jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nora-Ann Weststrate | Christopher Hassenrück | Michael Linseis | David C. Liles | Simon Lotz | Helmar Görls | Rainer F. Winter
Source: Zeitschrift für anorganische und allgemeine Chemie (article, 2021-04-26)
Field: Chemistry — Organic Chemistry

The Effect of Remote Donor Substituents on the Properties of Alkoxy and Amino Fischer Carbene Complexes of Tungsten Abstract Tungsten Fischer ethoxy‐ and dimethylaminocarbene complexes [W{C(X)(C 6 H 4 ‐4‐R}(CO) 5 ] (X=OEt: series a ; or X=NMe 2 : series b ) are synthesized from phenyl substrates containing remote tertiary amino substituents R’ 2 N with R’=Me ( 2 ), Ph ( 3 ) or C 6 H 4 Br‐4 ( 4 ). The π‐delocalization and carbene‐stabilizing effects of the distant tertiary amine donor substituent are investigated by NMR and IR spectroscopy, electrochemistry and quantum chemical calculations. A significant transfer of electron density from the remote 4‐R’ 2 N substituent to the carbene C atom in the ethoxycarbene complexes is supported by NMR data and the solid‐state structure of 2 a . This is strongly attenuated in the amino‐substituted carbene complex 2 c and irrelevant in the dimethylaminocarbene complexes of series b , where the electron demand of the electrophilic carbene center is satisfied by the directly attached dimethylamino substituent. Quantum chemical calculations and IR spectroelectrochemistry on complexes 1 a‐ ‐ 4 a verify carbene‐centered reductions and a ligand based oxidation of complex 2 b . Carbene ; Substituent ; Chemistry ; Alkoxy group ; Electrophile ; Ligand (biochemistry) ; Transition metal carbene complex ; Delocalized electron ; Medicinal chemistry ; Crystallography ; Photochemistry ; Stereochemistry ; Organic chemistry ; Alkyl ; Catalysis Organometallic Complex Synthesis and Catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Coordination Chemistry and Organometallics
Authors: Patrice Limelette
Source: AIP Advances (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Numerical investigation of the maximum thermoelectric efficiency The maximum thermoelectric efficiency that is given by the so-called dimensionless figure of merit ZT is investigated here numerically for various energy dependence. By involving the electrical conductivity σ, the thermopower α, and the thermal conductivity κ such that ZT = α2 × σ × T/κ, the figure of merit is computed in the frame of a semiclassical approach that implies Fermi integrals. This formalism allows us to take into account the full energy dependence in the transport integrals through a previously introduced exponent s that combines the energy dependence of the quasiparticles’ velocity, the density of states, and the relaxation time. While it has been shown that an unconventional exponent s = 4 was relevant in the context of the conducting polymers, the question of the maximum of ZT is addressed by varying s from 1 up to 4 through a materials quality factor analysis. In particular, it is found that the exponent s = 4 allows for an extended range of high figure of merit toward the slightly degenerate regime. Useful analytical asymptotic relations are given, and a generalization of the Chasmar and Stratton formula of ZT is also provided. Figure of merit ; Semiclassical physics ; Condensed matter physics ; Exponent ; Quasiparticle ; Physics ; Dimensionless quantity ; Degenerate energy levels ; Seebeck coefficient ; Thermal conductivity ; Thermoelectric effect ; Quantum mechanics ; Statistical physics ; Optics ; Quantum Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Semiconductor materials and interfaces
Authors: Wolfgang Hornfeck
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Chiral spiral cyclic twins A formula is presented for the generation of chiral m-fold multiply twinned two-dimensional point sets of even twin modulus m > 6 from an integer inclination sequence; in particular, it is discussed for the first three non-degenerate cases m = 8, 10, 12, which share a connection to the aperiodic crystallography of axial quasicrystals exhibiting octagonal, decagonal and dodecagonal long-range orientational order and symmetry. Aperiodic graph ; Quasicrystal ; Spiral (railway) ; Connection (principal bundle) ; Order (exchange) ; Point (geometry) ; Integer (computer science) Quasicrystal Structures and Properties ; Synthesis and Properties of Aromatic Compounds ; Analytic and geometric function theory
Authors: Carmen Rubio-Verdú | M. Reyes Calvo
Source: Physics (article, 2021-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

New Moiré Landscapes for Atomic Spins The interactions of the spins of single atoms with a substrate can be controlled via the moiré lattice created by depositing a 2D material on top of the substrate. Spins ; Moiré pattern ; Materials science ; Physics ; Condensed matter physics ; Chemical physics ; Optics Magnetism in coordination complexes ; Quantum and electron transport phenomena ; Advanced NMR Techniques and Applications
Authors: Kevin Klann | David Krause | Christian Münch
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-07-07)
Field: Chemistry — Spectroscopy

DynaTMT: A user-friendly tool to process combined SILAC/TMT data ABSTRACT The measurement of protein dynamics by proteomics to study cell remodeling has seen increased attention over the last years. This development is largely driven by a number of technological advances in proteomics methods. Pulsed stable isotope labeling in cell culture (SILAC) combined with tandem mass tag (TMT) labeling has evolved as a gold standard for profiling protein synthesis and degradation. While the experimental setup is similar to typical proteomics experiments, the data analysis proves more difficult: After peptide identification through search engines, data extraction requires either custom scripted pipelines or tedious manual table manipulations to extract the TMT-labeled heavy and light peaks of interest. To overcome this limitation, which deters researchers from using protein dynamic proteomics, we developed a user-friendly, browser-based application that allows easy and reproducible data analysis without the need for scripting experience. In addition, we provide a python package that can be implemented in established data analysis pipelines. We anticipate that this tool will ease data analysis and spark further research aimed at monitoring protein translation and degradation by proteomics. Stable isotope labeling by amino acids in cell culture ; Proteomics ; Computer science ; Python (programming language) ; Scripting language ; Proteogenomics ; Profiling (computer programming) ; Chemistry ; Operating system Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; RNA and protein synthesis mechanisms
Authors: Johannes W. Kung | Anne‐Katrin Meier | Max Willistein | Sina Weidenweber | Ulrike Demmer | Ulrich Ermler | Matthias Boll
Source: ChemBioChem (article, 2021-09-23)
Field: Materials Science — Materials Chemistry

Structural Basis of Cyclic 1,3‐Diene Forming Acyl‐Coenzyme A Dehydrogenases The biologically important, FAD-containing acyl-coenzyme A (CoA) dehydrogenases (ACAD) usually catalyze the anti-1,2-elimination of a proton and a hydride of aliphatic CoA thioesters. Here, we report on the structure and function of an ACAD from anaerobic bacteria catalyzing the unprecedented 1,4-elimination at C3 and C6 of cyclohex-1-ene-1-carboxyl-CoA (Ch1CoA) to cyclohex-1,5-diene-1-carboxyl-CoA (Ch1,5CoA) and at C3 and C4 of the latter to benzoyl-CoA. Based on high-resolution Ch1CoA dehydrogenase crystal structures, the unorthodox reactivity is explained by the presence of a catalytic aspartate base (D91) at C3, and by eliminating the catalytic glutamate base at C1. Moreover, C6 of Ch1CoA and C4 of Ch1,5CoA are positioned towards FAD-N5 to favor the biologically relevant C3,C6- over the C3,C4-dehydrogenation activity. The C1,C2-dehydrogenation activity was regained by structure-inspired amino acid exchanges. The results provide the structural rationale for the extended catalytic repertoire of ACADs and offer previously unknown biocatalytic options for the synthesis of cyclic 1,3-diene building blocks. Dehydrogenation ; Cofactor ; Chemistry ; Diene ; Stereochemistry ; Dehydrogenase ; Coenzyme A ; Catalysis ; Reactivity (psychology) ; Enzyme ; Biochemistry ; Organic chemistry ; Reductase Enzyme Structure and Function ; Enzyme Catalysis and Immobilization ; Microbial Metabolic Engineering and Bioproduction
Authors: Alexander Brodsky | Natan Kaplan | Karen I. Goldberg
Source: Microscopy Today (article, 2021-05-01)
Field: Materials Science — Surfaces, Coatings and Films

STED Simulation and Analysis by the Enhanced Geometrical Ray Tracing Method STED microscopy ; Ray tracing (physics) ; Tracing ; X-ray ; Physics ; Computer science ; Computer graphics (images) ; Optics ; Programming language Electron and X-Ray Spectroscopy Techniques
Authors: Maarten Degen
Source: Research Repository (Delft University of Technology) (article, 2021-04-06)
Field: Materials Science — Materials Chemistry

On the creation, coherence and entanglement of multi-defect quantum registers in diamond Due to its long spin coherence and coherent spin-photon interface the nitrogen vacancy (NV) center in diamond has emerged as a promising platform for quantum science and technology, including quantum networks, quantum computing and quantum sensing. In recent years larger quantum systems have been demonstrated by using optical entanglement links between distant NV centers. These systems were based on high-quality NV centers that exhibit good optical coherence. State-of-the-art experiments with such systems have shown deterministic delivery of entanglement across a two-node quantum network as well as genuine multi-partite entanglement across a three-node quantum network. The additional capability to create larger quantum registers by direct magnetic coupling between high-quality NV centers and to other nearby defects would provide new opportunities for quantum memories in quantum networks but also for enhanced sensing protocols and spin chains for quantum computation architectures. In this thesis, we investigate methods to create larger quantum registers based on magnetic coupling and develop techniques to address and control individual defects in a system consisting of multiple defects. The results provide new insights for extended quantum registers based on magnetically coupled defects... Quantum sensor ; Quantum network ; Quantum entanglement ; Quantum technology ; Quantum computer ; Coherence (philosophical gambling strategy) ; Physics ; Quantum ; Quantum information ; Quantum mechanics ; Open quantum system Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques ; Quantum optics and atomic interactions
Authors: Raquel Fernández-Martín | María J. Calderón | Laura Fanfarillo | Belén Valenzuela
Source: Condensed Matter (article, 2021-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Role of Orbital Nesting in the Superconductivity of Iron-Based Superconductors We analyze the magnetic excitations and the spin-mediated superconductivity in iron-based superconductors within a low energy model that operates in the band basis, but fully incorporates the orbital character of the spin excitations. We show how the orbital selectivity, encoded in our low energy description, simplifies substantially the analysis and allows for analytical treatments, while retaining all the main features of both spin excitations and gap functions computed using multiorbital models. Importantly, our analysis unveils the orbital matching between the hole and electron pockets as the key parameter to determine the momentum dependence and the hierarchy of the superconducting gaps, instead of the Fermi surface matching, as in the common nesting scenario. Superconductivity ; Fermi surface ; Spin (aerodynamics) ; Angular momentum ; Nesting (process) ; Electron ; Momentum (technical analysis) ; Hierarchy ; Fermi energy Iron-based superconductors research ; Electronic and Structural Properties of Oxides ; Rare-earth and actinide compounds
Authors: Nikola Nešić | Milan Cajić | Danilo Karličić | Aleksandar Obradović | Julijana Simonović
Source: Research Square (preprint, 2021-09-21)
Field: Materials Science — Materials Chemistry

Nonlinear Vibration of a Nonlocal Functionally Graded Beam on Fractional Visco-Pasternak Foundation Nonlinear system ; Foundation (evidence) ; Vibration ; Viscoelasticity ; Beam (structure) ; Fractional calculus ; Structural engineering ; Physics ; Mathematics ; Materials science ; Mathematical analysis ; Engineering ; Acoustics ; Composite material ; Political science ; Law Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Rama K. Vasudevan | Ayana Ghosh | Maxim Ziatdinov | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2021-04-23)
Field: Materials Science — Materials Chemistry

Exploring Electron Beam Induced Atomic Assembly via Reinforcement Learning in a Molecular Dynamics Environment Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanoscience and nanotechnology. While traditionally implemented via scanning probe microscopy techniques, recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature and proceeds via a large number of weakly-understood individual steps. Hence, harnessing an electron beam towards atomic assembly requires automated methods to control the parameters and positioning of the beam in such a way as to fabricate atomic-scale structures reliably. Here, we create a molecular dynamics environment wherein individual atom velocities can be modified, effectively simulating a beam-induced interaction, and apply reinforcement learning to model construction of specific atomic units consisting of Si dopant atoms on a graphene lattice. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants, whilst at the same time minimizing the amplitude of momentum changes. Inspection of the learned policies indicates that of fundamental importance is the component of velocity perpendicular to the material plane, and further, that the high stochasticity of the environment leads to conservative policies. This study shows the potential for reinforcement learning agents trained in simulated environments for potential use as atomic scale fabricators, and further, that the dynamics learned by agents encode specific elements of important physics that can be learned. Atomic units ; Reinforcement learning ; Nanotechnology ; Atom (system on chip) ; Graphene ; Molecular dynamics ; Dopant ; Scanning transmission electron microscopy ; Materials science ; Computer science ; Physics ; Transmission electron microscopy ; Artificial intelligence ; Optoelectronics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Molecular Junctions and Nanostructures ; Machine Learning in Materials Science
Authors: Aini Palizhati | Muratahan Aykol | Santosh K. Suram | Jens S. Hummelshøj | Joseph H. Montoya
Source: ChemRxiv (preprint, 2021-03-29)
Field: Materials Science — Materials Chemistry

Multi-fidelity Sequential Learning for Accelerated Materials Discovery We introduce a new agent-based framework for materials discovery that combines multi-fidelity modeling and sequential learning to lower the number of expensive data acquisitions while maximizing discovery. We demonstrate the framework's capability by simulating a materials discovery campaign using experimental and DFT band gap data. Using these simulations, we determine how different machine learning models and acquisition strategies influence the overall rate of discovery of materials per experiment. The framework demonstrates that including lower fidelity (DFT) data, whether as a-priori knowledge or using in-tandem acquisition, increases the discovery rate of materials suitable for solar photoabsorption. We also show that the performance of a given agent depends on data size, model selection, and acquisition strategy. As such, our framework provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. Computer science ; Fidelity ; Knowledge extraction ; High fidelity ; Hyperparameter ; A priori and a posteriori ; Machine learning ; Data acquisition ; Data discovery ; Discovery learning ; Artificial intelligence ; Data mining ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Expert finding and Q&A systems
Authors: Amir Abbas Kazemzadeh Farizhandi | Omar Betancourt | Mahmood Mamivand
Source: Research Square (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

Deep Learning Approach for Chemistry and Processing History Prediction from Materials Microstructure: Application to Spinodal Decomposition Abstract Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth. Spinodal decomposition ; Microstructure ; Convolutional neural network ; Spinodal ; Artificial intelligence ; Computer science ; Deep learning ; Phase (matter) ; Materials science ; Algorithm ; Chemistry ; Metallurgy Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Riley J. Hickman | Florian Häse | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: arXiv (Cornell University) (preprint, 2021-03-05)
Field: Materials Science — Materials Chemistry

Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation. However, expensive measurements are required to accurately estimate materials properties, and can quickly become a hindrance to exhaustive materials discovery campaigns. Here, we introduce Gemini: a data-driven model capable of using inexpensive measurements as proxies for expensive measurements by correcting systematic biases between property evaluation methods. We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values. In a regression setting, we showcase the ability of our method to make accurate predictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite materials. We further demonstrate the benefits that Gemini provides to autonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a scalable optimization framework leveraging multiple sources of measurement. Finally, we simulate an autonomous materials discovery platform for optimizing the activity of electrocatalysts for the oxygen evolution reaction. Realizing autonomous workflows with Gemini, we show that the number of measurements of a composition space comprising expensive and rare metals needed to achieve a target overpotential is significantly reduced when measurements from a proxy composition system with less expensive metals are available. Workflow ; Scalability ; Computer science ; Bayesian optimization ; Bayesian probability ; Proxy (statistics) ; Data mining ; Machine learning ; Artificial intelligence ; Database Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Benjamin P. MacLeod | Fraser G. L. Parlane | Connor C. Rupnow | Kevan E. Dettelbach | Michael Scott Elliott | Thomas D. Morrissey | Ted H. Haley | Oleksii Proskurin | Michael B. Rooney | Nina Taherimakhsousi | David Dvořák | Hsi Nien Chiu | Christopher E. B. Waizenegger | Karry Ocean | Mehrdad Mokhtari | Curtis P. Berlinguette
Source: arXiv (Cornell University) (preprint, 2021-06-01)
Field: Materials Science — Materials Chemistry

Advancing the Pareto front for thin-film materials using a self-driving\n laboratory Useful materials must satisfy multiple objectives, where the optimization of\none objective is often at the expense of another. The Pareto front reports the\noptimal trade-offs between competing objectives. Here we report a self-driving\nlaboratory, "Ada", that defines the Pareto front of conductivities and\nprocessing temperatures for palladium films formed by combustion synthesis. Ada\nidentified previously untested combustion synthesis conditions that resulted in\nthe discovery of lower processing temperatures (below 200 {\\deg}C) relative to\nthe prior art for this technique (250 {\\deg}C), a temperature difference that\nmakes the coating of different commodity plastic materials possible (e.g.,\nNafion, polyethersulfone). These conditions enabled us to use combustion\nsynthesis to spray coat uniform palladium films with moderate conductivity (1.1\n$\\times$ 10$^5$ S m$^{-1}$) at 191 {\\deg}C. Spray coating at 226 {\\deg}C\nyielded films with conductivities (2.0 $\\times$ 10$^6$ S m$^{-1}$) comparable\nto those of sputtered films (2.0 to 5.8 $\\times$ 10$^6$ S m$^{-1}$). This work\nshows how self-driving laboratories can discover materials satisfying multiple\nobjectives.\n Combustion ; Coating ; Materials science ; Thin film ; Palladium ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Memory and Neural Computing