jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Wei Bao | Rui Li | Zhi Cheng Dai | Jian Tang | Xin Shi | Jie Ting Geng | Zhi Feng Deng | Jing Hua
Source: Frontiers in Chemistry (review, 2020-09-10)
Field: Materials Science — Polymers and Plastics

Diketopyrrolopyrrole (DPP)-Based Materials and Its Applications: A Review Diketopyrrolopyrrole (DPP) and its derivatives have been widely studied in the past few years due to its intrinsic physical and chemical properties, such as strong electron-withdrawing, deep color, high charge carrier mobility, strong aggregation, good thermal-/photo-stability. In the 1970s, DPP was developed and used only in inks, paints, and plastics. Later, DPP containing materials were found to have potential other applications, typically in electronic devices, which attracted the attention of scientists. In this feature article, the synthesis pathway of DPP-based materials and their applications in organic field-effect transistors, photovoltaic devices, sensors, two photo-absorption materials, and others are reviewed, and possible future applications are discussed. The review outlines a theoretical scaffold for the development of conjugated DPP-based materials, which have multiple potential applications. Materials science ; Nanotechnology ; Conjugated system ; Electron mobility ; Polymer ; Optoelectronics ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Aaron Keeley | László Petri | Péter Ábrányi‐Balogh | György M. Keserű
Source: Drug Discovery Today (review, 2020-04-13)
Field: Chemistry — Organic Chemistry

Covalent fragment libraries in drug discovery Targeted covalent inhibitors and chemical probes have become integral parts of drug discovery approaches. Given the advantages of fragment-based drug discovery, screening electrophilic fragments emerged as a promising alternative to discover and validate novel targets and to generate viable chemical starting points even for targets that are barely tractable. In this review, we present recent principles and considerations in the design of electrophilic fragment libraries from the selection of the appropriate covalent warhead through the design of the covalent fragment to the compilation of the library. We then summarize recent screening methodologies of covalent fragments against surrogate models, proteins, and the whole proteome, or living cells. Finally, we highlight recent drug discovery applications of covalent fragment libraries. Drug discovery ; Fragment (logic) ; Covalent bond ; Computational biology ; Combinatorial chemistry ; Chemistry ; Electrophile ; Nanotechnology ; Computer science ; Biochemistry ; Biology ; Materials science ; Organic chemistry ; Algorithm Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Peptidase Inhibition and Analysis
Authors: Xiaowei Xie | Kristin A. Persson | David W. Small
Source: Journal of Chemical Theory and Computation (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). The new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on Li<sub><i>n</i></sub>H<sub><i>n</i></sub> clusters. Density functional theory ; Electronic structure ; Computer science ; Range (aeronautics) ; Function (biology) ; Atom (system on chip) ; Electronic density ; Potential energy ; Artificial neural network ; Energy (signal processing) ; Statistical physics ; Machine learning ; Physics ; Atomic physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Callum J. Court | Jacqueline M. Cole
Source: npj Computational Materials (article, 2020-03-13)
Field: Materials Science — Materials Chemistry

Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning Abstract Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications. Superconductivity ; Pipeline (software) ; Phase transition ; Phase diagram ; Computer science ; Property (philosophy) ; Materials science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Phase (matter) ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Iron-based superconductors research ; Electronic and Structural Properties of Oxides
Authors: Cas van der Oord | Geneviève Dusson | Gábor Cśanyi | Christoph Ortner
Source: Machine Learning Science and Technology (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials We investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The 'atomic body-ordered permutation-invariant polynomials' comprise a systematic basis and are constructed to preserve the symmetry of the potential energy function with respect to rotations and permutations. In contrast to kernel based and artificial neural network models, the explicit decomposition of the total energy as a sum of atomic body-ordered terms allows to keep the dimensionality of the fit reasonably low, up to just 10 for the 5-body terms. The explainability of the potential is aided by this decomposition, as the low body-order components can be studied and interpreted independently. Moreover, although polynomial basis functions are thought to extrapolate poorly, we show that the low dimensionality combined with careful regularisation actually leads to better transferability than the high dimensional, kernel based Gaussian Approximation Potential.&#13;\n&#13;\n Invariant (physics) ; Permutation (music) ; Curse of dimensionality ; Mathematics ; Pure mathematics ; Physics ; Mathematical physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Ralf Metzler
Source: The European Physical Journal Special Topics (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Superstatistics and non-Gaussian diffusion Abstract Brownian motion and viscoelastic anomalous diffusion in homogeneous environments are intrinsically Gaussian processes. In a growing number of systems, however, non-Gaussian displacement distributions of these processes are being reported. The physical cause of the non-Gaussianity is typically seen in different forms of disorder. These include, for instance, imperfect “ensembles” of tracer particles, the presence of local variations of the tracer mobility in heteroegenous environments, or cases in which the speed or persistence of moving nematodes or cells are distributed. From a theoretical point of view stochastic descriptions based on distributed (“superstatistical”) transport coefficients as well as time-dependent generalisations based on stochastic transport parameters with built-in finite correlation time are invoked. After a brief review of the history of Brownian motion and the famed Gaussian displacement distribution, we here provide a brief introduction to the phenomenon of non-Gaussianity and the stochastic modelling in terms of superstatistical and diffusing-diffusivity approaches. Statistical physics ; Gaussian ; Brownian motion ; Diffusion ; Anomalous diffusion ; Displacement (psychology) ; Continuous-time random walk ; Fractional Brownian motion ; Stochastic process ; Random walk ; Inverse Gaussian distribution ; Gaussian process ; Diffusion process ; Viscoelasticity ; Thermal diffusivity ; Physics ; Distribution (mathematics) ; Mathematics ; Computer science ; Mathematical analysis ; Innovation diffusion ; Statistics ; Thermodynamics ; Quantum mechanics Material Dynamics and Properties ; Fractional Differential Equations Solutions ; Theoretical and Computational Physics
Authors: Kameel Abdel‐Latif | Robert W. Epps | Fazel Bateni | Suyong Han | Kristofer G. Reyes | Milad Abolhasani
Source: Advanced Intelligent Systems (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow Identifying the optimal formulation of emerging inorganic lead halide perovskite quantum dots (LHP QDs) with their vast colloidal synthesis universe and multiple synthesis/postsynthesis processing parameters is a challenging undertaking for material‐ and time‐intensive, batch synthesis strategies. Herein, a modular microfluidic synthesis strategy, integrated with an artificial intelligence (AI)‐guided decision‐making agent for intelligent navigation through the complex colloidal synthesis universe of LHP QDs with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2 × 10 7 , is introduced. Utilizing the developed autonomous microfluidic experimentation strategy within a global learning framework, the optimal formulation of LHP QDs is rapidly identified through a two‐step colloidal synthesis and postsynthesis halide exchange reaction, for 10 different emission colors in less than 40 min per desired peak emission energy. Using two in‐series microfluidic reactors enables continuous bandgap engineering of LHP QDs via in‐line halide exchange reactions without the need for an intermediate washing step. Using an inert gas within a three‐phase flow format enables successful, self‐synchronized continuous delivery of halide salt precursor into moving droplets containing LHP QDs, resulting in accelerated closed‐loop formulation optimization and end‐to‐end continuous manufacturing of LHP QDs with desired optoelectronic properties. Microfluidics ; Quantum dot ; Halide ; Nanotechnology ; Materials science ; Continuous flow ; Computer science ; Chemistry ; Engineering ; Biochemical engineering ; Inorganic chemistry Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Namid Stillman | Marina Kovačević | Igor Balaž | Sabine Hauert
Source: npj Computational Materials (article, 2020-07-08)
Field: Materials Science — Biomaterials

In silico modelling of cancer nanomedicine, across scales and transport barriers Abstract Nanoparticles promise to improve the treatment of cancer through their increasingly sophisticated functionalisations and ability to accumulate in certain tumours. Yet recent work has shown that many nanomedicines fail during clinical trial. One issue is the lack of understanding of how nanoparticle designs impact their ability to overcome transport barriers in the body, including their circulation in the blood stream, extravasation into tumours, transport through tumour tissue, internalisation in the targeted cells, and release of their active cargo. Increased computational power, as well as improved multi-scale simulations of tumours, nanoparticles, and the biological transport barriers that affect them, now allow us to investigate the influence of a range of designs in biologically relevant scenarios. This presents a new opportunity for high-throughput, systematic, and integrated design pipelines powered by data and machine learning. With this paper, we review latest results in multi-scale simulations of nanoparticle transport barriers, as well as available software packages, with the aim of focussing the wider research community in building a common computational framework that can overcome some of the current obstacles facing efficient nanoparticle design. Nanomedicine ; Computer science ; Nanotechnology ; Scale (ratio) ; Cancer ; Risk analysis (engineering) ; Biochemical engineering ; Systems engineering ; Nanoparticle ; Engineering ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Microfluidic and Bio-sensing Technologies
Authors: Jingkai Weng | Yujiang Ding | Chengbo Hu | Xue-Feng Zhu | Bin Liang | Jing Yang | Jianchun Cheng
Source: Nature Communications (article, 2020-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Meta-neural-network for real-time and passive deep-learning-based object recognition Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the "intelligence" to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields. Object (grammar) ; Cognitive neuroscience of visual object recognition ; Field (mathematics) ; Artificial neural network ; Deep learning ; Metamaterial ; Convolutional neural network Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Orbital Angular Momentum in Optics
Authors: Lei Pang | Sen Men | Lei Yan | Jiang Xiao
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques Highly viable seeds are of great significance for agricultural development, and the traditional corn seed vigor detection method is time-consuming and laborious. In this paper, the spectral and image information of hyperspectral imaging was used, and a distinction between seed vigor detection and prediction was proposed. The potential of hyperspectral imaging technology and convolutional neural networks (CNNs) to identify and predict maize seed vitality was evaluated. The hyperspectral information in 10 hours before the germination of four vigor level seeds (144 samples each) was collected. A support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were used to model the spectral data set, comparing the effects of multidimensional scattering correction and principal component analysis. 1DCNN performed best on the original spectral data, reaching an accurate recognition of 90.11%. According to the spectral changes of the seed germination, the first three hours of data were selected for prediction, which had higher recognition accuracy than the test set. The image-based 2DCNN model achieved 99.96% accurate recognition at a fast convergence speed. By differentiating the spectra and image information, the various CNN models can achieve accurate detection and prediction, providing a framework to advance research on seed germination. Hyperspectral imaging ; Vitality ; Artificial intelligence ; Estimation ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Remote sensing ; Engineering ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Mingbin Gao | Hua Li | Wenjuan Liu | Zhaochao Xu | Shichao Peng | Miao Yang | Mao Ye | Zhongmin Liu
Source: Nature Communications (article, 2020-07-20)
Field: Chemistry — Inorganic Chemistry

Imaging spatiotemporal evolution of molecules and active sites in zeolite catalyst during methanol-to-olefins reaction Direct visualization of spatiotemporal evolution of molecules and active sites during chemical transformation in individual catalyst crystal will accelerate the intuitive understanding of heterogeneous catalysis. So far, widespread imaging techniques can only provide limited information either with large probe molecules or in model catalyst of large size, which are beyond the interests of industrial catalysis. Herein, we demonstrate a feasible deep data approach via synergy of multiscale reaction-diffusion simulation and super-resolution structured illumination microscopy to illustrate the dynamical evolution of spatiotemporal distributions of gas molecules, carbonaceous species and acid sites in SAPO-34 zeolite crystals of several micrometers that are typically used in industrial methanol-to-olefins process. The profound insights into the inadequate utilization of activated acid sites and rapid deactivation are unveiled. The notable elucidation of molecular reaction-diffusion process at the scale of single catalyst crystal via this approach opens an interesting method for mechanism study in materials synthesis and catalysis. Catalysis ; Zeolite ; Molecule ; Crystal (programming language) ; Nanotechnology ; Materials science ; Methanol ; Diffusion ; Heterogeneous catalysis ; Chemistry ; Chemical physics ; Chemical engineering ; Organic chemistry ; Computer science ; Physics ; Thermodynamics Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: Liang Chen | Tiancong Zhao | Mengyao Zhao | Wenxing Wang | Caixia Sun | Lu Liu | Qin Li | Fan Zhang | Dongyuan Zhao | Xiaomin Li
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Biomaterials

Size and charge dual-transformable mesoporous nanoassemblies for enhanced drug delivery and tumor penetration A series of biological barriers in a nanoparticle-formulated drug delivery process inevitably result in the current low delivery efficiency, limited tumor penetration and insufficient cellular internalization of drugs. These multiple biological barriers are intimately related to the physicochemical properties of nanoparticles, especially the contradictory demand on size and surface charge for long blood circulation (larger and negative) and deep tumor penetration (smaller) as well as efficient cellular internalization (positive). Herein, we report tumor microenvironment triggered size and charge dual-transformable nanoassemblies. The nanoassembly is realized by immobilizing positive up/downconverting luminescent nanoparticles (U/DCNPs) onto large mesoporous silica nanoparticles (MSNs) <i>via</i> acid-labile bonds to form core@satellite structured MSN@U/DCNPs nanoassemblies, and subsequent capping of charge reversible polymers. At physiological pH, the integrated nanoassemblies with a larger size (∼180 nm) and negative charge can effectively achieve a prolonged blood circulation and high tumor accumulation. While under an acidic tumor microenvironment, the charge reversal of outer polymers and cleavage of linkers between MSNs and U/DCNPs can induce disintegration of the nanoassemblies into isolated MSNs and smaller U/DCNPs, both with a positively charged surface, which thereby potentiate the tumor penetration and cell uptake of dissociated nanoparticles. Combined with the independent near-infrared (NIR)-to-visible and NIR-to-NIR luminescence of U/DCNPs and high surface area of MSNs, the nanoassemblies can implement NIR bioimaging guided chemo- and photodynamic combined therapy with remarkable antitumor efficiency because of the high accumulation and deep tumor penetration induced by the dual transformability of the nanoassemblies. Drug delivery ; Penetration (warfare) ; Mesoporous material ; Dual (grammatical number) ; Nanotechnology ; Drug ; Dual role ; Materials science ; Chemistry ; Combinatorial chemistry ; Medicine ; Pharmacology ; Engineering ; Operations research Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis