jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthias Ferger | Sarina M. Berger | Florian Rauch | Markus Schönitz | Jessica Rühe | Johannes Krebs | Alexandra Friedrich | Todd B. Marder
Source: Chemistry - A European Journal (article, 2021-04-12)
Field: Chemistry — Organic Chemistry

Synthesis of Highly Functionalizable Symmetrically and Unsymmetrically Substituted Triarylboranes from Bench‐Stable Boron Precursors A novel and convenient methodology for the one-pot synthesis of sterically congested triarylboranes by using bench-stable aryltrifluoroborates as the boron source is reported. This procedure gives systematic access to symmetrically and unsymmetrically substituted triarylboranes of the types BAr<sub>2</sub> Ar' and BArAr'Ar'', respectively. Three unsymmetrically substituted triarylboranes as well as their iridium-catalyzed C-H borylation products are reported. These borylated triarylboranes contain one to three positions that can subsequently be orthogonally functionalized in follow-up reactions, such as Suzuki-Miyaura cross-couplings or Sonogashira couplings. Borylation ; Sonogashira coupling ; Iridium ; Boron ; Chemistry ; Combinatorial chemistry ; Steric effects ; Catalysis ; Polymer chemistry ; Organic chemistry ; Palladium Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Catalytic Cross-Coupling Reactions
Authors: Sofia Caspani | Suellen Moraes | D. Navas | Mariana P. Proença | Ricardo Magalhães | Cláudia Nunes | João P. Araújo | C. T. Sousa
Source: Nanomaterials (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

The Magnetic Properties of Fe/Cu Multilayered Nanowires: The Role of the Number of Fe Layers and Their Thickness Multi-segmented bilayered Fe/Cu nanowires have been fabricated through the electrodeposition in porous anodic alumina membranes. We have assessed, with the support of micromagnetic simulations, the dependence of fabricated nanostructures' magnetic properties either on the number of Fe/Cu bilayers or on the length of the magnetic layers, by fixing both the nonmagnetic segment length and the wire diameter. The magnetic reversal, in the segmented Fe nanowires (NWs) with a 300 nm length, occurs through the nucleation and propagation of a vortex domain wall (V-DW) from the extremities of each segment. By increasing the number of bilayers, the coercive field progressively increases due to the small magnetostatic coupling between Fe segments, but the coercivity found in an Fe continuous nanowire is not reached, since the interactions between layers is limited by the Cu separation. On the other hand, Fe segments 30 nm in length have exhibited a vortex configuration, with around 60% of the magnetization pointing parallel to the wires' long axis, which is equivalent to an isolated Fe nanodisc. By increasing the Fe segment length, a magnetic reversal occurred through the nucleation and propagation of a V-DW from the extremities of each segment, similar to what happens in a long cylindrical Fe nanowire. The particular case of the Fe/Cu bilayered nanowires with Fe segments 20 nm in length revealed a magnetization oriented in opposite directions, forming a synthetic antiferromagnetic system with coercivity and remanence values close to zero. Coercivity ; Materials science ; Nanowire ; Nucleation ; Remanence ; Condensed matter physics ; Magnetization ; Nanodisc ; Domain wall (magnetism) ; Magnetic field ; Nanotechnology ; Membrane ; Chemistry ; Lipid bilayer Anodic Oxide Films and Nanostructures ; Magnetic properties of thin films ; Nanoporous metals and alloys
Authors: Lillian L. Lou | John C. Martin
Source: Molecules (article, 2021-02-07)
Field: Chemistry — Organic Chemistry

Selected Thoughts on Hydrophobicity in Drug Design The fundamental aim of drug design in research and development is to invent molecules with selective affinity towards desired disease-associated targets. At the atomic loci of binding surfaces, systematic structural variations can define affinities between drug candidates and biomolecules, and thereby guide the optimization of safety, efficacy and pharmacologic properties. Hydrophobic interaction between biomolecules and drugs is integral to binding affinity and specificity. Examples of antiviral drug discovery are discussed. Biomolecule ; Binding affinities ; Drug ; Affinities ; Computational biology ; Drug discovery ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Pharmacology ; Stereochemistry ; Biology ; Biochemistry ; Materials science ; Receptor Click Chemistry and Applications ; Monoclonal and Polyclonal Antibodies Research ; HIV/AIDS drug development and treatment
Authors: Qian Chen | Yunhai Xiong | Xiang Chen
Source: Computational Materials Science (article, 2021-08-05)
Field: Materials Science — Materials Chemistry

Directed graph attention neural network utilizing 3D coordinates for molecular property prediction Computer science ; Artificial neural network ; Pairwise comparison ; Theoretical computer science ; Molecular graph ; Artificial intelligence ; Graph ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: José Antonio Garrido Torres | Vahe Gharakhanyan | Nongnuch Artrith | Tobias Hoffmann Eegholm | Alexander Urban
Source: Nature Communications (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available. Kriging ; Process (computing) ; Gaussian process ; Computer science ; Thermodynamics ; Gaussian ; Statistical physics ; Materials science ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Jiaqian Yang | Zhiping Ye | Guanjie Wang | Liang Zhao | Xiuwen Zhang | Jiade Wang | Yufan Jin
Source: Applied Catalysis A General (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Neuro-genetic machine learning framework accelerates the optimization of Ag/MnOx catalyst for total oxidation of toluene Calcination ; Toluene ; Catalysis ; X-ray photoelectron spectroscopy ; Redox ; Manganese ; Catalytic oxidation ; Response surface methodology ; Materials science ; Oxidation state ; Raman spectroscopy ; Yield (engineering) ; Precipitation ; Nuclear chemistry ; Chemistry ; Chemical engineering ; Inorganic chemistry ; Metallurgy ; Organic chemistry ; Chromatography Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Xin Bai | Xin Guo | Linjun Wang
Source: The Journal of Physical Chemistry Letters (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the intermolecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. In particular, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus, this possesses unique advantages in many applications. Diabatic ; Electronic structure ; Coupling (piping) ; Molecular orbital ; Intermolecular force ; Atomic orbital ; Molecular dynamics ; Physics ; Chemistry ; Molecule ; Electron ; Molecular physics ; Chemical physics ; Statistical physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Spectroscopy and Quantum Chemical Studies
Authors: Luke Gundry | Gareth F. Kennedy | Alan M. Bond | Jie Zhang
Source: Faraday Discussions (article, 2021-10-06)
Field: Chemistry — Electrochemistry

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms The use of deep neural networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvements, relative to the single cycle training, in achieving the correct classification of E, EC<sub>1<sup>st</sup></sub> and EC<sub>2<sup>nd</sup></sub> mechanisms (E = electron transfer step and C<sub>1<sup>st</sup></sub> and C<sub>2<sup>nd</sup></sub> are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed. Artificial neural network ; Computer science ; Deep neural networks ; Artificial intelligence ; Mechanism (biology) ; Training set ; Machine learning ; Pattern recognition (psychology) ; Biological system ; Chemistry ; Physics Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: S. Ramesh | Gajanan Anne | Nagaraj Bhat | Ganesh Aithal | H. Shivananda Nayaka | Shashibhushan Arya
Source: Journal of Manufacturing Processes (article, 2021-06-05)
Field: Materials Science — Biomaterials

Surface modification of multi-directional forged biodegradable Mg-Zn alloy by ball burnishing process: Modeling and analysis using deep neural network Materials science ; Burnishing (metal) ; Indentation hardness ; Microstructure ; Alloy ; Ball (mathematics) ; Surface roughness ; Surface finish ; Metallurgy ; Scanning electron microscope ; Composite material ; Ultimate tensile strength Magnesium Alloys: Properties and Applications ; Surface Treatment and Residual Stress ; Metal and Thin Film Mechanics
Authors: Anoop Chakkingal | Pieter Janssens | Jeroen Poissonnier | Alan J. Barrios | Mirella Virginie | Andreï Y. Khodakov | Joris Thybaut
Source: Reaction Chemistry & Engineering (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Machine learning based interpretation of microkinetic data: a Fischer–Tropsch synthesis case study A systematic approach for analysing kinetic data and identifying hidden trends using interpretation techniques in data science with the ANN. Fischer–Tropsch process ; Interpretation (philosophy) ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: Marianne Lahnsteiner | Michael Caldera | Hipassia M. Moura | D. Alonso Cerrón-Infantes | Jérôme Roeser | Thomas Konegger | Arne Thomas | Jörg Menche | Miriam M. Unterlass
Source: Journal of Materials Chemistry A (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation We report on the hydrothermal polymerization (HTP) of polyimide (PI) networks using the medium H<sub>2</sub>O and the comonomers 1,3,5-tris(4-aminophenyl)benzene (TAPB) and pyromellitic acid (PMA). Full condensation is obtained at minimal reaction times of only 2 h at 200 °C. The PI networks are obtained as monoliths and feature thermal stabilities of >500 °C, and in several cases even up to 595 °C. The monoliths are built up by networks of densely packed, near-monodisperse spherical particles and annealed microfibers, and show three types of porosity: (i) intrinsic inter-segment ultramicroporosity (<0.8 nm) of the PI networks composing the particles (∼3-5 μm), (ii) interstitial voids between the particles (0.1-2 μm), and (iii) monolith cell porosity (∽10-100 μm), as studied <i>via</i> low pressure gas physisorption and Hg intrusion porosimetry analyses. This unique hierarchical porosity generates an outstandingly high specific pore volume of 7250 mm<sup>3</sup> g<sup>-1</sup>. A large-scale micromorphological study screening the reaction parameters time, temperature, and the absence/presence of the additive acetic acid was performed. Through expert interpretation of hundreds of scanning electron microscopy (SEM) images of the products of these experiments, we devise a hypothesis for morphology formation and evolution: a monomer salt is initially formed and subsequently transformed to overall eight different fiber, pearl chain, and spherical morphologies, composed of PI and, at long reaction times (>48 h), also PI/SiO<sub>2</sub> hybrids that form through reaction with the reaction vessel. Moreover, we have developed a computational image analysis pipeline that deciphers the complex morphologies of these SEM images automatically and also allows for formulating a hypothesis of morphology development in HTP that is in good agreement with the manual morphology analysis. Finally, we upscaled the HTP of PI(TAPB-PMA) and processed the resulting powder into dense cylindrical specimen by green solvent-free warm-pressing, showing that one can follow the full route from the synthesis of these PI networks to a final material without employing harmful solvents. Materials science ; Polymerization ; Porosity ; Chemical engineering ; Porosimetry ; Nanoporous ; Dispersity ; Polymer chemistry ; Composite material ; Polymer ; Nanotechnology ; Porous medium Covalent Organic Framework Applications ; Synthesis and properties of polymers ; Polymer composites and self-healing
Authors: Fatemah Mukadum | Quan Nguyen | Daniel Adrion | Gabriel Appleby | Rui Chen | Haley Dang | Remco Chang | Roman Garnett | Steven A. Lopez
Source: Journal of Chemical Information and Modeling (article, 2021-11-09)
Field: Materials Science — Materials Chemistry

Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search Photoswitches are molecules that undergo a reversible, structural isomerization after exposure to certain wavelengths of light. The dynamic control offered by molecular photoswitches is favorable for materials chemistry, photopharmacology, and catalysis applications. Ideal photoswitches absorb visible light and have long-lived metastable isomers. We used high-throughput virtual screening to predict the absorption maxima (λ<sub>max</sub>) of the <i>E</i>-isomer and half-life (<i>t</i><sub>1/2</sub>) of the <i>Z</i>-isomer. However, computing the photophysical and kinetic stabilities with density functional theory of each entry of a virtual molecular library containing thousands or millions of molecules is prohibitively time-consuming. We applied active search, a machine-learning technique, to intelligently search a chemical search space of 255 991 photoswitches based on 29 known azoarenes and their derivatives. We iteratively trained the active search algorithm on whether a candidate absorbed visible light (λ<sub>max</sub> > 450 nm). Active search was found to triple the discovery rate compared to random search. Further, we projected 1962 photoswitches to 2D using the Uniform Manifold Approximation and Projection algorithm and found that λ<sub>max</sub> depends on the core, which is tunable by substituents. We then incorporated a second stage of screening to predict the stabilities of the <i>Z</i>-isomers for the top candidates of each core. We identified four ideal photoswitches that concurrently satisfy the following criteria: λ<sub>max</sub> > 450 nm and <i>t</i><sub>1/2</sub> > 2 h.These candidates had λ<sub>max</sub> and <i>t</i><sub>1/2</sub> range from 465 to 531 nm and hours to days, respectively. Isomerization ; Virtual screening ; Photochromism ; Spiropyran ; Chemistry ; Cucurbituril ; Molecule ; Photoswitch ; Chemical space ; Drug discovery ; Photochemistry ; Computational chemistry ; Molecular dynamics ; Catalysis Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Nesrine Amor | Muhammad Tayyab Noman | Michal Petrů
Source: Scientific Reports (article, 2021-06-10)
Field: Materials Science — Polymers and Plastics

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ([Formula: see text]) compared to MLR. The correlation coefficient of MLP model ([Formula: see text]) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics. Nano- ; Artificial neural network ; Composite material ; Materials science ; Computer science ; Artificial intelligence Textile materials and evaluations ; Pigment Synthesis and Properties ; Dyeing and Modifying Textile Fibers
Authors: Zhuochen Yu | Weimin Huang
Source: Electroanalysis (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning Abstract In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed‐loop system. The approach requires at least three parts including high‐throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning‐based design of carbon‐based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed. Computer science ; Throughput ; Machine learning ; Artificial intelligence ; Electrocatalyst ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Ruocheng Han | Mauricio Rodríguez‐Mayorga | Sandra Luber
Source: Journal of Chemical Theory and Computation (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds A proper treatment of electron correlation effects is indispensable for accurate simulation of compounds. Various post-Hartree-Fock methods have been adopted to calculate correlation energies of chemical systems, but time complexity usually prevents their usage in a large scale. Here, we propose a density functional approximation, based on machine learning using neural networks, which can be readily employed to produce results comparable to second-order Møller-Plesset perturbation (MP2) ones for organic compounds with reduced computational cost. Various systems have been tested and the transferability across basis sets, structures, and nuclear configurations has been evaluated. Only a small number of molecules at the equilibrium structure has been needed for the training, and generally less than 5% relative error has been achieved for structures outside the training domain and systems containing about 140 atoms. In addition, this approach has been applied to make predictions on correlation energies of nuclear configurations extracted from density functional theory-based molecular dynamics trajectories with only one or two structures as training data. Transferability ; Perturbation theory (quantum mechanics) ; Computer science ; Density functional theory ; Correlation ; Electronic correlation ; Statistical physics ; Artificial neural network ; Perturbation (astronomy) ; Basis (linear algebra) ; Organic molecules ; Molecular dynamics ; Molecule ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Jean‐Claude Crivello | Jean‐Marc Joubert | Nataliya Sokolovska
Source: Computational Materials Science (article, 2021-09-21)
Field: Materials Science — Materials Chemistry

Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e425" altimg="si20.svg"><mml:mrow><mml:mi>σ</mml:mi><mml:mo>−</mml:mo></mml:mrow></mml:math>phase as an example Artificial intelligence ; Artificial neural network ; Machine learning ; Tetragonal crystal system ; Binary number ; Intermetallic ; Computer science ; Database ; Deep learning ; Algorithm ; Standard enthalpy of formation ; Phase (matter) ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions