jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marina Toplak | Arne Matthews | Robin Teufel
Source: Archives of Biochemistry and Biophysics (review, 2020-12-24)
Field: Chemistry — Inorganic Chemistry

The devil is in the details: The chemical basis and mechanistic versatility of flavoprotein monooxygenases The ubiquitous flavoenzymes commonly catalyze redox chemistry such as the monooxygenation of organic substrates and are both widely utilized in nature (e.g., in primary and secondary metabolism) and of significant industrial interest. In this work, we highlight the structural and mechanistic characteristics of the distinct types of flavoprotein monooxygenases (FPMOs). We thereby illustrate the chemical basis of FPMO catalysis, which enables reactions such as (aromatic) hydroxylation, epoxidation, (de)halogenation, heteroatom oxygenation, Baeyer-Villiger oxidation, α-hydroxylation of ketones, or non-oxidative carbon-hetero bond cleavage. This seemingly unmatched versatility in oxygenation chemistry results from extensive fine-tuning and regiospecific functionalization of the flavin cofactor that is tightly controlled by the surrounding protein matrix. Accordingly, FPMOs steer the formation of covalent flavin-oxygen adducts for oxygen transfer in the form of the classical flavin-C4a-(hydro)peroxide or the recently discovered N5-functionalized flavins (i.e. the flavin-N5-oxide and the flavin-N5-peroxide), while in rare cases covalent oxygen adduct formation may be foregone entirely. Finally, we speculate about hitherto undiscovered flavin-mediated oxygenation reactions and compare FPMOs to cytochrome P450 monooxygenases, before addressing open questions and challenges for the future investigation of FPMOs. Flavin group ; Flavoprotein ; Monooxygenase ; Chemistry ; Hydroxylation ; Covalent bond ; Cofactor ; Adduct ; Peroxide ; Redox ; Combinatorial chemistry ; Photochemistry ; Organic chemistry ; Cytochrome P450 ; Enzyme Metal-Catalyzed Oxygenation Mechanisms ; Microbial bioremediation and biosurfactants ; Enzyme Catalysis and Immobilization
Authors: Jiagen Li | Yuxiao Tu | Rulin Liu | Yihua Lu | Xi Zhu
Source: Advanced Science (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self-optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On-Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future. Reinforcement learning ; Computer science ; Compiler ; On demand ; Robot ; Quality (philosophy) ; Work (physics) ; Nanotechnology ; Artificial intelligence ; Materials science ; Multimedia ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ignacio Funes‐Ardoiz | Franziska Schoenebeck
Source: Chem (article, 2020-08-01)
Field: Materials Science — Materials Chemistry

Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning Homogeneous ; Nanotechnology ; Computer science ; Engineering ; Physics ; Materials science ; Statistical physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Xiaoxuan Zhang | Krishna Garikipati
Source: Computer Methods in Applied Mechanics and Engineering (article, 2020-08-22)
Field: Materials Science — Materials Chemistry

Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures Spinodal decomposition ; Spinodal ; Nonlinear system ; Statistical physics ; Elastic energy ; Microstructure ; Materials science ; Elasticity (physics) ; Artificial neural network ; Phase (matter) ; Computer science ; Mechanics ; Artificial intelligence ; Physics ; Thermodynamics ; Composite material Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Punyaslok Pattnaik | Shampa Raghunathan | Tarun Kalluri | Prabhakar Bhimalapuram | C. V. Jawahar | U. Deva Priyakumar
Source: The Journal of Physical Chemistry A (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations The computationally expensive nature of <i>ab initio</i> molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a Δ-NetFF machine learning model, where the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields, was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method. Force field (fiction) ; Molecular dynamics ; Artificial neural network ; Computer science ; Representation (politics) ; Statistical physics ; Field (mathematics) ; Work (physics) ; Artificial intelligence ; Experimental data ; Density functional theory ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Cheng Yan | Xiaming Feng | Collin D. Wick | Andrew J. Peters | Guoqiang Li
Source: Polymer (article, 2020-12-30)
Field: Materials Science — Polymers and Plastics

Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset Computer science ; Thermosetting polymer ; Field (mathematics) ; Dimension (graph theory) ; Machine learning ; Artificial intelligence ; Stress (linguistics) ; Genetic programming ; Identification (biology) ; Feature (linguistics) ; Materials science ; Mathematics Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Photochromic and Fluorescence Chemistry
Authors: Zheng Chen | Chi Chen | Yiming Chen | Shyue Ping Ong
Source: Patterns (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification. XANES ; Coordination number ; Random forest ; Jaccard index ; Coordination complex ; Absorption (acoustics) ; Spectroscopy ; Computer science ; Absorption spectroscopy ; Enhanced Data Rates for GSM Evolution ; Chemistry ; X-ray absorption spectroscopy ; Materials science ; Physics ; Artificial intelligence ; Optics ; Pattern recognition (psychology) ; Metal ; Ion ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Marc Finzi | Samuel Stanton | Pavel Izmailov | Andrew Gordon Wilson
Source: arXiv (Cornell University) (preprint, 2020-02-25)
Field: Materials Science — Materials Chemistry

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data The translation equivariance of convolutional layers enables convolutional neural networks to generalize well on image problems. While translation equivariance provides a powerful inductive bias for images, we often additionally desire equivariance to other transformations, such as rotations, especially for non-image data. We propose a general method to construct a convolutional layer that is equivariant to transformations from any specified Lie group with a surjective exponential map. Incorporating equivariance to a new group requires implementing only the group exponential and logarithm maps, enabling rapid prototyping. Showcasing the simplicity and generality of our method, we apply the same model architecture to images, ball-and-stick molecular data, and Hamiltonian dynamical systems. For Hamiltonian systems, the equivariance of our models is especially impactful, leading to exact conservation of linear and angular momentum. Equivariant map ; Lie group ; Convolutional neural network ; Mathematical proof ; Mathematics ; Algebra over a field ; Generality ; Logarithm ; Pure mathematics ; Computer science ; Artificial intelligence ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Physics and Python Applications
Authors: Karina Hemmer | Mirza Cokoja | Roland A. Fischer
Source: ChemCatChem (article, 2020-12-14)
Field: Chemistry — Inorganic Chemistry

Exploitation of Intrinsic Confinement Effects of MOFs in Catalysis Abstract In catalysis research the design of bio‐inspired,'artificial enzymes‘ is a field of huge interest. These catalysts are distinguished by their high catalytic efficiency resulting from a close proximity of several active sites and secondary substrate‐catalyst interactions enabled by functional groups in the catalytic pocket. A class of materials which meets these requirements are metal‐organic frameworks (MOFs). Here, the pores confine a reaction environment where several functionalities can be incorporated and spatially positioned in a tunable fashion. Recently, a number of reports revealed the importance of such confinement effects for the control of catalytic activity and selectivity by exploiting the intrinsic properties like pore size and neighboring group effects and the alignment and distance of different active sites within one MOF pore. Thus, this concept aims to accentuate the potential of the exploitation of those effects in MOFs for the design of sophisticated catalysts. Catalysis ; Nanotechnology ; Substrate (aquarium) ; Metal-organic framework ; Selectivity ; Rational design ; Heterogeneous catalysis ; Chemistry ; Materials science ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Giovanna Palermo | Kandammathe Valiyaveedu Sreekanth | Nicolò Maccaferri | Giuseppe Emanuele Lio | Giuseppe Nicoletta | Francesco De Angelis | Michael Hinczewski | Giuseppe Strangi
Source: Nanophotonics (article, 2020-10-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hyperbolic dispersion metasurfaces for molecular biosensing Abstract Sensor technology has become increasingly crucial in medical research and clinical diagnostics to directly detect small numbers of low‐molecular‐weight biomolecules relevant for lethal diseases. In recent years, various technologies have been developed, a number of them becoming core label‐free technologies for detection of cancer biomarkers and viruses. However, to radically improve early disease diagnostics, tracking of disease progression and evaluation of treatments, today’s biosensing techniques still require a radical innovation to deliver high sensitivity, specificity, diffusion‐limited transport, and accuracy for both nucleic acids and proteins. In this review, we discuss both scientific and technological aspects of hyperbolic dispersion metasurfaces for molecular biosensing. Optical metasurfaces have offered the tantalizing opportunity to engineer wavefronts while its intrinsic nanoscale patterns promote tremendous molecular interactions and selective binding. Hyperbolic dispersion metasurfaces support high‐ k modes that proved to be extremely sensitive to minute concentrations of ultralow‐molecular‐weight proteins and nucleic acids. Biosensor ; Nanotechnology ; Biomolecule ; Dispersion (optics) ; Computer science ; Materials science ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Weibin Wu | Zhenbang Zhang | Lijun Zheng | Chongyang Han | Xiaoming Wang | Jian Xu | Xinrong Wang
Source: Sensors (review, 2020-07-03)
Field: Chemistry — Analytical Chemistry

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques Pine wilt disease (PWD) caused by pine wood nematode (PWN, <i>Bursaphelenchus xylophilus</i>) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing. Wilt disease ; Bursaphelenchus xylophilus ; Hyperspectral imaging ; Pine wood ; Quarantine ; Agroforestry ; Environmental science ; Remote sensing ; Geography ; Biology ; Ecology ; Botany ; Nematode Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Remote Sensing in Agriculture
Authors: Tijmen S. Bos | Wouter C. Knol | Stef R.A. Molenaar | Leon E. Niezen | Peter J. Schoenmakers | Govert W. Somsen | Bob W.J. Pirok
Source: Journal of Separation Science (review, 2020-02-25)
Field: Chemistry — Spectroscopy

Recent applications of chemometrics in one‐ and two‐dimensional chromatography The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization. Chemometrics ; Chromatography ; Chemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies
Authors: Robert J. Mayer | Nathalie Hampel | Armin R. Ofial
Source: Chemistry - A European Journal (article, 2020-11-20)
Field: Chemistry — Organic Chemistry

Lewis Acidic Boranes, Lewis Bases, and Equilibrium Constants: A Reliable Scaffold for a Quantitative Lewis Acidity/Basicity Scale A quantitative Lewis acidity/basicity scale toward boron-centered Lewis acids has been developed based on a set of 90 experimental equilibrium constants for the reactions of triarylboranes with various O-, N-, S-, and P-centered Lewis bases in dichloromethane at 20 °C. Analysis with the linear free energy relationship log K<sub>B</sub> =LA<sub>B</sub> +LB<sub>B</sub> allows equilibrium constants, K<sub>B</sub> , to be calculated for any type of borane/Lewis base combination through the sum of two descriptors, one for Lewis acidity (LA<sub>B</sub> ) and one for Lewis basicity (LB<sub>B</sub> ). The resulting Lewis acidity/basicity scale is independent of fixed reference acids/bases and valid for various types of trivalent boron-centered Lewis acids. It is demonstrated that the newly developed Lewis acidity/basicity scale is easily extendable through linear relationships with quantum-chemically calculated or common physical-organic descriptors and known thermodynamic data (ΔH <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow></mml:mrow> <mml:mrow><mml:mi>BF</mml:mi> <mml:msub><mml:mrow></mml:mrow> <mml:mn>3</mml:mn></mml:msub> </mml:mrow> </mml:msub> </mml:math> ). Furthermore, this experimental platform can be utilized for the rational development of borane-catalyzed reactions. Lewis acids and bases ; Boranes ; Chemistry ; Borane ; Equilibrium constant ; Boron ; Computational chemistry ; Organic chemistry ; Catalysis ; Inorganic chemistry Organoboron and organosilicon chemistry ; Sulfur-Based Synthesis Techniques ; Chemical Synthesis and Reactions
Authors: Shao‐Jie Lou | Liang Zhang | Yong Luo | Masayoshi Nishiura | Gen Luo | Yi Luo | Zhaomin Hou
Source: Journal of the American Chemical Society (article, 2020-09-28)
Field: Chemistry — Organic Chemistry

Regiodivergent C–H Alkylation of Quinolines with Alkenes by Half-Sandwich Rare-Earth Catalysts The regiodivergent catalysis of C-H alkylation with alkenes is of great interest and importance but has remained hardly explored to date. We report herein the first regiodivergent C-H alkylation of quinolines with alkenes by half-sandwich rare-earth catalysts. The regiodivergence was achieved by fine-tuning the metal/ligand combination or steric and electronic properties of the catalysts. The use of the C<sub>5</sub>Me<sub>5</sub>-ligated scandium catalyst <b>Sc-3</b> for the reaction of quinolines with styrenes and that of the C<sub>5</sub>Me<sub>4</sub>H-ligated yttrium catalyst <b>Y-2</b> for the reaction with aliphatic olefins exclusively afforded the corresponding C8-H alkylation products, thus constituting the first example of direct C8-H alkylation of neutral quinolines. In contrast, the <b>Sc-3</b>-catalyzed reaction of 2-arylquinolines with aliphatic olefins and the <b>Y-2</b>-catalyzed reaction with styrenes selectively gave the 2-aryl <i>o</i>-C-H alkylation products. On the basis of the catalyst/substrate-controlled regiodivergence, the sequential regiospecific dialkylation of quinolines with two different alkenes has also been achieved. DFT studies revealed that the C-H activation of 2-phenylquinoline at both the C8 position and an <i>ortho</i> position of the 2-phenyl substituent was possible, and these two types of initially formed C-H activation products were interconvertible through the coordination and C-H activation of another molecule of quinoline. The regioselectivity for the C-H alkylation reactions was governed not only by the ease of the initial formation of the C-H activation products but also by the energy barriers for their interconversions, as well as by the energy barriers or steric and electronic influences in the subsequent alkene insertion processes. This work has not only constituted an efficient protocol for the selective synthesis of diversified quinoline derivatives but also offered unprecedented insights into the C-H activation and transformation of quinolines and may help in the design of more efficient, selective, or complementary catalysts. Chemistry ; Alkylation ; Catalysis ; Regioselectivity ; Quinoline ; Substituent ; Aryl ; Medicinal chemistry ; Alkene ; Organic chemistry ; Alkyl Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Zoha H. Syed | Fanrui Sha | Xuan Zhang | David M. Kaphan | Massimiliano Delferro | Omar K. Farha
Source: ACS Catalysis (article, 2020-09-10)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework Nodes as a Supporting Platform for Tailoring the Activity of Metal Catalysts Catalysis is a ubiquitous element of the modern economy, representing a cornerstone of many sectors including energy, materials, and pharmaceuticals. Within this realm, 80% of reactions are carried out by heterogeneous catalysts due to their advantageous physical and chemical characteristics. In some heterogeneous systems, the reaction rate can be optimized through rational catalyst design, in which support materials can be tailored structurally and stereoelectronically for targeted purposes to better facilitate the catalytic reaction. To this end, metal–organic frameworks (MOFs) have garnered recent attention due to their high porosity, crystallinity, and chemical tunability, which can be used to derive structure–activity relationships in the design of catalysts. In this Perspective, we survey examples of catalytically active guest metals on the inorganic nodes of MOFs. This is done with the intention of providing design guidelines for tailoring chemical reactivity and insights into the opportunities for future growth in this field. Strategies for rational design in MOF catalysis include electronic tuning of the metal node in the MOF support, installing promoter ions onto the node to effect the reactivity of grafted metals, modulation of the spatial environment around the transition metal using the metal node structure, and achieving site uniformity in supported catalysts. By highlighting these efforts, we seek to underscore the role of MOF nodes as nonspectator supports for catalytic metal complexes and provide future directions for rational catalyst design by tuning reactivity on these anchoring platforms. Rational design ; Catalysis ; Nanotechnology ; Materials science ; Reactivity (psychology) ; Metal-organic framework ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications