jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon Zank | Jesús M. Fernández‐García | Anton J. Stasyuk | Alexander A. Voityuk | Marcel Krug | Miquel Solà | Dirk M. Guldi | Nazario Martı́n
Source: Angewandte Chemie International Edition (article, 2021-10-11)
Field: Chemistry — Organic Chemistry

Initiating Electron Transfer in Doubly Curved Nanographene Upon Supramolecular Complexation of C<sub>60</sub> The formation of supramolecular complexes between C<sub>60</sub> and a molecular nanographene endowed with both positive and negative curvatures is described. The presence of a corannulene moiety and the saddle shape of the molecular nanographene allows the formation of complexes with 1:1, 1:2, and 2:1 stoichiometries. The association constants for the three possible supramolecular complexes were determined by <sup>1</sup> H NMR titration. Furthermore, the stability of the three complexes was calculated by theoretical methods that also predict the photoinduced electron transfer from the curved nanographene to the electron acceptor C<sub>60</sub> . Time-resolved transient absorption measurements on the ns-time scale showed that the addition of C<sub>60</sub> to NG-1 solutions and photo-exciting them at 460 nm leads to the solvent-dependent formation of new species, in particular the formation of the one-electron reduced form of C<sub>60</sub> in benzonitrile was observed. Electron transfer ; Supramolecular chemistry ; Materials science ; Electron ; Transfer (computing) ; Crystallography ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Photochemistry ; Computer science ; Crystal structure ; Quantum mechanics Fullerene Chemistry and Applications ; Graphene research and applications ; Synthesis and Properties of Aromatic Compounds
Authors: William Bort | Igor I. Baskin | Timur Gimadiev | Artem Mukanov | Ramil Nugmanov | Pavel Sidorov | Gilles Marcou | Dragos Horvath | Olga Klimchuk | Timur Madzhidov | Alexandre Varnek
Source: Scientific Reports (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Discovery of novel chemical reactions by deep generative recurrent neural network The "creativity" of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that "creative" AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed "SMILES/CGR" strings, encoding reactions of the USPTO database. The autoencoder latent space was visualized on a generative topographic map. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways. Autoencoder ; Computer science ; Artificial intelligence ; Chemical space ; Generative grammar ; Sequence (biology) ; Space (punctuation) ; Encoding (memory) ; Artificial neural network ; Class (philosophy) ; Recurrent neural network ; Creativity ; Deep learning ; Generative model ; Cognitive science ; Pattern recognition (psychology) ; Bioinformatics ; Biology ; Drug discovery ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: H. Stärk | Dominique Beaini | Gabriele Corso | Prudencio Tossou | Christian Dallago | Stephan Günnemann | Píetro Lió
Source: arXiv (Cornell University) (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

3D Infomax improves GNNs for Molecular Property Prediction Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces. Computer science ; Infomax ; Mutual information ; Artificial intelligence ; Property (philosophy) ; Range (aeronautics) ; Machine learning ; Graph ; Scale (ratio) ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Shuaihua Lu | Qionghua Zhou | Yilv Guo | Jinlan Wang
Source: Chem (article, 2021-12-03)
Field: Materials Science — Materials Chemistry

On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature Curie temperature ; Ferromagnetism ; Condensed matter physics ; Curie ; Materials science ; Artificial intelligence ; Physics ; Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Condensed Matter Physics
Authors: Mohammad-Reza Mohammadi | Abdolhossein Hemmati‐Sarapardeh | Mahin Schaffie | Maen M. Husein | Mohammad Ranjbar
Source: Journal of Petroleum Science and Engineering (article, 2021-04-26)
Field: Chemistry — Analytical Chemistry

Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery Backpropagation ; Conjugate gradient method ; Asphaltene ; Artificial neural network ; Pyrolysis ; Residual ; Biological system ; Computer science ; Mathematics ; Algorithm ; Chemistry ; Artificial intelligence ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: R. K. Haghi | E. Pérez-Fernández | A. H. Jean Robertson
Source: Geoderma (article, 2021-04-09)
Field: Chemistry — Analytical Chemistry

Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: A comparison of near infrared and mid-infrared spectroscopy Attenuated total reflection ; Fourier transform infrared spectroscopy ; Partial least squares regression ; Mean squared error ; Diffuse reflectance infrared fourier transform ; Spectroscopy ; Near-infrared spectroscopy ; Linear regression ; Support vector machine ; Soil science ; Environmental science ; Mathematics ; Artificial intelligence ; Chemistry ; Computer science ; Statistics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Soil Geostatistics and Mapping
Authors: Yasmin Lima Brasil | J.P. Cruz‐Tirado | Douglas Fernandes Barbin
Source: Food Control (article, 2021-07-12)
Field: Chemistry — Analytical Chemistry

Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning Quail ; Partial least squares regression ; Yolk ; Haugh unit ; Support vector machine ; Linear discriminant analysis ; Computer science ; Statistics ; Spectrometer ; Quality (philosophy) ; Cross-validation ; Artificial intelligence ; Mathematics ; Machine learning ; Food science ; Chemistry ; Biology ; Ecology ; Body weight ; Feed conversion ratio ; Physics Spectroscopy and Chemometric Analyses ; Animal Nutrition and Physiology ; Identification and Quantification in Food
Authors: G.B.V.S. Lakshmi | Amit K. Yadav | Neha Mehlawat | Rekha Jalandra | Pratima R. Solanki | Anil Kumar
Source: Scientific Reports (article, 2021-01-14)
Field: Chemistry — Analytical Chemistry

Gut microbiota derived trimethylamine N-oxide (TMAO) detection through molecularly imprinted polymer based sensor Trimethylamine N-oxide (TMAO), a microbiota-derived metabolite has been implicated in human health and disease. Its early detection in body fluids has been presumed to be significant in understanding the pathogenesis and treatment of many diseases. Hence, the development of reliable and rapid technologies for TMAO detection may augment our understanding of pathogenesis and diagnosis of diseases that TMAO has implicated. The present work is the first report on the development of a molecularly imprinted polymer (MIP) based electrochemical sensor for sensitive and selective detection of TMAO in body fluids. The MIP developed was based on the polypyrrole (PPy), which was synthesized via chemical oxidation polymerization method, with and without the presence of TMAO. The MIP, NIP and the non-sonicated polymer (PPy-TMAO) were separately deposited electrophoretically onto the hydrolyzed indium tin oxide (ITO) coated glasses. The chemical, morphological, and electrochemical behavior of MIP, non-imprinted polymer (NIP), and PPy-TMAO were characterized using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and electrochemical techniques. The detection response was recorded using differential pulse voltammetry (DPV), which revealed a decrease in the peak current with the increase in concentration of TMAO. The MIP sensor showed a dynamic detection range of 1-15 ppm with a sensitivity of 2.47 µA mL ppm<sup>-1</sup> cm<sup>-2</sup>. The developed sensor is easy to construct and operate and is also highly selective to detect TMAO in body fluids such as urine. The present research provides a basis for innovative strategies to develop sensors based on MIP to detect other metabolites derived from gut microbiota that are implicated in human health and diseases. Trimethylamine N-oxide ; Differential pulse voltammetry ; Molecularly imprinted polymer ; Polypyrrole ; Trimethylamine ; Electrochemical gas sensor ; Cyclic voltammetry ; NIP ; Indium tin oxide ; Polymerization ; Fourier transform infrared spectroscopy ; Materials science ; Polymer ; Conductive polymer ; Chemistry ; Electrochemistry ; Nanotechnology ; Chemical engineering ; Electrode ; Organic chemistry ; Selectivity ; Layer (electronics) Analytical chemistry methods development ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Ahmed Gamal | Kamel Eid | Muftah H. El‐Naas | Dharmesh Kumar | Anand Kumar
Source: Nanomaterials (review, 2021-05-06)
Field: Materials Science — Materials Chemistry

Catalytic Methane Decomposition to Carbon Nanostructures and COx-Free Hydrogen: A Mini-Review Catalytic methane decomposition (CMD) is a highly promising approach for the rational production of relatively CO<sub>x</sub>-free hydrogen and carbon nanostructures, which are both important in multidisciplinary catalytic applications, electronics, fuel cells, etc. Research on CMD has been expanding in recent years with more than 2000 studies in the last five years alone. It is therefore a daunting task to provide a timely update on recent advances in the CMD process, related catalysis, kinetics, and reaction products. This mini-review emphasizes recent studies on the CMD process investigating self-standing/supported metal-based catalysts (e.g., Fe, Ni, Co, and Cu), metal oxide supports (e.g., SiO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, and TiO<sub>2</sub>), and carbon-based catalysts (e.g., carbon blacks, carbon nanotubes, and activated carbons) alongside their parameters supported with various examples, schematics, and comparison tables. In addition, the review examines the effect of a catalyst's shape and composition on CMD activity, stability, and products. It also attempts to bridge the gap between research and practical utilization of the CMD process and its future prospects. Catalysis ; Carbon fibers ; Methane ; Decomposition ; Hydrogen ; Nanotechnology ; Materials science ; Carbon nanotube ; Oxide ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite number ; Engineering ; Composite material ; Metallurgy Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Mark Geppert | Martin Himly
Source: Frontiers in Immunology (review, 2021-06-15)
Field: Materials Science — Biomaterials

Iron Oxide Nanoparticles in Bioimaging – An Immune Perspective Iron oxide nanoparticles (IONPs) bear big hopes in nanomedicine due to their (potential) applications in tumor therapy, drug delivery or bioimaging. However, as foreign entities, such particles may be recognized by the immune system and, thus, lead to inflammation, hypersensitivity or anaphylactic shock. In addition, an overload with iron is known to cause oxidative stress. In this short review, we summarize the biological effects of such particles with a major focus on IONP-formulations used for bioimaging purposes and their effects on the human immune system. We conclude that especially the characteristics of the particles (size, shape, surface charge, coating, <i>etc.</i>) as well as the presence of bystander substances, such as bacterial endotoxin are important factors determining the resulting biological and immunological effects of IONPs. Further studies are needed in order to establish clear structure-activity relationships. Immune system ; Iron oxide nanoparticles ; Nanomedicine ; Nanotechnology ; Drug delivery ; Nanoparticle ; Materials science ; Medicine ; Chemistry ; Immunology Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Extracellular vesicles in disease
Authors: Huizhi Lu | LI Xun-lai | Qingquan Lei
Source: Frontiers in Chemistry (review, 2021-09-06)
Field: Materials Science — Polymers and Plastics

Conjugated Conductive Polymer Materials and its Applications: A Mini-Review Since their discovery 50 years ago, conjugated conducting polymers have received increasing attention owing to their unique conductive properties and potential applications in energy storage, sensors, coatings, and electronic devices such as organic field-effect transistors, photovoltaic cells, and light-emitting devices. Recently, these materials have played a key role in providing a more comfortable environment for humans. Consequently, the development of novel, high-performance conjugated conductive materials is crucial. In this mini-review, the progress of conjugated conductive materials in various applications and the relationship between the chemical structures and their performances is reviewed. This can aid in the molecular design and development of novel high-performance conjugated polymer materials. Conjugated system ; Materials science ; Nanotechnology ; Conductive polymer ; Electrical conductor ; Polymer ; Photovoltaic system ; Transistor ; Electrical engineering ; Composite material ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Manuel Cordova | M. Balodis | Albert Hofstetter | Federico M. Paruzzo | Sten O. Nilsson Lill | Emma Eriksson | Pierrick Berruyer | Bruno Simões de Almeida | Michael J. Quayle | Stefan T. Norberg | Anna Svensk Ankarberg | Staffan Schantz | Lyndon Emsley
Source: Nature Communications (article, 2021-05-20)
Field: Chemistry — Spectroscopy

Structure determination of an amorphous drug through large-scale NMR predictions Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state nuclear magnetic resonance (NMR) is among the most popular methods to characterize amorphous materials, and molecular dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies. Amorphous solid ; Molecular dynamics ; Atomic units ; Intermolecular force ; Solid-state nuclear magnetic resonance ; Chemical physics ; Molecular solid ; Materials science ; Chemical shift ; Nuclear magnetic resonance ; Molecule ; Computational chemistry ; Chemistry ; Crystallography ; Physics ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Sarah Akers | Elizabeth J. Kautz | Andrea Trevino-Gavito | Matthew J. Olszta | Bethany E. Matthews | Le Wang | Yingge Du | Steven R. Spurgeon
Source: npj Computational Materials (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms. Instrumentation (computer programming) ; Computer science ; Segmentation ; Artificial intelligence ; Feature (linguistics) ; Materials science ; Shot (pellet) ; Throughput ; Image processing ; Transmission electron microscopy ; Machine learning ; Nanotechnology ; Computer vision ; Image (mathematics) Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Xin Ma | Mathilde Lepoitevin | Christian Serre
Source: Materials Chemistry Frontiers (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Metal–organic frameworks towards bio-medical applications This mini review summarises the progress in the field of MOFs and their use in biomedical applications, from their early discovery and conception, to more recent achievements including promising <italic>in vivo</italic> applications. Nanotechnology ; Metal-organic framework ; Data science ; Engineering ethics ; Computer science ; Materials science ; Engineering ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Aysel Tomak | Selin Cesmeli | Bercem D. Hanoglu | David A. Winkler | Ceyda Öksel Karakuş
Source: Nanotoxicology (review, 2021-11-26)
Field: Materials Science — Biomaterials

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from <i>in vitro</i> studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components. Corona (planetary geology) ; Nanotechnology ; Nanoparticle ; Biodistribution ; Biological fluids ; Nanomaterials ; Biophysics ; Protein adsorption ; Materials science ; Chemistry ; In vitro ; Biology ; Adsorption ; Biochemistry ; Chromatography Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Nanoparticles: synthesis and applications