jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liyao Zheng | Ruimao Hua
Source: Frontiers in Chemistry (review, 2020-10-15)
Field: Chemistry — Organic Chemistry

Recent Advances in Construction of Polycyclic Natural Product Scaffolds via One-Pot Reactions Involving Alkyne Annulation Polycyclic scaffolds are omnipresent in natural products and drugs, and the synthetic strategies and methods toward construction of these scaffolds are of particular importance. Compared to simple cyclic ring systems, polycyclic scaffolds have higher structure complexity and diversity, making them suitable for charting broader chemical space, yet bringing challenges for the syntheses. In this review, we surveyed progress in the past decade on synthetic methods for polycyclic natural product scaffolds, in which the key steps are one-pot reactions involving intermolecular or intramolecular alkyne annulation. Synthetic strategies of selected polycyclic carbocycles and heterocycles with at least three fused, bridged, or spiro rings are discussed with emphasis on the synthetic efficiency and product diversity. Recent examples containing newly developed synthetic concepts or toolkits such as collective and divergent total synthesis, gold catalysis, C-H functionalization, and dearomative cyclization are highlighted. Finally, several "privileged synthetic strategies" for "privileged polycyclic scaffolds" are summarized, with discussion of remained challenges and future perspectives. Annulation ; Natural product ; Chemical space ; Alkyne ; Intramolecular force ; Combinatorial chemistry ; Total synthesis ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis ; Drug discovery ; Materials science Catalytic Alkyne Reactions ; Catalytic C–H Functionalization Methods ; Synthetic Organic Chemistry Methods
Authors: Wu‐Xing Zhou | Yuan Cheng | Ke‐Qiu Chen | Guofeng Xie | Tian Wang | Gang Zhang
Source: Advanced Functional Materials (article, 2020-02-01)
Field: Materials Science — Polymers and Plastics

Thermal Conductivity: Thermal Conductivity of Amorphous Materials (Adv. Funct. Mater. 8/2020) In article number 1903829, Guofeng Xie, Tian Wang, Gang Zhang, and co-workers summarize the fundamental physical aspects of thermal conductivity in amorphous materials, which have valuable applications in flexible electronics, artificial intelligence chips, thermal protection, and thermoelectrics. Materials science ; Thermal conductivity ; Thermoelectric materials ; Amorphous solid ; Nanotechnology ; Electronics ; Engineering physics ; Composite material ; Physics ; Crystallography ; Electrical engineering Textile materials and evaluations
Authors: João M. J. M. Ravasco | Jaime A. S. Coelho
Source: Journal of the American Chemical Society (article, 2020-02-14)
Field: Chemistry — Organic Chemistry

Predictive Multivariate Models for Bioorthogonal Inverse-Electron Demand Diels–Alder Reactions Inverse-electron demand Diels-Alder cycloadditions have emerged as important bioorthogonal reactions in chemical biology. Understanding and predicting reaction rates for bioconjugation reactions is fundamental for evaluating their efficacy in biological systems. Here, we present multivariate models to predict the second order rate constants of bioorthogonal inverse-electron demand Diels-Alder reactions involving 1,2,4,5-tetrazines derivatives. A data-driven approach was used to model these reactions by parametrizing both the dienophiles and the dienes partners. The models are statistically robust and were used to predict/extrapolate the outcome of several reactions as well as to identify mechanistic differences among similar reactants. Bioorthogonal chemistry ; Chemistry ; Bioconjugation ; Inverse ; Multivariate statistics ; Reaction rate constant ; Diels–Alder reaction ; Chemical reaction ; Computational chemistry ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Kinetics ; Catalysis ; Click chemistry ; Computer science ; Machine learning Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Upendra Sharan Gupta | Mohit Dhamarikar | Amit Dharkar | Sudhir Tiwari | Rejeev Namdeo
Source: Advanced Composites and Hybrid Materials (article, 2020-09-26)
Field: Materials Science — Polymers and Plastics

Study on the effects of fibre volume percentage on banana-reinforced epoxy composite by finite element method Epoxy ; Composite material ; Materials science ; Composite number ; Ultimate tensile strength ; Flexural strength ; Finite element method ; Volume fraction ; Natural fiber ; Structural engineering ; Engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Zhaoqiang Zhang | Qi Ding | Jiyu Cui | Xili Cui | Huabin Xing
Source: Science China Materials (article, 2020-10-19)
Field: Chemistry — Inorganic Chemistry

High and selective capture of low-concentration CO2 with an anion-functionalized ultramicroporous metal-organic framework Isostructural ; Materials science ; Bar (unit) ; Adsorption ; Metal-organic framework ; Chemical engineering ; Ion ; Carbon fibers ; Crystallography ; Organic chemistry ; Crystal structure ; Composite material ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Qing‐Hui Ling | Junlong Zhu | Yi Qin | Lin Xu
Source: Materials Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Naphthalene diimide- and perylene diimide-based supramolecular cages This review comprehensively summarized the construction of naphthalene diimide- and perylene diimide-based cages as well as their properties and applications. Perylene ; Diimide ; Naphthalene ; Materials science ; Supramolecular chemistry ; Photochemistry ; Chemistry ; Organic chemistry ; Molecule Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials
Authors: Tianhao Lan | Libo Li | Yang Chen | Xiaoqing Wang | Jiangfeng Yang | Jinping Li
Source: Materials Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Opportunities and critical factors of porous metal–organic frameworks for industrial light olefins separation We summarize and highlight recent advances in the realm of light olefins separation using MOFs as adsorbents, covering the progress made in improving the key features of MOFs that are necessary for their real-world applications. Metal-organic framework ; Materials science ; Realm ; Nanotechnology ; Adsorption ; Separation (statistics) ; Porosity ; Key (lock) ; Computer science ; Organic chemistry ; Chemistry ; Composite material ; Political science Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Covalent Organic Framework Applications
Authors: Rafaela S. Guimarães | Carolina F. Rodrigues | André F. Moreira | Ilídio J. Correia
Source: Pharmacological Research (review, 2020-03-06)
Field: Materials Science — Biomaterials

Overview of stimuli-responsive mesoporous organosilica nanocarriers for drug delivery Nanocarriers ; Mesoporous silica ; Drug delivery ; Mesoporous organosilica ; Nanotechnology ; Biocompatibility ; Materials science ; Nanoparticle ; Mesoporous material ; Chemistry ; Organic chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Muhammad Ikram | Israt Jahan | Ali Haider | Jahan Zeb Hassan | Anwar Ul‐Hamid | Muhammad Imran | Junaid Haider | Anum Shahzadi | Atif Shahbaz | Sajjad Ali
Source: Applied Nanoscience (article, 2020-04-25)
Field: Materials Science — Materials Chemistry

Bactericidal behavior of chemically exfoliated boron nitride nanosheets doped with zirconium High-resolution transmission electron microscopy ; Materials science ; Wurtzite crystal structure ; Boron nitride ; Raman spectroscopy ; Nanochemistry ; Zirconium ; Analytical Chemistry (journal) ; Dopant ; Field emission microscopy ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Photoluminescence ; Doping ; Nuclear chemistry ; Nanotechnology ; Chemical engineering ; Transmission electron microscopy ; Chemistry ; Diffraction ; Composite material ; Organic chemistry ; Optics ; Zinc ; Optoelectronics ; Metallurgy Graphene research and applications ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Rafael R. Castillo | Lorena de la Torre | Félix García‐Ochoa | Miguel Ladero | María Vallet‐Regí
Source: International Journal of Molecular Sciences (article, 2020-10-24)
Field: Materials Science — Materials Chemistry

Production of MCM-41 Nanoparticles with Control of Particle Size and Structural Properties: Optimizing Operational Conditions during Scale-Up The synthesis of Mobil Composition of Matter 41 (MCM-41) mesoporous silica nanoparticles (MSNs) of controlled sizes and porous structure has been performed at laboratory and pilot plant scales. Firstly, the effects of the main operating conditions (TEOS -Tetraethyl ortosilicate- addition rate, nanoparticle maturation time, temperature, and CTAB -Cetrimonium bromide- concentration) on the synthesis at laboratory scale (1 L round-bottom flask) were studied via a Taguchi experimental design. Subsequently, a profound one-by-one study of operating conditions was permitted to upscale the process without significant particle enlargement and pore deformation. To achieve this, the temperature was set to 60 °C and the CTAB to TEOS molar ratio to 8. The final runs were performed at pilot plant scale (5 L cylindrical reactor with temperature and stirring speed control) to analyze stirring speed, type of impeller, TEOS addition rate, and nanoparticle maturation time effects, confirming results at laboratory scale. Despite slight variations on the morphology of the nanoparticles, this methodology provided MSNs with adequate sizes and porosities for biomedical applications, regardless of the reactor/scale. The process was shown to be robust and reproducible using mild synthesis conditions (2 mL⋅min<sup>-1</sup> TEOS addition rate, 400 rpm stirred by a Rushton turbine, 60 min maturation time, 60 °C, 2 g⋅L<sup>-1</sup> CTAB, molar ratio TEOS/CTAB = 8), providing <i>ca</i>. 13 g of prismatic short mesoporous 100-200 nm nanorods with non-connected 3 nm parallel mesopores. Nanoparticle ; Materials science ; Mesoporous material ; Particle size ; Chemical engineering ; SCALE-UP ; Rushton turbine ; Porosity ; Particle (ecology) ; Nanotechnology ; Impeller ; Composite material ; Chemistry ; Mechanical engineering ; Catalysis ; Organic chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Nanoparticle-Based Drug Delivery
Authors: Byungju Lee | Jaekyun Yoo | Kisuk Kang
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting the chemical reactivity of organic materials using a machine-learning approach Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, <i>i.e.</i> data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium-oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium-oxygen batteries. Compatibility (geochemistry) ; Biochemical engineering ; Chemical stability ; Reactivity (psychology) ; Computer science ; Stability (learning theory) ; Chemistry ; Artificial intelligence ; Machine learning ; Chemical engineering ; Organic chemistry ; Engineering ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Karun K. Rao | Kaiming Quan | Khoa Van Pham | Debtanu Maiti | Lars C. Grabow
Source: Topics in Catalysis (article, 2020-04-29)
Field: Materials Science — Materials Chemistry

Extendable Machine Learning Model for the Stability of Single Atom Alloys Atom (system on chip) ; Stability (learning theory) ; Support vector machine ; Artificial neural network ; Decision tree ; Hybrid functional ; Algorithm ; Kernel (algebra) ; Block (permutation group theory) ; Tree (set theory) ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Chemistry ; Density functional theory ; Physics ; Mathematics ; Geometry ; Computational chemistry ; Mathematical analysis ; Discrete mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Mila Krämer | Philipp M. Dohmen | Weiwei Xie | Daniel Holub | Anders S. Christensen | Marcus Elstner
Source: Journal of Chemical Theory and Computation (article, 2020-06-03)
Field: Materials Science — Materials Chemistry

Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians Quantum-mechanical simulations of charge and exciton transfer in molecular organic materials are a key method to increase our understanding of organic semiconductors. Our goal is to build an efficient multiscale model to predict charge-transfer mobilities and exciton diffusion constants from nonadiabatic molecular dynamics simulations and Marcus-based Monte Carlo approaches. In this work, we apply machine learning models to simulate charge and exciton propagation in organic semiconductors. We show that kernel ridge regression models can be trained to predict electronic and excitonic couplings from semiempirical density functional tight binding (DFTB) reference data with very good accuracy. In simulations, the models could reproduce hole mobilities along the anthracene crystal axes to within 8.5% of the DFTB reference and 34% of the experimental results with only 1000 training data points. Using these models decreased the cost of exciton transfer simulations by one order of magnitude. Exciton ; Charge (physics) ; Organic semiconductor ; Statistical physics ; Molecular dynamics ; Diffusion ; Kernel (algebra) ; Monte Carlo method ; Chemical physics ; Anthracene ; Physics ; Materials science ; Chemistry ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Guido Falk von Rudorff | Stefan Heinen | Marco Bragato | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Thousands of reactants and transition states for competing E2 and S N 2 reactions Abstract Reaction barriers are a crucial ingredient for first principles based computational retro-synthesis efforts as well as for comprehensive reactivity assessments throughout chemical compound space. While extensive databases of experimental results exist, modern quantum machine learning applications require atomistic details which can only be obtained from quantum chemistry protocols. For competing E2 and S <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi/> <mml:mrow> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:msub> </mml:math> 2 reaction channels we report 4,466 transition state and 143,200 reactant complex geometries and energies at MP2/6-311G(d) and single point DF-LCCSD/cc-pVTZ level of theory, respectively, covering the chemical compound space spanned by the substituents NO 2 , CN, CH 3 , and NH 2 and early halogens (F, Cl, Br) and hydrogen as nucleophiles and early halogens as leaving groups. Reactants are chosen such that the activation energy of the competing E2 and S <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi> </mml:mi> <mml:mrow> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:msub> </mml:math> 2 reactions are of comparable magnitude. The correct concerted motion for each of the one-step reactions has been validated for all transition states. We demonstrate how quantum machine learning models can support data set extension, and discuss the distribution of key internal coordinates of the transition states. Halogen ; Algorithm ; Chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Haoran Li | Seungjae Ryan Lee | Min Luo | Charles R. Sullivan | Yuxin Chen | Minjie Chen
Source:  (article, 2020-11-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MagNet: A Machine Learning Framework for Magnetic Core Loss Modeling This paper presents a two-stage machine learning framework – MagNet – for magnetic core loss modeling. The first stage of MagNet is a waveform transformation network, which generates 2-D images (tensors) and extracts both the frequency and time domain features from the magnetic excitation waveforms; the second stage of MagNet is a convolutional neural network (CNN), which is trained to recognize the patterns in the 2-D images and predict the core loss based on regression. MagNet is supported by a hardware-in-the-loop (HIL) data acquisition system. The system can automatically generate a large amount of data to train the neural network models. MagNet achieved an average relative error of around 5% for single-frequency core loss prediction. In addition to experimental measurements, MagNet can also be trained with data provided on the datasheets of magnetic materials to improve the accuracy. Magnet ; Computer science ; Convolutional neural network ; Artificial neural network ; Waveform ; Artificial intelligence ; Approximation error ; Core (optical fiber) ; Machine learning ; Algorithm ; Engineering ; Mechanical engineering Magnetic Properties and Applications ; Magnetic properties of thin films ; Magnetic Field Sensors Techniques
Authors: I. A. Balyakin | S. V. Rempel | R. E. Ryltsev | А. А. Rempel
Source: Physical review. E (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Deep machine learning interatomic potential for liquid silica The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing such potentials is the preparation of a training dataset of ab initio trajectories. Here we apply a deep potential molecular dynamics (DeePMD) approach to develop NNP for silica, which is the representative glassformer widely used as a model system for simulating network-forming liquids and glasses. We show that the use of a relatively small training dataset of high-temperature ab initio configurations is enough to fabricate NNP, which describes well both structural and dynamical properties of liquid silica. In particular, we calculate the pair correlation functions, angular distribution function, velocity autocorrelation functions, vibrational density of states, and mean-square displacement and reveal a close agreement with ab initio data. We show that NNP allows us to expand significantly the time-space scales achievable in simulations and thus calculating dynamical and transport properties with more accuracy than that for ab initio methods. We find that developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure. The results obtained open up prospects for simulating structural and dynamical properties of liquids and glasses via NNP. Ab initio ; Interatomic potential ; Autocorrelation ; Mean squared displacement ; Artificial neural network ; Statistical physics ; Computer science ; Molecular dynamics ; Radial distribution function ; Displacement (psychology) ; Materials science ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Dynamics and Properties
Authors: Saber Naserifar | Yalu Chen | Soonho Kwon | Hai Xiao | William A. Goddard
Source: Matter (article, 2020-11-27)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts QM/MM ; Polarizability ; Field (mathematics) ; Force field (fiction) ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Molecular dynamics ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Michael McCarthy | Kin Long Kelvin Lee | P. Brandon Carroll | Jessica P. Porterfield | P. Bryan Changala | James H. Thorpe | John F. Stanton
Source: The Journal of Physical Chemistry A (article, 2020-05-21)
Field: Chemistry — Spectroscopy

Exhaustive Product Analysis of Three Benzene Discharges by Microwave Spectroscopy Using chirped and cavity microwave spectroscopies, automated double resonance, new high-speed fitting and deep learning algorithms, and large databases of computed structures, the discharge products of benzene alone, or in combination with molecular oxygen or nitrogen, have been exhaustively characterized between 6.5 and 26 GHz. In total, more than 3300 spectral features were observed; 89% of these, accounting for 97% of the total intensity, have now been assigned to 152 distinct chemical species and 60 of their variants (i.e., isotopic species and vibrationally excited states). Roughly 50 of the products are entirely new or poorly characterized at high resolution, including many heavier by mass than the precursor benzene. These findings provide direct evidence for a rich architecture of two- and three-dimensional carbon and indicate that benzene growth, particularly the formation of ring-chain molecules, occurs facilely under our experimental conditions. The present analysis also illustrates the utility of microwave spectroscopy as a precision tool for complex mixture analysis, irrespective of whether the rotational spectrum of a product species is known <i>a priori</i> or not. From this large quantity of data, for example, it is possible to determine with confidence the relative abundances of different product masses, but more importantly the relative abundances of different isomers with the same mass. The complementary nature of this type of analysis to traditional mass spectrometry is discussed. Benzene ; Mass spectrometry ; Spectroscopy ; Excited state ; Chemistry ; Microwave ; Mass spectrum ; Analytical Chemistry (journal) ; Molecule ; Rotational spectroscopy ; Resonance (particle physics) ; Atomic physics ; Physics ; Organic chemistry ; Chromatography Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies