jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manuel Sommer | Markus Sutter | Sayan Gupta | Henning Kirst | Aiko Turmo | Sigal Lechno‐Yossef | Rodney Burton | Christine Saechao | Nancy B. Sloan | Xiaolin Cheng | Leanne-Jade G. Chan | Christopher J. Petzold | Miguel Fuentes‐Cabrera | Corie Y. Ralston | Cheryl A. Kerfeld
Source: PLANT PHYSIOLOGY (article, 2018-11-02)
Field: Materials Science — Materials Chemistry

Heterohexamers Formed by CcmK3 and CcmK4 Increase the Complexity of Beta Carboxysome Shells Bacterial microcompartments (BMCs) encapsulate enzymes within a selectively permeable, proteinaceous shell. Carboxysomes are BMCs containing ribulose-1,5-bisphosphate carboxylase oxygenase and carbonic anhydrase that enhance carbon dioxide fixation. The carboxysome shell consists of three structurally characterized protein types, each named after the oligomer they form: BMC-H (hexamer), BMC-P (pentamer), and BMC-T (trimer). These three protein types form cyclic homooligomers with pores at the center of symmetry that enable metabolite transport across the shell. Carboxysome shells contain multiple BMC-H paralogs, each with distinctly conserved residues surrounding the pore, which are assumed to be associated with specific metabolites. We studied the regulation of β-carboxysome shell composition by investigating the BMC-H genes <i>ccmK3</i> and <i>ccmK4</i> situated in a locus remote from other carboxysome genes. We made single and double deletion mutants of <i>ccmK3</i> and <i>ccmK4</i> in <i>Synechococcus elongatus</i> PCC7942 and show that, unlike CcmK3, CcmK4 is necessary for optimal growth. In contrast to other CcmK proteins, CcmK3 does not form homohexamers; instead CcmK3 forms heterohexamers with CcmK4 with a 1:2 stoichiometry. The CcmK3-CcmK4 heterohexamers form stacked dodecamers in a pH-dependent manner. Our results indicate that CcmK3-CcmK4 heterohexamers potentially expand the range of permeability properties of metabolite channels in carboxysome shells. Moreover, the observed facultative formation of dodecamers in solution suggests that carboxysome shell permeability may be dynamically attenuated by "capping" facet-embedded hexamers with a second hexamer. Because β-carboxysomes are obligately expressed, heterohexamer formation and capping could provide a rapid and reversible means to alter metabolite flux across the shell in response to environmental/growth conditions. Random hexamer ; Chemistry ; RuBisCO ; Metabolite ; Trimer ; Biochemistry ; Biophysics ; Stereochemistry ; Biology ; Enzyme ; Dimer Enzyme Structure and Function ; Metal-Catalyzed Oxygenation Mechanisms ; Porphyrin Metabolism and Disorders
Authors: Zhijie Ma | Stephen M. Hanham | Yandong Gong | Minghui Hong
Source: Optics Letters (article, 2018-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-dielectric reflective half-wave plate metasurface based on the anisotropic excitation of electric and magnetic dipole resonances We present an all-dielectric metasurface that simultaneously supports electric and magnetic dipole resonances for orthogonal polarizations. At resonances, the metasurface reflects the incident light with nearly perfect efficiency and provides a phase difference of π in the two axes, making a low-loss half-wave plate in reflection mode. The polarization handedness of the incident circularly polarized light is preserved after reflection; this is different from either a pure electric mirror or magnetic mirror. With the features of high reflection and circular polarization conservation, the metamirror is an ideal platform for the geometric phase-based gradient metasurface functioning in reflection mode. Anomalous reflection with the planar meta-mirror is demonstrated as a proof of concept. The proposed meta-mirror can be a good alternative to plasmonic metasurfaces for future compact and high-efficiency metadevices for polarization and phase manipulation in reflection mode. Optics ; Polarization (electrochemistry) ; Physics ; Excitation ; Reflection (computer programming) ; Circular polarization ; Dipole ; Metamaterial ; Magnetic dipole ; Plasmon ; Dielectric ; Planar ; Waveplate ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Akira Yada | Kenji Nagata | Yasunobu Ando | Tarojiro Matsumura | Sakina Ichinoseki | Kazuhiko Sato
Source: Chemistry Letters (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Prediction of Reaction Yield with Simulated Catalyst Parameters Prediction of reaction yields by machine learning approach is demonstrated in tungsten-catalyzed epoxidation of alkenes. The various electronic and vibrational parameters of the phosphonic acids are collected by DFT simulation, and chosen by LASSO as the essential parameters for prediction of the reaction yields. With the trained model, we can predict yields of the reaction with unverified phosphonic acids with an error of 26%. Chemistry ; Yield (engineering) ; Catalysis ; Artificial intelligence ; Machine learning ; Organic chemistry ; Thermodynamics ; Computer science Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Asymmetric Hydrogenation and Catalysis
Authors: Keisuke Takahashi | Itsuki Miyazato
Source: Journal of Computational Chemistry (article, 2018-10-20)
Field: Materials Science — Materials Chemistry

Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are revealed within the 788 activation energy dataset. With the implementation of machine learning and chosen descriptors, activation energy can be instantly predicted with over 90% accuracy during cross-validation. Thus, rapid estimation of activation energies within heterogeneous catalytic reactions can be made achievable via machine learning, leading toward the acceleration of catalysts design and characterization. © 2018 Wiley Periodicals, Inc. Activation energy ; Catalysis ; Computer science ; Energy (signal processing) ; Acceleration ; Estimation ; Biological system ; Artificial intelligence ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Hythem Sidky | Jonathan K. Whitmer
Source: The Journal of Chemical Physics (article, 2018-03-12)
Field: Materials Science — Materials Chemistry

Learning free energy landscapes using artificial neural networks Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input. Overfitting ; Hyperparameter ; Computer science ; Artificial neural network ; Artificial intelligence ; Energy (signal processing) ; Machine learning ; Bayesian optimization ; Free energy principle ; Bayesian probability ; Adaptive sampling ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Gabriel Laude | Danilo Calderini | David P. Tew | Jeremy O. Richardson
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> instanton rate theory made efficient using Gaussian process regression In this paper, we describe how we use Gaussian process regression to fit a local representation of the potential energy surface and thereby obtain the instanton rate using only a small number of <italic>ab initio</italic> calculations. Instanton ; Ab initio ; Statistical physics ; Gaussian ; Gaussian process ; Representation (politics) ; Regression ; Point process ; Process (computing) ; Kriging ; Computational chemistry ; Mathematics ; Physics ; Statistics ; Quantum mechanics ; Computer science ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Ali Moghimi | Ce Yang | Marisa E. Miller | Shahryar F. Kianian | Peter Marchetto
Source: Frontiers in Plant Science (article, 2018-08-24)
Field: Chemistry — Analytical Chemistry

A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner. Hyperspectral imaging ; Artificial intelligence ; Endmember ; Pattern recognition (psychology) ; Preprocessor ; Computer science ; Similarity (geometry) ; Pixel ; Mathematics ; Support vector machine ; Computer vision ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Nicolas Rolland | Juan Felipe Franco‐Gonzalez | Riccardo Volpi | Mathieu Linares | Igor Zozoulenko
Source: Physical Review Materials (article, 2018-04-30)
Field: Materials Science — Polymers and Plastics

Understanding morphology-mobility dependence in PEDOT:Tos The potential of conjugated polymers to compete with inorganic materials in the field of semiconductor is conditional on fine-tuning of the charge carriers mobility. The latter is closely related to the material morphology, and various studies have shown that the bottleneck for charge transport is the connectivity between well-ordered crystallites, with a high degree of $\ensuremath{\pi}\text{\ensuremath{-}}\ensuremath{\pi}$ stacking, dispersed into a disordered matrix. However, at this time there is a lack of theoretical descriptions accounting for this link between morphology and mobility, hindering the development of systematic material designs. Here we propose a computational model to predict charge carriers mobility in conducting polymer PEDOT depending on the physicochemical properties of the system. We start by calculating the morphology using molecular dynamics simulations. Based on the calculated morphology we perform quantum mechanical calculation of the transfer integrals between states in polymer chains and calculate corresponding hopping rates using the Miller-Abrahams formalism. We then construct a transport resistive network, calculate the mobility using a mean-field approach, and analyze the calculated mobility in terms of transfer integrals distributions and percolation thresholds. Our results provide theoretical support for the recent study [Noriega et al., Nat. Mater. 12, 1038 (2013)] explaining why the mobility in polymers rapidly increases as the chain length is increased and then saturates for sufficiently long chains. Our study also provides the answer to the long-standing question whether the enhancement of the crystallinity is the key to designing high-mobility polymers. We demonstrate, that it is the effective $\ensuremath{\pi}\text{\ensuremath{-}}\ensuremath{\pi}$ stacking, not the long-range order that is essential for the material design for the enhanced electrical performance. This generic model can compare the mobility of a polymer thin film with different solvent contents, solvent additives, dopant species or polymer characteristics, providing a general framework to design new high mobility conjugated polymer materials. Materials science ; Electron mobility ; Stacking ; Charge carrier ; Dopant ; Polymer ; Chemical physics ; Condensed matter physics ; Physics ; Doping ; Optoelectronics ; Nuclear magnetic resonance ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Eufrânio N. da Silva Júnior | Guilherme A. M. Jardim | Roberto da Silva Gomes | Yu‐Feng Liang | Lutz Ackermann
Source: Chemical Communications (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Weakly-coordinating <i>N</i>-oxide and carbonyl groups for metal-catalyzed C–H activation: the case of A-ring functionalization Compounds featuring weakly-coordinating N-oxides or carbonyl groups, as for instance, quinoline N-oxide and quinonoid systems represent important structural scaffolds with potential biological activities. Due to their biological importance, significant efforts have been devoted to devise robust methods for their step-economical preparation. Among these approaches, the C-H activation strategy has emerged as a powerful, versatile and efficient tool in molecular sciences. This feature article summarizes recent key advances in transition-metal-catalyzed C-H functionalization for A-ring functionalization of heterocyclic and quinoidal compounds by challenging weakly-coordinating entities, published prior to May 2018. Surface modification ; Catalysis ; Ring (chemistry) ; Chemistry ; Metal ; Oxide ; Combinatorial chemistry ; Carbonyl group ; Medicinal chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Catalytic Cross-Coupling Reactions
Authors: Risi Kondor
Source: arXiv (Cornell University) (preprint, 2018-03-05)
Field: Materials Science — Materials Chemistry

N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials We describe N-body networks, a neural network architecture for learning the behavior and properties of complex many body physical systems. Our specific application is to learn atomic potential energy surfaces for use in molecular dynamics simulations. Our architecture is novel in that (a) it is based on a hierarchical decomposition of the many body system into subsytems, (b) the activations of the network correspond to the internal state of each subsystem, (c) the "neurons" in the network are constructed explicitly so as to guarantee that each of the activations is covariant to rotations, (d) the neurons operate entirely in Fourier space, and the nonlinearities are realized by tensor products followed by Clebsch-Gordan decompositions. As part of the description of our network, we give a characterization of what way the weights of the network may interact with the activations so as to ensure that the covariance property is maintained. Covariant transformation ; Computer science ; Architecture ; Property (philosophy) ; Artificial neural network ; Tensor (intrinsic definition) ; Network architecture ; Decomposition ; Covariance ; State space ; Topology (electrical circuits) ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Pure mathematics ; Geometry ; Chemistry ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Jung Woo Sohn | Gi‐Woo Kim | Seung‐Bok Choi
Source: Applied Sciences (review, 2018-10-15)
Field: Materials Science — Materials Chemistry

A State-of-the-Art Review on Robots and Medical Devices Using Smart Fluids and Shape Memory Alloys Over the last two decades, smart materials have received significant attention over a broad range of engineering applications because of their unique and inherent characteristics for actuating and sensing aspects. In this review article, recent research works on various robots, medical devices and rehabilitation mechanisms whose main functions are activated by smart materials are introduced and discussed. Among many smart materials, electro-rheological fluids, magneto-rheological fluids, and shape memory alloys are considered since there are mostly appropriate application candidates for the robot and medical devices. Many different types of robots proposed to date, such as parallel planar robots, are investigated focusing on design configuration and operating principles. In addition, specific mechanism and operating principles of medical devices and rehabilitation systems are introduced and commented in terms of practical realization. Robot ; Computer science ; Smart material ; Realization (probability) ; State (computer science) ; Human–computer interaction ; Mechanical engineering ; Control engineering ; Nanotechnology ; Materials science ; Engineering ; Artificial intelligence Shape Memory Alloy Transformations ; Polymer composites and self-healing ; Soft Robotics and Applications
Authors: Yue Shi | Wenjiang Huang | Pablo González‐Moreno | Belinda Luke | Yingying Dong | Qiong Zheng | Huiqin Ma | Linyi Liu
Source: Remote Sensing (article, 2018-03-27)
Field: Chemistry — Analytical Chemistry

Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat Understanding the progression of host–pathogen interaction through time by hyperspectral features is vital for tracking yellow rust (Puccinia striiformis) development, one of the major diseases of wheat. However, well-designed features are still open issues that impact the performance of relevant models to nondestructively detect pathological progress of wheat rust. The aim of this paper is (1) to propose a novel wavelet-based rust spectral feature set (WRSFs) to uncover wheat rust-related processes; and (2) to evaluate the performance and robustness of the proposed WRSFs and models for retrieving the progression of host–pathogen interaction and tracking rust development. A hyperspectral dataset was collected by analytical spectral devices (ASD) spectroradiometer and Headwall spectrograph, along with corresponding physiological measurements of chlorophyll index (CHL), nitrogen balance index (NBI), anthocyanin index (ANTH), and percentile dry matter (PDM) from the 7th to 41st day after inoculation (dai) under controlled conditions. The resultant findings suggest that the progression of yellow rust on wheat is better characterized by the proposed WRSFs (R2 &gt; 0.7). The WRSFs-based PLSR model provides insight into specific leaf biophysical variations in the rust pathological progress. To evaluate the efficiency of the proposed WRSFs on yellow rust discrimination during different infestation stages, the identified WRSFs and vegetation indices (VIs) were fed into linear discriminant analysis (LDA) and support vector machine (SVM) classification frames. The WRSFs in conjunction with a SVM classifier can obtain better performance than that of LDA method and the VIs-based models. Overall, synthesizing the biophysical analysis, retrieving accuracy, and classification performance, we recommend the proposed WRSFs for monitoring the progression of the host–pathogen interaction of yellow rust on wheat cross various hyperspectral sensors. Rust (programming language) ; Wavelet ; Transformation (genetics) ; Pattern recognition (psychology) ; Artificial intelligence ; Feature (linguistics) ; Set (abstract data type) ; Computer science ; Host (biology) ; Continuous wavelet transform ; Wavelet transform ; Biological system ; Biology ; Discrete wavelet transform ; Ecology Spectroscopy and Chemometric Analyses ; Wheat and Barley Genetics and Pathology ; Smart Agriculture and AI
Authors: Anjana Talapatra | S. Boluki | T. Duong | X. Qian | E. Dougherty | R. Arróyave
Source: Physical Review Materials (article, 2018-11-26)
Field: Materials Science — Materials Chemistry

Autonomous efficient experiment design for materials discovery with Bayesian model averaging The accelerated exploration of the materials space in order to identify configurations with optimal properties is an ongoing challenge. Current paradigms are typically centered around the idea of performing this exploration through high-throughput experimentation/computation. Such approaches, however, do not account for---the always present---constraints in resources available. Recently this problem has been addressed by framing materials discovery as an optimal experiment design. This work augments earlier efforts by putting forward a framework that efficiently explores the materials design space not only accounting for resource constraints but also incorporating the notion of model uncertainty. The resulting approach combines Bayesian model averaging within Bayesian optimization in order to realize a system capable of autonomously and adaptively learning not only the most promising regions in the materials space but also the models that most efficiently guide such exploration. The framework is demonstrated by efficiently exploring the MAX ternary carbide/nitride space through density functional theory (DFT) calculations. Bayesian optimization ; Bayesian probability ; Space (punctuation) ; Bayesian inference ; Framing (construction) ; Design of experiments ; Ternary operation Machine Learning in Materials Science ; Advanced materials and composites ; Boron and Carbon Nanomaterials Research