jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iseult Lynch | Antreas Afantitis | Thomas E. Exner | Martin Himly | Vladimir Lobaskin | Philip Doganis | Dieter Maier | Natasha Sanabria | Anastasios G. Papadiamantis | Anna Rybińska‐Fryca | Maciej Gromelski | Tomasz Puzyn | Egon Willighagen | Blair D. Johnston | Mary Gulumian | Marianne Matzke | Amaia Green Etxabe | Nathan Bossa | Angela Serra | Irene Liampa | Stacey L. Harper | Kaido Tämm | Alexander CØ Jensen | Pekka Kohonen | Luke T. Slater | Andreas Tsoumanis | Dario Greco | David A. Winkler | Haralambos Sarimveis | Georgia Melagraki
Source: Nanomaterials (article, 2020-12-11)
Field: Materials Science — Materials Chemistry

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies? Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (<i>NInChI</i>). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested <i>NInChI</i> layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard. Cheminformatics ; Chemical nomenclature ; Nanomaterials ; Computer science ; Representation (politics) ; Nanotechnology ; ENCODE ; Surface modification ; Chemistry ; Materials science ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Luis Castillo Henríquez | Karla Alfaro-Aguilar | Jeisson Ugalde-Álvarez | Laura Vega-Fernández | Gabriela Montes de Oca-Vásquez | José Roberto Vega‐Baudrit
Source: Preprints.org (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

Green Synthesis of Metal Nanoparticles from Plant Extracts, and Their Possible Application as Antimicrobial Agents in the Agricultural Area Currently, metal nanoparticles have varied uses for different medical, pharmaceutical, and agricultural applications. Nano-biotechnology combined with green chemistry has great potential for the development of novel and necessary products that benefit human activities, while encourages the reduction of hazardous reagents for nanoparticle production. Green chemistry has an important role due to its contribution to unconventional synthesis methods of gold and silver nanoparticles from plant extracts, which have exhibited antimicrobial potential among other outstanding properties. Biodiversity-rich countries need to collect and convert knowledge from biological resources into processes, compounds, methods, and tools, which need to be achieved along with sustainable use and exploitation of biological diversity. Therefore, this review focuses on the importance of metal nanoparticles, the use of plant extract for their synthesis as well as other available methods, and the relevant antimicrobial activity that can be exploited in a sustainable model of agricultural management through a modern nanotechnological approach. Antimicrobial ; Agriculture ; Nanotechnology ; Biochemical engineering ; Green chemistry ; Nanoparticle ; Biotechnology ; Chemistry ; Engineering ; Materials science ; Biology ; Ecology ; Organic chemistry ; Catalysis Nanoparticles: synthesis and applications
Authors: Nanda V. Ranade | Sanjay Nagarajan | Varaha P. Sarvothaman | Vivek V. Ranade
Source: Ultrasonics Sonochemistry (article, 2020-12-28)
Field: Materials Science — Materials Chemistry

ANN based modelling of hydrodynamic cavitation processes: Biomass pre-treatment and wastewater treatment We have developed artificial neural network (ANN) based models for simulating two application examples of hydrodynamic cavitation (HC) namely, biomass pre-treatment to enhance biogas and degradation of organic pollutants in water. The first case reports data on influence of number of passes through HC reactor on bio-methane generation from bagasse. The second case reports data on influence of HC reactor scale on degradation of dichloroaniline (DCA). Similar to most of the HC based applications, the availability of experimental data for these two applications is rather limited. In this work a systematic methodology for developing ANN model is presented. The models were shown to describe the experimental data very well. The ANN models were then evaluated for their ability to interpolate and extrapolate. Despite the limited data, the ANN models were able to simulate and interpolate the data for two very different and complex HC applications very well. The extrapolated results of biomethane generation in terms of number of passes were consistent with the intuitive understanding. The extrapolated results in terms of elapsed time were however not consistent with the intuitive understanding. The ANN model was able to generate intuitively consistent extrapolated results for degradation of DCA in terms of number of passes as well as scale of HC reactor. The results will be useful for developing quantitative models of complex HC applications. Biomass (ecology) ; Biogas ; Artificial neural network ; Degradation (telecommunications) ; Experimental data ; Sewage treatment ; Environmental science ; Wastewater ; Computer science ; Process engineering ; Work (physics) ; Scale (ratio) ; Biological system ; Biochemical engineering ; Environmental engineering ; Waste management ; Machine learning ; Mathematics ; Engineering ; Mechanical engineering ; Physics ; Geology ; Statistics Ultrasound and Cavitation Phenomena ; Electrohydrodynamics and Fluid Dynamics ; Coal Combustion and Slurry Processing
Authors: Leixin Ouyang | Daniel Rosenmann | David A. Czaplewski | Jie Gao | Xiaodong Yang
Source: Nanotechnology (article, 2020-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband infrared circular dichroism in chiral metasurface absorbers Chirality is ubiquitous in nature and it is essential in many fields, but natural materials possess weak and narrow-band chiroptical effects. Here, chiral plasmonic metasurface absorbers are designed and demonstrated to achieve large broadband infrared circular dichroism (CD). The broadband chiral absorber is made of multiple double-rectangle resonators with different sizes, showing strong absorption of left-handed or right-handed circularly polarized (LCP or RCP) light above 0.7 and large CD in absorption more than 0.5 covering the wavelength range from 1.35 µm to 1.85 µm. High broadband polarization-dependent local temperature increase is also obtained. The switchable infrared reflective chiral images are further presented by changing the wavelength and polarization of incident light. The broadband chiral metasurface absorbers promise future applications in many areas such as polarization detection, thermophotovoltaics, and chiral imaging. Materials science ; Broadband ; Circular dichroism ; Circular polarization ; Optics ; Infrared ; Chirality (physics) ; Optoelectronics ; Polarization (electrochemistry) ; Plasmon ; Wavelength ; Vibrational circular dichroism ; Metamaterial ; Absorption (acoustics) ; Resonator ; Physics ; Chiral symmetry ; Crystallography Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Weiran Song | Zhiyuan Song | Jordan Vincent | Hui Wang | Zhe Wang
Source: Talanta (article, 2020-03-13)
Field: Chemistry — Analytical Chemistry

Quantification of extra virgin olive oil adulteration using smartphone videos Adulterant ; Chemometrics ; Chemistry ; Partial least squares regression ; Olive oil ; Edible oil ; Digital imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Digital image ; Image processing ; Food science ; Computer science ; Chromatography ; Machine learning ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Charlotte A. Softley | Mark J. Bostock | Grzegorz M. Popowicz | Michael Sattler
Source: Journal of Biomolecular NMR (review, 2020-06-10)
Field: Chemistry — Spectroscopy

Paramagnetic NMR in drug discovery Abstract The presence of an unpaired electron in paramagnetic molecules generates significant effects in NMR spectra, which can be exploited to provide restraints complementary to those used in standard structure-calculation protocols. NMR already occupies a central position in drug discovery for its use in fragment screening, structural biology and validation of ligand–target interactions. Paramagnetic restraints provide unique opportunities, for example, for more sensitive screening to identify weaker-binding fragments. A key application of paramagnetic NMR in drug discovery, however, is to provide new structural restraints in cases where crystallography proves intractable. This is particularly important at early stages in drug-discovery programs where crystal structures of weakly-binding fragments are difficult to obtain and crystallization artefacts are probable, but structural information about ligand poses is crucial to guide medicinal chemistry. Numerous applications show the value of paramagnetic restraints to filter computational docking poses and to generate interaction models. Paramagnetic relaxation enhancements (PREs) generate a distance-dependent effect, while pseudo-contact shift (PCS) restraints provide both distance and angular information. Here, we review strategies for introducing paramagnetic centers and discuss examples that illustrate the utility of paramagnetic restraints in drug discovery. Combined with standard approaches, such as chemical shift perturbation and NOE-derived distance information, paramagnetic NMR promises a valuable source of information for many challenging drug-discovery programs. Paramagnetism ; Drug discovery ; Chemistry ; Physics ; Condensed matter physics Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Electron Spin Resonance Studies
Authors: Jie Chen | Ting Qiu | Wei Yan | Charl F. J. Faul
Source: Journal of Materials Chemistry A (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Exploiting Hansen solubility parameters to tune porosity and function in conjugated microporous polymers We expand our recently reported Bristol–Xi'an Jiaotong (BXJ) approach using simple salts to fine tune the porosity of conjugated microporous materials, from Buchwald–Hartwig to Sonogashira–Hagihara, oxidative and Suzuki cross-coupling reactions. Conjugated microporous polymer ; Microporous material ; Porosity ; Sonogashira coupling ; Materials science ; Polymer ; Conjugated system ; Solubility ; Chemical engineering ; Polymer chemistry ; Organic chemistry ; Chemistry ; Composite material ; Palladium ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Subhabrata Mukhopadhyay | Olivia Basu | Rajendar Nasani | Samar K. Das
Source: Chemical Communications (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Evolution of metal organic frameworks as electrocatalysts for water oxidation In the last two decades, metal organic frameworks (MOFs) have been extensively investigated to develop heterogeneous electrocatalysts for water oxidation (WO). The scope of reticular synthesis, enormous surface area and accessible internal volume of MOFs make them promising candidates for catalysis. However, low electrical conductivity, slow mass transport and lack of stability restrict the scope of MOF-based WO. In recent times, various material designing approaches, e.g., the introduction of mixed metal and multi-metal systems, ligand engineering, guest@MOF composite formation, preparation of thin films, MOF composite formation with conducting carbon-based materials, metal oxides, polymers and layered compounds, etc. have emerged as an effective means to counteract the aforementioned limitations. This feature article critically discusses the common MOF-based material designing strategies with respect to electrochemical WO and provides a platform to understand the potential of MOFs to prepare a sophisticated hybrid electrocatalyst for WO. Metal-organic framework ; Electrocatalyst ; Nanotechnology ; Materials science ; Mass transport ; Electrochemistry ; Scope (computer science) ; Catalysis ; Chemistry ; Computer science ; Biochemical engineering ; Electrode ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Lars Banko | Yury Lysogorskiy | Dario Grochla | Dennis Naujoks | Ralf Drautz | Alfred Ludwig
Source: Communications Materials (article, 2020-03-26)
Field: Materials Science — Materials Chemistry

Predicting structure zone diagrams for thin film synthesis by generative machine learning Abstract Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties, their microstructure needs to be controlled in a multi-parameter space, which usually requires too high a number of experiments to map. Here, we propose to master thin film processing microstructure complexity, and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach using a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams that can be applied for the optimization of chemical composition and processing parameters to achieve a desired microstructure. Generative grammar ; Microstructure ; Computer science ; Artificial intelligence ; Generative model ; Generative Design ; Space (punctuation) ; Chemical space ; Diagram ; Generative adversarial network ; Deep learning ; Machine learning ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Wuxin Sha | Yaqing Guo | Qing Yuan | Shun Tang | Xinfang Zhang | Songfeng Lu | Xin Guo | Yuan‐Cheng Cao | Shijie Cheng
Source: Advanced Intelligent Systems (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence to Power the Future of Materials Science and Engineering AI in Materials Science As reviewed by Prof. Yuan-Cheng Cao and co-workers in article number 1900143, with the assistance of machine learning, the application of AI in materials science including property prediction, synthesis route planning, and processing optimization, has been boosted. The future development direction is expounded from both hardware and software aspects to power the future of materials science and engineering. Science and engineering ; Property (philosophy) ; Applications of artificial intelligence ; Computer science ; Power (physics) ; Software ; Engineering management ; Artificial intelligence ; Engineering ; Software engineering ; Engineering ethics Machine Learning in Materials Science
Authors: Jiang Wang | Stefan Chmiela | Klaus‐Robert Müller | Frank Noé | Cecilia Clementi
Source: The Journal of Chemical Physics (article, 2020-05-20)
Field: Materials Science — Materials Chemistry

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorithm. Here, we demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner. The CG force field is learned by following the thermodynamic consistency principle, here by minimizing the error between the predicted CG force and the all-atom mean force in the CG coordinates. Solving this problem by GDML directly is impossible because coarse-graining requires averaging over many training data points, resulting in impractical memory requirements for storing the kernel matrices. In this work, we propose a data-efficient and memory-saving alternative. Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective CG model. We illustrate our method on a simple biomolecular system, alanine dipeptide, by reconstructing the free energy landscape of a CG variant of this molecule. Our novel GDML training scheme yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when the training set is sufficiently large. Computer science ; Force field (fiction) ; Kernel (algebra) ; Granularity ; Ensemble learning ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Jinlai Zhang | Yanmei Meng | Jianfan Wu | Johnny Qin | Hui Wang | Yao Tao | Shuangshuang Yu
Source: Journal of Food Engineering (article, 2020-02-08)
Field: Chemistry — Analytical Chemistry

Monitoring sugar crystallization with deep neural networks Artificial intelligence ; Sugar ; Convolutional neural network ; Sugar cane ; Computer science ; Artificial neural network ; Sugar production ; Deep learning ; Automation ; Process (computing) ; Contextual image classification ; Machine learning ; Crystallization ; Cane ; Pattern recognition (psychology) ; Engineering ; Image (mathematics) ; Chemistry ; Agricultural engineering ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Sugarcane Cultivation and Processing
Authors: Giuseppe Ciaburro | Gino Iannace | Jessica Passaro | Aurelio Bifulco | Aniello Daniele Marano | Michele Guida | Francesco Marulo | F. Branda
Source: Applied Acoustics (article, 2020-06-20)
Field: Materials Science — Biomaterials

Artificial neural network-based models for predicting the sound absorption coefficient of electrospun poly(vinyl pyrrolidone)/silica composite Noise reduction coefficient ; Materials science ; Composite material ; Attenuation coefficient ; Electrospinning ; Composite number ; Correlation coefficient ; Artificial neural network ; Absorption (acoustics) ; Speed of sound ; Acoustics ; Polymer ; Porosity ; Optics ; Computer science Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Yuanjie Zhi | Tao Yang | Dongmei Fu
Source: Journal of Material Science and Technology (article, 2020-02-28)
Field: Materials Science — Materials Chemistry

An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels Random forest ; Corrosion ; Support vector machine ; Cascade ; Artificial neural network ; Relative humidity ; Variable (mathematics) ; Environmental science ; Process (computing) ; Artificial intelligence ; Computer science ; Data mining ; Machine learning ; Meteorology ; Materials science ; Metallurgy ; Engineering ; Mathematics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Wenling Ye | Chao Shen | Guo‐Li Xiong | Junjie Ding | Aiping Lü | Tingjun Hou | Dongsheng Cao
Source: Journal of Chemical Information and Modeling (article, 2020-04-30)
Field: Chemistry — Physical and Theoretical Chemistry

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein-Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein-ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design. Docking (animal) ; Virtual screening ; Protein–ligand docking ; Computer science ; Computational biology ; Chemistry ; Artificial intelligence ; Molecular dynamics ; Computational chemistry ; Biology ; Medicine Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research