jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenhua Chen | Pengju Liu | Lizhen Min | Yiming Zhou | Yuan Liu | Qi Wang | Wenfeng Duan
Source: Nano-Micro Letters (article, 2018-02-16)
Field: Materials Science — Polymers and Plastics

Non-covalently Functionalized Graphene Oxide-Based Coating to Enhance Thermal Stability and Flame Retardancy of PVA Film The synergistic effect of conventional flame-retardant elements and graphene has received extensive attention in the development of a new class of flame retardants. Compared to covalent modification, the non-covalent strategy is simpler and expeditious and entirely preserves the original quality of graphene. Thus, non-covalently functionalized graphene oxide (FGO) with a phosphorus-nitrogen compound was successfully prepared via a one-pot process in this study. Polyethyleneimine and FGO were alternatively deposited on the surface of a poly(vinyl alcohol) (PVA) film via layer-by-layer assembly driven by electrostatic interaction, imparting excellent flame retardancy to the coated PVA film. The multilayer FGO-based coating formed a protective shield encapsulating the PVA matrix, effectively blocking the transfer of heat and mass during combustion. The coated PVA has a higher initial decomposition temperature of about 260 °C and a nearly 60% reduction in total heat release than neat PVA does. Our results may have a promising prospect for flame-retardant polymers. Graphene ; Materials science ; Fire retardant ; Coating ; Covalent bond ; Vinyl alcohol ; Thermal stability ; Chemical engineering ; Oxide ; Polymer ; Thermal decomposition ; Composite material ; Polymer chemistry ; Organic chemistry ; Nanotechnology ; Chemistry Flame retardant materials and properties ; Graphene research and applications ; Electromagnetic wave absorption materials
Authors: Florian Häse | Ignacio Fdez. Galván | Alán Aspuru‐Guzik | Roland Lindh | Morgane Vacher
Source: Chemical Science (article, 2018-12-21)
Field: Materials Science — Materials Chemistry

How machine learning can assist the interpretation of <i>ab initio</i> molecular dynamics simulations and conceptual understanding of chemistry Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without <i>a priori</i> knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of <i>ab initio</i> molecular dynamics simulations - the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans. Interpretation (philosophy) ; Molecular dynamics ; Dynamics (music) ; Ab initio ; Computer science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: A. Miguel | Anja Aarva | Volker L. Deringer | Gábor Cśanyi | Tomi Laurila
Source: Chemistry of Materials (article, 2018-09-10)
Field: Materials Science — Materials Chemistry

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp<sup>2</sup> motifs with markedly different reactivities. We therefore draw a comprehensive, both qualitative and quantitative, picture of the surface chemistry of a-C and its reactivity toward -H, -O, -OH, and -COOH. While this paper focuses on a-C surfaces, the presented methodology opens up a new systematic and general way to study the surface chemistry of amorphous and disordered materials. Reactivity (psychology) ; Amorphous solid ; Amorphous carbon ; Surface modification ; Carbon fibers ; Nanotechnology ; Materials science ; Chemical physics ; Density functional theory ; Characterization (materials science) ; Surface (topology) ; Computational chemistry ; Chemistry ; Organic chemistry ; Physical chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Catalysis and Oxidation Reactions
Authors: Ting Guo | Mei Lin | Junxing Huang | Chenglin Zhou | Weizhong Tian | Hong Yu | Xingmao Jiang | Jun Ye | Yujuan Shi | Yanhong Xiao | Xuefeng Bian | Xiaoqian Feng
Source: Journal of Nanomaterials (article, 2018-01-01)
Field: Materials Science — Biomaterials

The Recent Advances of Magnetic Nanoparticles in Medicine With the progress of nanotechnology and molecular biology, nanoparticles have been widely studied and applied in biomedicine. Particularly, characterized by unique magnetic property, targeting, and biocompatibility, magnetic nanoparticles have become one of the research hotspots in the nanomedical field. Herein, we summarized the recent advances of magnetic nanoparticles in medicine, including the property, carrier function, MRI, and tumor magnetic inductive hyperthermia of magnetic nanoparticles. Materials science ; Biomedicine ; Nanotechnology ; Magnetic nanoparticles ; Biocompatibility ; Nanoparticle ; Nanomedicine ; Bioinformatics Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Yuge Huang | Engle Liao | Ran Chen | Shin‐Tson Wu
Source: Applied Sciences (article, 2018-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Liquid-Crystal-on-Silicon for Augmented Reality Displays In this paper, we review liquid-crystal-on-silicon (LCoS) technology and focus on its new application in emerging augmented reality (AR) displays. In the first part, the LCoS working principles of three commonly adopted LC modes—vertical alignment and twist nematic for amplitude modulation, and homogeneous alignment for phase modulation—are introduced and their pros and cons evaluated. In the second part, the fringing field effect is analyzed, and a novel pretilt angle patterning method for suppressing the effect is presented. Moreover, we illustrate how to integrate the LCoS panel in an AR display system. Both currently available intensity modulators and under-developing holographic displays are covered, with special emphases on achieving high image quality, such as a fast response time and high-resolution. The rapidly increasing application of LCoS in AR head-mounted displays and head-up displays is foreseeable. Liquid crystal on silicon ; Liquid crystal ; Materials science ; Holography ; Liquid-crystal display ; Optics ; Modulation (music) ; Augmented reality ; Twist ; Optoelectronics ; Computer science ; Physics ; Artificial intelligence ; Acoustics Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Photonic Crystals and Applications
Authors: Pasquale Tripodi | Daniele Massa | Accursio Venezia | Teodoro Cardi
Source: Agronomy (article, 2018-04-22)
Field: Chemistry — Analytical Chemistry

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with “omics” disciplines will also be discussed. Current (fluid) ; Biotechnology ; Environmental science ; Agricultural engineering ; Agroforestry ; Agronomy ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Ailan Yan | Xinchang Wang | J.P. Cheng
Source: Nanomaterials (review, 2018-09-20)
Field: Materials Science — Materials Chemistry

Research Progress of NiMn Layered Double Hydroxides for Supercapacitors: A Review The research on supercapacitors has been attractive due to their large power density, fast charge/discharge speed and long lifespan. The electrode materials for supercapacitors are thus intensively investigated to improve the electrochemical performances. Various transition metal layered double hydroxides (LDHs) with a hydrotalcite-like structure have been developed to be promising electrode materials. Earth-abundant metal hydroxides are very suitable electrode materials due to the low cost and high specific capacity. This is a review paper on NiMn LDHs for supercapacitor application. We focus particularly on the recent published papers using NiMn LDHs as electrode materials for supercapacitors. The preparation methods for NiMn LDHs are introduced first. Then, the structural design and chemical modification of NiMn LDH materials, as well as the composites and films derived from NiMn LDHs are discussed. These approaches are proven to be effective to enhance the performance of supercapacitor. Finally, the reports related to NiMn LDH-based asymmetric supercapacitors are summarized. A brief discussion of the future development of NiMn LDHs is also provided. Supercapacitor ; Layered double hydroxides ; Materials science ; Electrochemistry ; Electrode ; Nanotechnology ; Chemical engineering ; Chemistry ; Hydroxide ; Engineering Layered Double Hydroxides Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions
Authors: Ruixue Zhang | Jason Li | Tian Zhang | Mohammad Amini | Chunsheng He | Brian Lu | Taksim Ahmed | HoYin Lip | Andrew M. Rauth | Xiao Yu Wu
Source: Acta Pharmacologica Sinica (review, 2018-04-26)
Field: Materials Science — Biomaterials

Importance of integrating nanotechnology with pharmacology and physiology for innovative drug delivery and therapy – an illustration with firsthand examples Nanocarriers ; Drug delivery ; Drug ; Pharmacology ; Nanotechnology ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Christopher J. Bartel | Alan W. Weimer | Stephan Lany | Charles B. Musgrave | Aaron M. Holder
Source: npj Computational Materials (article, 2018-12-28)
Field: Materials Science — Materials Chemistry

The role of decomposition reactions in assessing first-principles predictions of solid stability Abstract The performance of density functional theory approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determined enthalpies of formation from elemental phases, Δ H f . However, a compound competes thermodynamically with both other compounds and their constituent elemental forms, and thus, the enthalpies of the decomposition reactions to these competing phases, Δ H d , determine thermodynamic stability. We evaluated the phase diagrams for 56,791 compounds to classify decomposition reactions into three types: 1. those that produce elemental phases, 2. those that produce compounds, and 3. those that produce both. This analysis shows that the decomposition into elemental forms is rarely the competing reaction that determines compound stability and that approximately two-thirds of decomposition reactions involve no elemental phases. Using experimentally reported formation enthalpies for 1012 solid compounds, we assess the accuracy of the generalized gradient approximation (GGA) (PBE) and meta-GGA (SCAN) density functionals for predicting compound stability. For 646 decomposition reactions that are not trivially the formation reaction, PBE (mean absolute difference between theory and experiment (MAD) = 70 meV/atom) and SCAN (MAD = 59 meV/atom) perform similarly, and commonly employed correction schemes using fitted elemental reference energies make only a negligible improvement (~2 meV/atom). Furthermore, for 231 reactions involving only compounds (Type 2), the agreement between SCAN, PBE, and experiment is within ~35 meV/atom and is thus comparable to the magnitude of experimental uncertainty. Decomposition ; Elemental analysis ; Chemistry ; Standard enthalpy of formation ; Density functional theory ; Atom (system on chip) ; Chemical stability ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Inorganic chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; X-ray Diffraction in Crystallography
Authors: Prabu Ravindran | Adriana Costa | Richard Soares | Alex C. Wiedenhoeft
Source: Plant Methods (article, 2018-03-23)
Field: Chemistry — Organic Chemistry

Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks The end-to-end trained image classifiers that we present discriminate the woods based on digital images of the transverse surface of solid wood blocks, which are surfaces and images that can be prepared and captured in the field. Hence this work represents a strong proof-of-concept for using computer vision and convolutional neural networks to develop practical models for field screening timber and wood products to combat illegal logging. Swietenia macrophylla ; CITES ; Convolutional neural network ; Illegal logging ; Identification (biology) ; Artificial intelligence ; Solid wood ; Computer science ; Machine learning ; Forestry ; Geography ; Ecology ; Biology ; Logging Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Plant Diversity and Evolution
Authors: Monica Terracciano | Luca De Stefano | Ilaria Rea
Source: Pharmaceutics (review, 2018-11-20)
Field: Materials Science — Biomaterials

Diatoms Green Nanotechnology for Biosilica-Based Drug Delivery Systems Diatom microalgae are the most outstanding natural source of porous silica. The diatom cell is enclosed in a three-dimensional (3-D) ordered nanopatterned silica cell wall, called frustule. The unique properties of the diatom frustule, including high specific surface area, thermal stability, biocompatibility, and tailorable surface chemistry, make diatoms really promising for biomedical applications. Moreover, they are easy to cultivate in an artificial environment and there is a large availability of diatom frustules as fossil material (diatomite) in several areas of the world. For all these reasons, diatoms are an intriguing alternative to synthetic materials for the development of low-cost drug delivery systems. This review article focuses on the possible use of diatom-derived silica as drug carrier systems. The functionalization strategies of diatom micro/nanoparticles for improving their biophysical properties, such as cellular internalization and drug loading/release kinetics, are described. In addition, the realization of hybrid diatom-based devices with advanced properties for theranostics and targeted or augmented drug delivery applications is also discussed. Frustule ; Diatom ; Drug delivery ; Nanotechnology ; Biocompatibility ; Nanoparticle ; Mesoporous silica ; Materials science ; Chemistry ; Mesoporous material ; Biology ; Ecology ; Organic chemistry Diatoms and Algae Research ; Protist diversity and phylogeny
Authors: Anurag Jha | Anand Chandrasekaran | Chiho Kim | Rampi Ramprasad
Source: Modelling and Simulation in Materials Science and Engineering (article, 2018-12-17)
Field: Materials Science — Materials Chemistry

Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures Abstract Over the past decade, there has been a resurgence in the importance of data-driven techniques in materials science and engineering. The utilization of state-of-the art algorithms, coupled with the increased availability of experimental and computational data, has led to the development of surrogate models offering the promise of rapid and accurate predictions of materials’ properties based solely on their structure or composition. Such machine learning (ML) models are trained on available past data and are thus susceptible to the intrinsic uncertainties/errors associate with these past measurements. The glass transition temperature ( T g ) of polymers, a property of paramount interest in polymer science, is one strong example of a material property that can show widespread variation in the final reported value as a result of a variety of intrinsic and extrinsic factors that occur during the experimental measurement process. In the current work, we curate a large database of T g measurements from a variety of data sources and proceed to investigate the statistical nature of the inherent uncertainties in the database. Through the partitioning of the dataset using statistically relevant measures, we investigate the effect of variations in the dataset on the performance of the final ML model. We demonstrate that the measure of central tendency, median is a valid approximation when dealing with multiple reported values for T g when dealing with multiple reported values of T g for the same polymeric material. Moreover, the Bayesian model noise/uncertainty that emerges from our machine-learning pipeline is able to represent quantitatively the underlying noise/uncertainties in the experimental measurement of T g . Materials science ; Glass transition ; Polymer ; Thermodynamics ; Statistical physics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Yang Hong | Zhongtao Zhang | Jingchao Zhang | Xiao Cheng Zeng
Source: Nanoscale (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride High-performance thermal interface materials (TIMs) have attracted persistent attention for the design and development of miniaturized nanoelectronic devices; however, a large number of potential new materials exist to form these heterostructures and the explorations of their thermal properties are time consuming and expensive. In this work, we train several supervised machine learning (ML) and artificial neural network (ANN) models to predict the interfacial thermal resistance (R) between graphene and hexagonal boron-nitride (hBN) with only the knowledge of the system temperature, coupling strength between two layers, and in-plane tensile strains. The training data were obtained by high-throughput computations (HTCs) of R using classical molecular dynamics (MD) simulations. Four different ML models, i.e., linear regression, polynomial regression, decision tree and random forest, are explored. A pair of one dense layer ANNs and another pair of two dense layer deep neural networks (DNNs) are also investigated. It is reported that the DNN models provide better R prediction results compared to the ML models. The thermal property predictions using HTC and ML/ANN models are applicable to a wide range of materials and open up new perspectives in the explorations of TIMs. Materials science ; Artificial neural network ; Graphene ; Machine learning ; Thermal ; Artificial intelligence ; Hexagonal boron nitride ; Computation ; Decision tree ; Boron nitride ; Computer science ; Nanotechnology ; Algorithm ; Thermodynamics ; Physics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Qunchao Tong | Lantian Xue | Jian Lv | Yanchao Wang | Yanming Ma
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and materials discovery. However, they are generally restricted to small systems owing to the heavy computational cost of the underlying density functional theory (DFT) calculations in structure optimizations. In this work, by combining a state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for the structure prediction of large systems, in which a ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, both of which are challenging cases for either the construction of ML potentials or extensive structure searches. Experimental structures of B36 and B40 clusters can be readily reproduced, and the putative global minimum structure for the B84 cluster is proposed, where the computational cost is substantially reduced by ∼1-2 orders of magnitude if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction in large systems via the combination of state-of-art structure prediction methods and ML techniques. Ab initio ; Potential energy surface ; Density functional theory ; Cluster (spacecraft) ; Protein structure prediction ; Surface structure ; Acceleration ; Potential energy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enzyme Structure and Function