jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongyu Xue | Yukang Gong | Zhaoyi Yang | Guohui Chuai | Sheng Qu | Aizong Shen | Jing Yu | Qi Liu
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2018-10-19)
Field: Materials Science — Materials Chemistry

Advances and challenges in deep generative models for de novo molecule generation The de novo molecule generation problem involves generating novel or modified molecular structures with desirable properties. Taking advantage of the great representation learning ability of deep learning models, deep generative models, which differ from discriminative models in their traditional machine learning approach, provide the possibility of generation of desirable molecules directly. Although deep generative models have been extensively discussed in the machine learning community, a specific investigation of the computational issues related to deep generative models for de novo molecule generation is needed. A concise and insightful discussion of recent advances in applying deep generative models for de novo molecule generation is presented, with particularly emphasizing the most important challenges for successful application of deep generative models in this specific area. This article is categorized under: Computer and Information Science &gt; Chemoinformatics Computer and Information Science &gt; Computer Algorithms and Programming Generative grammar ; Deep learning ; Cheminformatics ; Artificial intelligence ; Computer science ; Generative model ; Discriminative model ; Machine learning ; Representation (politics) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pengfei Gao | Guoliang Qin | X.X. Wang | Yuexuan Li | Mei Zhan | G.J. Li | Jinshan Li
Source: Materials Science and Engineering A (article, 2018-10-12)
Field: Materials Science — Materials Chemistry

Dependence of mechanical properties on the microstructural parameters of TA15 titanium alloy with tri-modal microstructure Materials science ; Microstructure ; Ultimate tensile strength ; Nucleation ; Plasticity ; Lamellar structure ; Alloy ; Composite material ; Elongation ; Titanium alloy ; Equiaxed crystals ; Void (composites) ; Metallurgy ; Chemistry Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Gajanan Pandey | Sandhya Singh | Gaurav Hitkari
Source: International nano letters. (article, 2018-05-25)
Field: Chemistry — Organic Chemistry

Synthesis and characterization of polyvinyl pyrrolidone (PVP)-coated Fe3O4 nanoparticles by chemical co-precipitation method and removal of Congo red dye by adsorption process The present paper deals synthesis of polyvinyl pyrrolidone (PVP)-coated magnetite nanoparticles by chemical co-precipitation method. The samples were characterized using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR) and Brunauer–Emmett–Teller (BET) methods. The surface area, pore volume and pore radius were calculated by the BET analysis. The EDX and FT-IR analysis demonstrated the fabrication of PVP-coated Fe3O4 NPs. The as-synthesized Fe3O4/PVP has successfully been used as an adsorbent for elimination of Congo red dye in aqueous medium. Congo red ; Materials science ; Fourier transform infrared spectroscopy ; Adsorption ; Chemical engineering ; Scanning electron microscope ; Precipitation ; Aqueous solution ; Nanochemistry ; Nanoparticle ; BET theory ; Magnetite ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Metallurgy Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal
Authors: Cameron J. Brown | Thomas McGlone | Stephanie Yerdelen | Vijay K. Srirambhatla | Fraser Adam Mabbott | Rajesh Gurung | Maria L. Briuglia | Bilal Ahmed | Hector Polyzois | John McGinty | Francesca Perciballi | Dimitris Fysikopoulos | Pól MacFhionnghaile | Humera Siddique | Vishal Raval | Tomás Seosamh Harrington | Antony D. Vassileiou | Murray N. Robertson | Elke Prasad | Andrea Johnston | Blair F. Johnston | Alison Nordon | Jagjit Singh Srai | Gavin Halbert | Joop H. ter Horst | C. Price | Chris D. Rielly | Ján Šefčı́k | Alastair J. Florence
Source: Molecular Systems Design & Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Enabling precision manufacturing of active pharmaceutical ingredients: workflow for seeded cooling continuous crystallisations Presentation and applied case study of a system-wide workflow which supports rapid, systematic and efficient continuous seeded cooling crystallisation process design, with the aim to deliver a robust, consistent process with tight control of particle attributes. Workflow ; Seeding ; Process engineering ; Process (computing) ; Computer science ; Manufacturing engineering ; Process management ; Engineering ; Database ; Operating system Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography
Authors: Yuhua Chang | Jingxuan Wei | Chengkuo Lee
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metamaterials – from fundamentals and MEMS tuning mechanisms to applications Abstract Metamaterials, consisting of subwavelength resonant structures, can be artificially engineered to yield desired response to electromagnetic waves. In contrast to the naturally existing materials whose properties are limited by their chemical compositions and structures, the optical response of metamaterials is controlled by the geometrics of resonant unit cells, called “meta‐atoms”. Many exotic functionalities such as negative refractive index, cloaking, perfect absorber, have been realized in metamaterials. One recent technical advance in this field is the active metamaterial, in which the structure of metamaterials can be tuned to realize multiple states in a single device. Microelectromechanical systems (MEMS) technology, well‐known for its ability of reconfiguring mechanical structures, complementary metal‐oxide‐semiconductor (CMOS) compatibility and low power consumption, is perfectly suitable for such purpose. In the past one decade, we have seen numerous exciting works endeavoring to incorporate the novel MEMS functionalities with metamaterials for widespread applications. In this review, we will first visit the fundamental theories of MEMS‐based active metamaterials, such as the lumped circuit model, coupled‐mode theory, and interference theory. Then, we summarize the recent applications of MEMS‐based metamaterials in various research fields. Finally, we provide an outlook on the future research directions of MEMS‐based metamaterials and their possible applications. Metamaterial ; Microelectromechanical systems ; Cloaking ; Transformation optics ; Computer science ; Materials science ; Nanotechnology ; Electronic engineering ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Domenico Cifá | Mihaela Skrt | Paola Pittia | Carla Di Mattia | Nataša Poklar Ulrih
Source: Food Science & Nutrition (article, 2018-04-17)
Field: Chemistry — Organic Chemistry

Enhanced yield of oleuropein from olive leaves using ultrasound‐assisted extraction The aim of this study was to optimize the extraction of oleuropein from olive leaves through a systematic study of the effects of different parameters of ultrasound-assisted extraction (USAE) on the oleuropein yield, in comparison with conventional maceration extraction. A range of operational parameters were investigated for both conventional maceration extraction and USAE: solvent type, olive leaf mass-to-solvent volume ratio, and extraction time and temperature. Oleuropein yield was determined using high-performance liquid chromatography, with total phenolics content also determined. The optimized conditions (water-ethanol, 30:70 [v/v]; leaf-to-solvent ratio, 1:5 [w/v]; 2 hr; 25°C) provided ~30% greater oleuropein extraction yield compared to conventional maceration extraction. The total phenolics content obtained using the optimized USAE conditions was greater than reported in other studies. USAE is shown to be an efficient alternative to conventional maceration extraction techniques, as not only can it offer increased oleuropein extraction yield, but it also shows a number of particular advantages, such as the possibility of lower volumes of solvent and lower extraction times, with the extraction carried out at lower temperatures. Oleuropein ; Yield (engineering) ; Extraction (chemistry) ; Olea ; Olive leaf ; Olive oil ; Ultrasound ; Chemistry ; Horticulture ; Pulp and paper industry ; Chromatography ; Botany ; Food science ; Biology ; Materials science ; Engineering ; Medicine ; Composite material Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Asma Nouira | Nataliya Sokolovska | Jean‐Claude Crivello
Source: arXiv (Cornell University) (preprint, 2018-10-26)
Field: Materials Science — Materials Chemistry

CrystalGAN: Learning to Discover Crystallographic Structures with\n Generative Adversarial Networks Our main motivation is to propose an efficient approach to generate novel\nmulti-element stable chemical compounds that can be used in real world\napplications. This task can be formulated as a combinatorial problem, and it\ntakes many hours of human experts to construct, and to evaluate new data.\nUnsupervised learning methods such as Generative Adversarial Networks (GANs)\ncan be efficiently used to produce new data. Cross-domain Generative\nAdversarial Networks were reported to achieve exciting results in image\nprocessing applications. However, in the domain of materials science, there is\na need to synthesize data with higher order complexity compared to observed\nsamples, and the state-of-the-art cross-domain GANs can not be adapted\ndirectly. In this contribution, we propose a novel GAN called CrystalGAN which\ngenerates new chemically stable crystallographic structures with increased\ndomain complexity. We introduce an original architecture, we provide the\ncorresponding loss functions, and we show that the CrystalGAN generates very\nreasonable data. We illustrate the efficiency of the proposed method on a real\noriginal problem of novel hydrides discovery that can be further used in\ndevelopment of hydrogen storage materials.\n Generative grammar ; Adversarial system ; Computer science ; Artificial intelligence ; Cognitive science ; Psychology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Iason Papademetriou | Else M. Vedula | Joseph L. Charest | Tyrone M. Porter
Source: PLoS ONE (article, 2018-10-09)
Field: Materials Science — Biomaterials

Effect of flow on targeting and penetration of angiopep-decorated nanoparticles in a microfluidic model blood-brain barrier The blood-brain barrier (BBB) limits transport of nanoparticles from the circulation to the brain parenchyma. Angiopep-2, a peptide which functions as a brain transport vector, can be coupled to nanoparticles in order to facilitate binding and internalization by brain endothelial cells (ECs), and subsequent BBB penetration. This multi-step process may be affected by blood flow over brain ECs, as flow influences endothelial cell phenotype as well as interactions of nanoparticles with ECs. In the present study a microfluidic BBB model was constructed to evaluate binding and internalization by brain ECs, as well as BBB penetration of Angiopep-2 coupled liposomes (Ang2-Liposomes) in static and flow conditions. Ang2 conjugation to liposomes markedly improved binding relative to unconjugated liposomes. Ang2-Liposomes bound and were internalized efficiently by brain endothelial cells after static incubation or with 1 dyne/cm2 of fluid shear stress (FSS), while binding was reduced at a FSS of 6 dyne/cm2. Penetration of the model microfluidic BBB by Ang2-Liposomes was higher at a FSS of 1 dyne/cm2 and 6 dyne/cm2 than with static incubation. Analysis of barrier function and control experiments for receptor-mediated penetration provided insight into the magnitude of transcellular versus paracellular transport at each tested FSS. Overall, the results demonstrate that flow impacted the binding and BBB penetration of Ang2-functionalized nanoparticles. This highlights the relevance of the local flow environment for in vitro modeling of the performance of nanoparticles functionalized with BBB penetrating ligands. Paracellular transport ; Penetration (warfare) ; Liposome ; Blood–brain barrier ; Internalization ; Biophysics ; Transcellular ; Chemistry ; Tight junction ; Receptor ; Biology ; Biochemistry ; Membrane ; Central nervous system ; Permeability (electromagnetism) ; Neuroscience Nanoparticle-Based Drug Delivery ; Barrier Structure and Function Studies ; Advanced Drug Delivery Systems
Authors: Liping Guo | Baiyu Zhang | Ying Qin | Dawen Li | Lin Li | Xiaofeng Qian | Feng Yan
Source: Solar RRL (article, 2018-06-29)
Field: Materials Science — Materials Chemistry

Tunable Quasi‐One‐Dimensional Ribbon Enhanced Light Absorption in Sb<sub>2</sub>Se<sub>3</sub>Thin‐film Solar Cells Grown by Close‐Space Sublimation The non‐cubic antimony chalcogenides, i.e., Sb 2 Se 3 , formed by quasi‐one‐dimensional ribbons can enhance light absorption and carrier transport by tuning the ribbon direction using the close‐space sublimation (CSS) deposition. The improved device performance is found to be associated with the ribbon direction, which was investigated with theoretical calculation and experimental optical measurement in the Sb 2 Se 3 films and devices. Decent device efficiency could be achieved when the ribbons were tuned to be as normal to the substrate as possible. The substrate temperature and film thickness are critical for the fine‐tuning of ribbon orientations during the CSS deposition. Our results show that [211]‐preferred orientation leads to the minimum series resistance and highest light absorbance in the device. This observation demonstrates that Sb 2 Se 3 ‐like quasi‐one‐dimensional materials with van der Waals boundaries can achieve scalable production at low cost and hold great potential for next‐generation solar cell using the recently developed vapor transport deposition technology. Sublimation (psychology) ; Ribbon ; Materials science ; Optoelectronics ; Antimony ; Solar cell ; Thin film ; Absorbance ; Substrate (aquarium) ; Absorption (acoustics) ; Heterojunction ; Deposition (geology) ; Optics ; Nanotechnology ; Composite material Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David A. Winkler | Michael J. Ford
Source: Advanced Theory and Simulations (article, 2018-10-31)
Field: Materials Science — Materials Chemistry

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches Abstract There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work. However, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. A combination of density functional theory (DFT) and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiments. As a proof of concept, this methodology is applied to predict the interlayer distance and band gap of bilayer heterostructures. The methods quickly and accurately predict these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of vdW heterostructures to identify new hybrid materials with useful and interesting properties. Density functional theory ; Computer science ; Key (lock) ; van der Waals force ; Heterojunction ; Space (punctuation) ; Parameter space ; Work (physics) ; Hybrid functional ; Artificial intelligence ; Machine learning ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Fan Feng | Luhua Lai | Jianfeng Pei
Source: Frontiers in Chemistry (review, 2018-06-04)
Field: Materials Science — Materials Chemistry

Computational Chemical Synthesis Analysis and Pathway Design With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules. Programs like ARChem Route Designer replaced hand-coded reaction modes with automatically-extracted rules, and programs like Chematica changed traditional designing into network searching. Afterward, with the help of machine learning, two-step models which combine reaction rules and statistical methods became the main stream. Recently, fully data-driven learning methods using deep neural networks which even do not require any prior knowledge, were applied into this field. Up to now, however, these methods still cannot replace experienced human organic chemists due to their relatively low accuracies. Future new algorithms with the aid of powerful computational hardware will make this topic promising and with good prospects. Retrosynthetic analysis ; Computer science ; Field (mathematics) ; Artificial intelligence ; Process (computing) ; Artificial neural network ; Machine learning ; Data science ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Metabolic Engineering and Bioproduction
Authors: Run Han | Yilong Yang | Xiaoshan Li | Defang Ouyang
Source: Asian Journal of Pharmaceutical Sciences (article, 2018-02-02)
Field: Chemistry — Analytical Chemistry

Predicting oral disintegrating tablet formulations by neural network techniques Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product. Artificial neural network ; Computer science ; Medicine ; Artificial intelligence Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Carlos García Núñez | Fengyuan Liu | William Taube Navaraj | Adamos Christou | Dhayalan Shakthivel | Ravinder Dahiya
Source: Microsystems & Nanoengineering (article, 2018-08-10)
Field: Materials Science — Materials Chemistry

Heterogeneous integration of contact-printed semiconductor nanowires for high-performance devices on large areas In this work, we have developed a contact-printing system to efficiently transfer the bottom-up and top-down semiconductor nanowires (NWs), preserving their as-grown features with a good control over their electronic properties. In the close-loop configuration, the printing system is controlled with parameters such as contact pressure and sliding speed/stroke. Combined with the dry pre-treatment of the receiver substrate, the system prints electronic layers with high NW density (7 NWs/μm for bottom-up ZnO and 3 NWs/μm for top-down Si NWs), NW transfer yield and reproducibility. We observed compactly packed (~115 nm average diameters of NWs, with NW-to-NW spacing ~165 nm) and well-aligned NWs (90% with respect to the printing direction). We have theoretically and experimentally analysed the role of contact force on NW print dynamics to investigate the heterogeneous integration of ZnO and Si NWs over pre-selected areas. Moreover, the contact-printing system was used to fabricate ZnO and Si NW-based ultraviolet (UV) photodetectors (PDs) with Wheatstone bridge (WB) configuration on rigid and flexible substrates. The UV PDs based on the printed ensemble of NWs demonstrate high efficiency, a high photocurrent to dark current ratio (>10<sup>4</sup>) and reduced thermal variations as a result of inherent self-compensation of WB arrangement. Due to statistically lesser dimensional variations in the ensemble of NWs, the UV PDs made from them have exhibited uniform response. Nanowire ; Materials science ; Transfer printing ; Photocurrent ; Optoelectronics ; Photodetector ; Semiconductor ; Nanosensor ; Wheatstone bridge ; Substrate (aquarium) ; Nanotechnology ; Contact resistance ; Contact print ; Composite material ; Layer (electronics) ; Electrical engineering ; Resistor ZnO doping and properties ; Nanowire Synthesis and Applications ; Advanced Sensor and Energy Harvesting Materials