jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiqing Shu | Penglai Guo | Xiaohui Li | Guian Li | Peng Wang | Ge Shen | Jianqing Li
Source: Frontiers in Chemistry (article, 2019-10-24)
Field: Materials Science — Materials Chemistry

Gold Nanorods as Saturable Absorber for Harmonic Soliton Molecules Generation Gold nanorods (GNRs) has been investigated in the field of chemistry, optoelectronics, and medicine for their tenability, compatibility, electromagnetics, and excellent photonics properties. Especially, GNRs, used to generate ultrashort pulse, have been studied recently. However, multiple pulses evolution based on GNRs needs to be further explored. In this article, GNRs are synthesized by seed-mediated growth method, characterized systematically and been chosen as saturable absorber (SA) to apply in ultrafast photonics. The GNRs SA presents a saturable intensity of 266 MW/cm<sup>2</sup>, modulation depth of 0.6%, and non-saturable loss of 51%. Furthermore, a passively mode-locked erbium-doped fiber laser based on GNRs SA with femtosecond pulse is demonstrated. Thanks to the excellent properties of GNRs, by adjusting the cavity polarization direction with the proposed GNRs SA, soliton molecules operation with spectrum modulation period of 3.3 nm and pulse modulation interval of 2.238 ps is directly obtained. For the most important, 9th-order harmonic soliton molecules have been generated in the laser cavity for the first time. It is demonstrated that GNRs can be a novel type of non-linear optical (NLO) device and have potential applications in the field of ultrafast photonics. Saturable absorption ; Nanorod ; Molecule ; High harmonic generation ; Materials science ; Soliton ; Optoelectronics ; Nanotechnology ; Nonlinear system ; Optics ; Physics ; Quantum mechanics ; Fiber laser ; Laser ; Wavelength Porphyrin and Phthalocyanine Chemistry ; Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Junctions and Nanostructures
Authors: Froze Jameel | Emilija Kohls | Matthias Stein
Source: ChemCatChem (article, 2019-07-19)
Field: Chemistry — Organic Chemistry

Mechanism and Control of the Palladium‐Catalyzed Alkoxycarbonylation of Oleochemicals from Sustainable Sources Abstract The transformation of chemical production processes to a sustainable feedstock from renewable sources requires a careful assessment of current thermodynamic data, reaction mechanisms and kinetics. The Pd‐catalyzed alkoxycarbonylation of the long chain olefin methyl 10‐undecenoate (10‐UME) from castor oil with methanol yields the building blocks for a renewable polyamide. The mechanism for the complex multi‐step reaction cycle including active catalyst formation was elucidated. The experimental catalyst selectivity with 1,2‐bis(di‐tert‐butylphosphino‐methyl)benzene (1,2‐DTBPMB) as a ligand towards the desired linear diester product can be reproduced and rationalized. The mechanisms of possible side reactions and well as catalyst inhibition by carbon monoxide were also investigated. Solvent effects have an influence on reaction equilibria and transition barriers. These were considered in polar (methanol) and nonpolar (dodecane) media using implicit or mixed cluster/continuum solvation models when explicit solvent coordination was critical. Catalysis ; Chemistry ; Organic chemistry ; Methanol ; Solvent ; Dodecane Organometallic Complex Synthesis and Catalysis ; Metal complexes synthesis and properties ; Asymmetric Hydrogenation and Catalysis
Authors: Gianvito Grasso | Martina Rebella | Umberto Morbiducci | Jack A. Tuszyński | Andrea Danani | Marco A. Deriu
Source: Frontiers in Bioengineering and Biotechnology (article, 2019-04-24)
Field: Materials Science — Biomaterials

The Role of Structural Polymorphism in Driving the Mechanical Performance of the Alzheimer's Beta Amyloid Fibrils Alzheimer's Disease (AD) is related with the abnormal aggregation of amyloid β-peptides Aβ<sub>1-40</sub> and Aβ<sub>1-42</sub>, the latter having a polymorphic character which gives rise to U- or S-shaped fibrils. Elucidating the role played by the nanoscale-material architecture on the amyloid fibril stability is a crucial breakthrough to better understand the pathological nature of amyloid structures and to support the rational design of bio-inspired materials. The computational study here presented highlights the superior mechanical behavior of the S-architecture, characterized by a Young's modulus markedly higher than the U-shaped architecture. The S-architecture showed a higher mechanical resistance to the enforced deformation along the fibril axis, consequence of a better interchain hydrogen bonds' distribution. In conclusion, this study, focusing the attention on the pivotal multiscale relationship between molecular phenomena and material properties, suggests the S-shaped Aβ<sub>1-42</sub> species as a target of election in computational screen/design/optimization of effective aggregation modulators. Fibril ; Rational design ; Amyloid fibril ; Hydrogen bond ; Biophysics ; Materials science ; Amyloid beta ; Molecular dynamics ; Amyloid (mycology) ; Polymorphism (computer science) ; Amyloid β ; Nanotechnology ; Chemistry ; Disease ; Molecule ; Peptide ; Biology ; Allele ; Medicine ; Biochemistry ; Pathology ; Gene ; Computational chemistry Supramolecular Self-Assembly in Materials ; Alzheimer's disease research and treatments ; Bone Tissue Engineering Materials
Authors: Swastibrata Bhattacharyya | Ryoji Sahara | Kaoru Ohno
Source: Nature Communications (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

A first-principles phase field method for quantitatively predicting multi-composition phase separation without thermodynamic empirical parameter To design tailored materials, it is highly desirable to predict microstructures of alloys without empirical parameter. Phase field models (PFMs) rely on parameters adjusted to match experimental information, while first-principles methods cannot directly treat the typical length scale of 10 μm. Combining density functional theory, cluster expansion theory and potential renormalization theory, we derive the free energy as a function of compositions and construct a parameter-free PFM, which can predict microstructures in high-temperature regions of alloy phase diagrams. Applying this method to Ni-Al alloys at 1027 °C, we succeed in reproducing evolution of microstructures as a function of only compositions without thermodynamic empirical parameter. The resulting patterns including cuboidal shaped precipitations are in excellent agreement with the experimental microstructures in each region of the Ni-Al phase diagram. Our method is in principle applicable to any kind of alloys as a reliable theoretical tool to predict microstructures of new materials. Microstructure ; Phase diagram ; Thermodynamics ; Phase (matter) ; Statistical physics ; Alloy ; Materials science ; Function (biology) ; Field (mathematics) ; Cluster expansion ; Density functional theory ; Physics ; Metallurgy ; Mathematics ; Chemistry ; Computational chemistry Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Andriy Kryshtafovych | Sony Malhotra | Bohdan Monastyrskyy | Tristan Cragnolini | Agnel Praveen Joseph | Wah Chiu | Maya Topf
Source: Proteins Structure Function and Bioinformatics (article, 2019-10-02)
Field: Materials Science — Materials Chemistry

Cryo‐electron microscopy targets in CASP13: Overview and evaluation of results Structures of seven CASP13 targets were determined using cryo-electron microscopy (cryo-EM) technique with resolution between 3.0 and 4.0 Å. We provide an overview of the experimentally derived structures and describe results of the numerical evaluation of the submitted models. The evaluation is carried out by comparing coordinates of models to those of reference structures (CASP-style evaluation), as well as checking goodness-of-fit of modeled structures to the cryo-EM density maps. The performance of contributing research groups in the CASP-style evaluation is measured in terms of backbone accuracy, all-atom local geometry and similarity of inter-subunit interfaces. The results on the cryo-EM targets are compared with those on the whole set of eighty CASP13 targets. A posteriori refinement of the best models in their corresponding cryo-EM density maps resulted in structures that are very close to the reference structure, including some regions with better fit to the density. CASP ; Cryo-electron microscopy ; Resolution (logic) ; Similarity (geometry) ; Set (abstract data type) ; A priori and a posteriori ; Algorithm ; Computer science ; Protein structure prediction ; Physics ; Artificial intelligence ; Protein structure ; Image (mathematics) ; Nuclear magnetic resonance Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Serge G. Lemay
Source: ACS Nano (article, 2019-05-31)
Field: Chemistry — Electrochemistry

Noise as Data: Nucleation of Electrochemically Generated Nanobubbles Single-entity electrochemistry aims to expand the toolkit for probing matter at the nanometer scale. Originally focused largely on electrochemically active systems, these methods are increasingly turning into versatile probes complementary to optical, electrical, or mechanical methods. Recent studies of the nucleation, structure, and stability of gas nanobubbles, which exploit electrochemistry at nanoelectrodes as generation and stabilization mechanisms, are prototypical examples. These measurements illustrate the interplay between advances in electrochemical methods and strategies for extracting microscopic information from the results. Nucleation ; Nanotechnology ; Materials science ; Electrochemistry ; Nanometre ; Exploit ; Electrochemical noise ; Computer science ; Electrode ; Chemistry Electrochemical Analysis and Applications ; Minerals Flotation and Separation Techniques ; Force Microscopy Techniques and Applications
Authors: Wei Chen | Xinxin Wang | Hao Liu | Yang Tang | Junjie Liu
Source: Electronics (article, 2019-01-09)
Field: Materials Science — Surfaces, Coatings and Films

Optimized Combination of Spray Painting Trajectory on 3D Entities In this research, a novel method of space spraying trajectory optimization is proposed for 3D entity spraying. According to the particularity of the three-dimensional entity, the finite range model is set up, and the 3D entity is patched by the surface modeling method based on FPAG (flat patch adjacency graph). After planning the spray path on each patch, the variance of the paint thickness of the discrete point and the ideal paint thickness is taken as the objective function and the trajectory on each patch is optimized. The improved GA (genetic algorithm), ACO (ant colony optimization), and PSO (particle swarm optimization) are used to solve the TTOI (tool trajectory optimal integration) problem. The practicability of the three algorithms is verified by simulation experiments. Finally, the trajectory optimization algorithm of the 3D entity spraying robot can improve the spraying efficiency. Ant colony optimization algorithms ; Trajectory optimization ; Trajectory ; Particle swarm optimization ; Computer science ; Mathematical optimization ; Range (aeronautics) ; Robot ; Set (abstract data type) ; Path (computing) ; Algorithm ; Mathematics ; Artificial intelligence ; Engineering ; Optimal control Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Plant Surface Properties and Treatments
Authors: Muhammad Imran Din | Faria Rafique | Muhammad Sadaf Hussain | Hafiz Arslan Mehmood | Sadia Waseem
Source: Science Progress (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Recent developments in the synthesis and stability of metal ferrite nanoparticles This article presents a comprehensive review on the synthesis and stability of ferrite nanoparticles such as nickel ferrite (NiFe<sub>2</sub>O<sub>4</sub>), zinc ferrite (ZnFe<sub>2</sub>O<sub>4</sub>), manganese ferrite (MnFe<sub>2</sub>O<sub>4</sub>), iron ferrite (Fe<sub>2</sub>O<sub>3</sub>), cobalt ferrite (CoFe<sub>2</sub>O<sub>4</sub>) and also mixed nanoparticles. Different synthetic methods for ferrite nanoparticles have been reviewed such as co-precipitation, thermal decomposition and hydrothermal, microwave-assisted and sonochemical methods. The effect on the stability of different capping agents like canola oil, glycerol, sodium dodecyl, sodium citrate, oleic acid, Triton-100 and sodium dodecyl benzene sulfonates has also been studied. Zinc ferrite ; Nanoparticle ; Materials science ; Ferrite (magnet) ; Thermal stability ; Chemical engineering ; Thermal decomposition ; Chemistry ; Nanotechnology ; Organic chemistry ; Composite material Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Copper-based nanomaterials and applications
Authors: Mihail Bogojeski | Leslie Vogt-Maranto | Mark E. Tuckerman | Klaus-Robert Müller | Kieron Burke
Source: ChemRxiv (preprint, 2019-05-06)
Field: Materials Science — Materials Chemistry

Quantum chemical accuracy from density functional approximations via machine learning &lt;div&gt; &lt;div&gt; &lt;p&gt;Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal/mol with presently-available functionals. &lt;i&gt;Ab initio&lt;/i&gt; methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal/mol) on test data. Moreover, density-based ∆-learning (learning only the correction to a standard DFT calculation, termed ∆-DFT) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of ∆-DFT is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C&lt;sub&gt;6&lt;/sub&gt;H&lt;sub&gt;4&lt;/sub&gt;(OH)&lt;sub&gt;2&lt;/sub&gt;) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that ∆-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; Density functional theory ; Coupled cluster ; Quantum chemical ; Ab initio ; Quantum chemistry ; Computational chemistry ; Chemistry ; Leverage (statistics) ; Molecule ; Physics ; Computer science ; Mathematics ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Hiroyuki Hayashi | Katsuyuki Hayashi | Keita Kouzai | Atsuto Seko | Isao Tanaka
Source: Chemistry of Materials (article, 2019-11-19)
Field: Materials Science — Materials Chemistry

Recommender System of Successful Processing Conditions for New Compounds Based on a Parallel Experimental Data Set We propose a machine-learning method to recommend successful processing conditions for new compounds on the basis of parallel experiments. Initially, an experimental database was constructed for 67 pseudobinary oxides registered in the Inorganic Crystal Structure Database (ICSD) by parallel experiments using 23 starting materials and 23 cation mixing ratios. Precursor powders were obtained by four synthesis methods (solid-state reaction, polymerized complex, cyclic ether sol–gel, and spray coprecipitation), which were fired at five different temperatures. This resulted in 1648 unique chemical synthesis conditions and database entries. The reactants were characterized sequentially using powder X-ray diffraction equipment with an automatic sample exchanger. The synthesis results were rated as a score, which was placed into a fifth-order tensor with 243 340 elements. The Tucker decomposition method was used to predict yet-to-be-rated scores for unexperimented processing conditions. Good predictive performance of the present model was demonstrated by cross validation. It was further evaluated by examining the presence of highly rated compositions in another database, ICDD-PDF (International Center for Diffraction Data-Powder Diffraction File). Successful processing conditions for unexperimented compositions were found to be well recommended. Coprecipitation ; Database ; Decomposition ; Diffraction ; Powder diffraction ; Materials science ; Computer science ; Chemistry ; Crystallography ; Organic chemistry ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Ali Malek | Mohammad J. Eslamibidgoli | Mehrdad Mokhtari | Qianpu Wang | Michael Eikerling | Kourosh Malek
Source: ChemPhysChem (article, 2019-10-06)
Field: Materials Science — Materials Chemistry

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts Similar to advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI-driven modeling and computational approaches that are currently applied to those objects. An application-driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO<sub>2</sub> conversion as a use case. Workflow ; Big data ; Computer science ; Data science ; Analytics ; Electrocatalyst ; Field (mathematics) ; Electrochemical energy storage ; Electrochemical energy conversion ; Characterization (materials science) ; Nanotechnology ; Materials science ; Supercapacitor ; Chemistry ; Electrochemistry ; Data mining Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Jianhua Ma | Puhan Zhang | Yaohua Tan | Avik W. Ghosh | Gia-Wei Chern
Source: Physical review. B./Physical review. B (article, 2019-02-11)
Field: Materials Science — Materials Chemistry

Machine learning electron correlation in a disordered medium Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and interatomic forces through examples, thus bypassing the computationally expensive density functional theory calculations. Combining many-body techniques with a deep-learning approach, we demonstrate that a fully connected neural network is able to learn the complex collective behavior of electrons in strongly correlated systems. Specifically, we consider the Anderson-Hubbard (AH) model, which is a canonical system for studying the interplay between electron correlation and strong localization. The ground states of the AH model on a square lattice are obtained using the real-space Gutzwiller method. The obtained solutions are used to train a multitask multilayer neural network, which subsequently can accurately predict quantities such as the local probability of double occupation and the quasiparticle weight, given the disorder potential in the neighborhood as the input. Quasiparticle ; Artificial neural network ; Statistical physics ; Electron ; Hubbard model ; Lattice (music) ; Computer science ; Physics ; Electronic correlation ; Square lattice ; Artificial intelligence ; Correlation ; Quantum mechanics ; Mathematics ; Ising model Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Samuel S. Schoenholz | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2019-12-09)
Field: Materials Science — Materials Chemistry

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python A large fraction of computational science involves simulating the dynamics of particles that interact via pairwise or many-body interactions. These simulations, called Molecular Dynamics (MD), span a vast range of subjects from physics and materials science to biochemistry and drug discovery. Most MD software involves significant use of handwritten derivatives and code reuse across C++, FORTRAN, and CUDA. This is reminiscent of the state of machine learning before automatic differentiation became popular. In this work we bring the substantial advances in software that have taken place in machine learning to MD with JAX, M.D. (JAX MD). JAX MD is an end-to-end differentiable MD package written entirely in Python that can be just-in-time compiled to CPU, GPU, or TPU. JAX MD allows researchers to iterate extremely quickly and lets researchers easily incorporate machine learning models into their workflows. Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code. In addition to making existing workloads easier, JAX MD allows researchers to take derivatives through whole-simulations as well as seamlessly incorporate neural networks into simulations. This paper explores the architecture of JAX MD and its capabilities through several vignettes. Code is available at www.github.com/google/jax-md. We also provide an interactive Colab notebook that goes through all of the experiments discussed in the paper. Computer science ; Python (programming language) ; CUDA ; Programming language ; Software ; Workflow ; Computational science ; Operating system ; Database Machine Learning in Materials Science ; Computational Physics and Python Applications ; Parallel Computing and Optimization Techniques