemistry Frontiers (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of pore structure and doping species on charge storage mechanisms in porous carbon-based supercapacitors Turning to improve performances under industrial requirements may be more meaningful for research itself. Supercapacitor ; Materials science ; Porosity ; Doping ; Chemical engineering ; Carbon fibers ; Nanotechnology ; Energy storage ; Charge (physics) ; Composite material ; Capacitance ; Electrode ; Composite number ; Optoelectronics ; Chemistry ; Engineering ; Physical chemistry ; Power (physics) Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Ruimin Ma | Tengfei Luo
Source: Journal of Chemical Information and Modeling (article, 2020-09-28)
Field: Materials Science — Materials Chemistry

PI1M: A Benchmark Database for Polymer Informatics Open-source data on large scale are the cornerstones for data-driven research, but they are not readily available for polymers. In this work, we build a benchmark database, called PI1M (referring to ∼1 million polymers for polymer informatics), to provide data resources that can be used for machine learning research in polymer informatics. A generative model is trained on ∼12 000 polymers manually collected from the largest existing polymer database PolyInfo, and then the model is used to generate ∼1 million polymers. A new representation for polymers, polymer embedding (PE), is introduced, which is then used to perform several polymer informatics regression tasks for density, glass transition temperature, melting temperature, and dielectric constants. By comparing the PE trained by the PolyInfo data and that by the PI1M data, we conclude that the PI1M database covers similar chemical space as PolyInfo, but significantly populate regions where PolyInfo data are sparse. We believe that PI1M will serve as a good benchmark database for future research in polymer informatics. Benchmark (surveying) ; Materials informatics ; Computer science ; Informatics ; Database ; Polymer ; Data mining ; Data science ; Health informatics ; Materials science ; Engineering informatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Rodrigo A. Vargas–Hernández
Source: The Journal of Physical Chemistry A (article, 2020-04-27)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models The accuracy of some density functional (DF) models widely used in material science depends on empirical or free parameters that are commonly tuned using reference physical properties. Grid-search methods are the standard numerical approximations used to find the optimal values of the free parameters, making the computational complexity to scale with the number of points in the grid. In this report, we illustrate that Bayesian optimization (BO), a sample-efficient machine learning algorithm, can calibrate different density functional models, e.g., hybrid exchange-correlation and range-separated density functionals. Using the atomization energies and bond lengths from the Gaussian-1 (G1) and Gaussian-2 (G2) databases, we show that BO optimizes the free parameters of the hybrid exchange-correlation functionals with approximately 55 evaluations of the error function. We also show that selecting exchange-correlation functionals for different physical systems can be done with BO. We jointly optimize and select the form of the exchange-correlation functionals and the free parameters by minimizing the root-mean-square error functions for the G1 and G2 data set atomization energies. The calibrated DF model is more accurate on average than standard DF methods, e.g., PBE0 and B3LYP. Bayesian optimization ; Gaussian ; Hyperparameter optimization ; Range (aeronautics) ; Grid ; Algorithm ; Hybrid functional ; Mathematics ; Mean squared error ; Function (biology) ; Computer science ; Bayesian probability ; Mathematical optimization ; Applied mathematics ; Density functional theory ; Statistics ; Artificial intelligence ; Computational chemistry ; Materials science ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Domenico Lombardo | Pietro Calandra | Luigi Pasqua | Salvatore Magazù
Source: Materials (review, 2020-02-26)
Field: Materials Science — Biomaterials

Self-Assembly of Organic Nanomaterials and Biomaterials: The Bottom-Up Approach for Functional Nanostructures Formation and Advanced Applications In this paper, we survey recent advances in the self-assembly processes of novel functional platforms for nanomaterials and biomaterials applications. We provide an organized overview, by analyzing the main factors that influence the formation of organic nanostructured systems, while putting into evidence the main challenges, limitations and emerging approaches in the various fields of nanotechology and biotechnology. We outline how the building blocks properties, the mutual and cooperative interactions, as well as the initial spatial configuration (and environment conditions) play a fundamental role in the construction of efficient nanostructured materials with desired functional properties. The insertion of functional endgroups (such as polymers, peptides or DNA) within the nanostructured units has enormously increased the complexity of morphologies and functions that can be designed in the fabrication of bio-inspired materials capable of mimicking biological activity. However, unwanted or uncontrollable effects originating from unexpected thermodynamic perturbations or complex cooperative interactions interfere at the molecular level with the designed assembly process. Correction and harmonization of unwanted processes is one of the major challenges of the next decades and requires a deeper knowledge and understanding of the key factors that drive the formation of nanomaterials. Self-assembly of nanomaterials still remains a central topic of current research located at the interface between material science and engineering, biotechnology and nanomedicine, and it will continue to stimulate the renewed interest of biologist, physicists and materials engineers by combining the principles of molecular self-assembly with the concept of supramolecular chemistry. Nanotechnology ; Nanomedicine ; Nanomaterials ; Harmonization ; Process (computing) ; Computer science ; Materials science ; Nanoparticle ; Physics Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Polydiacetylene-based materials and applications
Authors: Zakariyya Uba Zango | Khairulazhar Jumbri | Nonni Soraya Sambudi | Anita Ramli | Noor Hana Hanif Abu Bakar | Bahruddin Saad | Muhammad Nur’ Hafiz Rozaini | Hamza Ahmad Isiyaka | Ahmad Hussaini Jagaba | Osamah Aldaghri | Abdelmoneim Sulieman
Source: Polymers (review, 2020-11-10)
Field: Chemistry — Inorganic Chemistry

A Critical Review on Metal-Organic Frameworks and Their Composites as Advanced Materials for Adsorption and Photocatalytic Degradation of Emerging Organic Pollutants from Wastewater Water-borne emerging pollutants are among the greatest concern of our modern society. Many of these pollutants are categorized as endocrine disruptors due to their environmental toxicities. They are harmful to humans, aquatic animals, and plants, to the larger extent, destroying the ecosystem. Thus, effective environmental remediations of these pollutants became necessary. Among the various remediation techniques, adsorption and photocatalytic degradation have been single out as the most promising. This review is devoted to the compilations and analysis of the role of metal-organic frameworks (MOFs) and their composites as potential materials for such applications. Emerging organic pollutants, like dyes, herbicides, pesticides, pharmaceutical products, phenols, polycyclic aromatic hydrocarbons, and perfluorinated alkyl substances, have been extensively studied. Important parameters that affect these processes, such as surface area, bandgap, percentage removal, equilibrium time, adsorption capacity, and recyclability, are documented. Finally, we paint the current scenario and challenges that need to be addressed for MOFs and their composites to be exploited for commercial applications. Pollutant ; Adsorption ; Environmental remediation ; Wastewater ; Photocatalysis ; Environmental science ; Environmental chemistry ; Materials science ; Waste management ; Environmental engineering ; Contamination ; Chemistry ; Organic chemistry ; Catalysis ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Advanced Nanomaterials in Catalysis
Authors: Shuqi Wu | Yang Li | Wei-Hang Ding | Letong Xu | Yuan Ma | Lianbing Zhang
Source: Nano-Micro Letters (review, 2020-03-10)
Field: Materials Science — Materials Chemistry

Recent Advances of Persistent Luminescence Nanoparticles in Bioapplications Persistent luminescence phosphors are a novel group of promising luminescent materials with afterglow properties after the stoppage of excitation. In the past decade, persistent luminescence nanoparticles (PLNPs) with intriguing optical properties have attracted a wide range of attention in various areas. Especially in recent years, the development and applications in biomedical fields have been widely explored. Owing to the efficient elimination of the autofluorescence interferences from biotissues and the ultra-long near-infrared afterglow emission, many researches have focused on the manipulation of PLNPs in biosensing, cell tracking, bioimaging and cancer therapy. These achievements stimulated the growing interest in designing new types of PLNPs with desired superior characteristics and multiple functions. In this review, we summarize the works on synthesis methods, bioapplications, biomembrane modification and biosafety of PLNPs and highlight the recent advances in biosensing, imaging and imaging-guided therapy. We further discuss the new types of PLNPs as a newly emerged class of functional biomaterials for multiple applications. Finally, the remaining problems and challenges are discussed with suggestions and prospects for potential future directions in the biomedical applications. Nanotechnology ; Persistent luminescence ; Biocompatible material ; Luminescence ; Materials science ; Optical imaging ; Computer science ; Medicine ; Biomedical engineering ; Optoelectronics ; Physics ; Optics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: S. Prabha | D. Durgalakshmi | Saravanan Rajendran | Éric Lichtfouse
Source: Environmental Chemistry Letters (review, 2020-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plant-derived silica nanoparticles and composites for biosensors, bioimaging, drug delivery and supercapacitors: a review Husk ; Nanoparticle ; Materials science ; Supercapacitor ; Biomass (ecology) ; Drug delivery ; Bagasse ; Nanotechnology ; Nanocomposite ; Pulp and paper industry ; Chemistry ; Electrochemistry Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications ; Electrochemical sensors and biosensors
Authors: Chu Zhang | Wenyan Wu | Lei Zhou | Huan Cheng | Xingqian Ye | Yong He
Source: Food Chemistry (article, 2020-03-01)
Field: Chemistry — Analytical Chemistry

Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Principal component analysis ; Partial least squares regression ; Feature extraction ; Convolutional neural network ; Support vector machine ; Mathematics ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Konstantin L. Firestein | Joel E. von Treifeldt | Dmitry G. Kvashnin | Joseph F. S. Fernando | Chao Zhang | Alexander G. Kvashnin | Evgeny V. Podryabinkin | Alexander V. Shapeev | Dumindu P. Siriwardena | Павел Б. Сорокин | Dmitri Golberg
Source: Nano Letters (article, 2020-07-07)
Field: Materials Science — Materials Chemistry

Young’s Modulus and Tensile Strength of Ti<sub>3</sub>C<sub>2</sub> MXene Nanosheets As Revealed by <i>In Situ</i> TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations Two-dimensional transition metal carbides, that is, MXenes and especially Ti<sub>3</sub>C<sub>2</sub>, attract attention due to their excellent combination of properties. Ti<sub>3</sub>C<sub>2</sub> nanosheets could be the material of choice for future flexible electronics, energy storage, and electromechanical nanodevices. There has been limited information available on the mechanical properties of Ti<sub>3</sub>C<sub>2</sub>, which is essential for their utilization. We have fabricated Ti<sub>3</sub>C<sub>2</sub> nanosheets and studied their mechanical properties using direct <i>in situ</i> tensile tests inside a transmission electron microscope, quantitative nanomechanical mapping, and theoretical calculations employing machine-learning derived potentials. Young's modulus in the direction perpendicular to the Ti<sub>3</sub>C<sub>2</sub> basal plane was found to be 80-100 GPa. The tensile strength of Ti<sub>3</sub>C<sub>2</sub> nanosheets reached up to 670 MPa for ∼40 nm thin nanoflakes, while a strong dependence of tensile strength on nanosheet thickness was demonstrated. Theoretical calculations allowed us to study mechanical characteristics of Ti<sub>3</sub>C<sub>2</sub> as a function of nanosheet geometrical parameters and structural defect concentration. Materials science ; Ultimate tensile strength ; Modulus ; Atomic force microscopy ; In situ ; Young's modulus ; Nanoscopic scale ; Composite material ; Nanotechnology ; Chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Aluminum Alloys Composites Properties
Authors: Francisco A.G. Soares Silva | Fernando Dourado | Miguel Gama | Maria de Fátima Tavares Poças
Source: Nanomaterials (review, 2020-10-16)
Field: Materials Science — Biomaterials

Nanocellulose Bio-Based Composites for Food Packaging The food industry is increasingly demanding advanced and eco-friendly sustainable packaging materials with improved physical, mechanical and barrier properties. The currently used materials are synthetic and non-degradable, therefore raising environmental concerns. Consequently, research efforts have been made in recent years towards the development of bio-based sustainable packaging materials. In this review, the potential of nanocelluloses as nanofillers or as coatings for the development of bio-based nanocomposites is discussed, namely: (i) the physico-chemical interaction of nanocellulose with the adjacent polymeric phase, (ii) the effect of nanocellulose modification/functionalization on the final properties of the composites, (iii) the production methods for such composites, and (iv) the effect of nanocellulose on the overall migration, toxicity, and the potential risk to human health. Lastly, the technology readiness level of nanocellulose and nanocellulose based composites for the market of food packaging is discussed. Nanocellulose ; Materials science ; Food packaging ; Nanocomposite ; Composite material ; Surface modification ; Environmentally friendly ; Nanotechnology ; Cellulose ; Chemical engineering ; Chemistry ; Engineering Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties
Authors: Guang Chen | Zhiqiang Shen | Akshay Iyer | Umar Farooq Ghumman | Shan Tang | Jinbo Bi | Wei Chen | Ying Li
Source: Polymers (review, 2020-01-08)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields. Material Design ; Organic molecules ; Nanotechnology ; Computer science ; Chemical space ; Intuition ; Polymer ; Biochemical engineering ; Materials science ; Molecule ; Engineering ; Chemistry ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Injection Molding Process and Properties ; Computational Drug Discovery Methods