jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tongqi Wen | Anwen Liu | Rui Wang | Linfeng Zhang | Jian Han | Handong Wang | David J. Srolovitz | Zhaoxuan Wu
Source: arXiv (Cornell University) (preprint, 2022-11-25)
Field: Materials Science — Materials Chemistry

Atomistic modelling of all dislocations and twins in HCP and BCC Ti Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or quantitative, despite extensive experimental and simulation studies. Here, we determine all the core and twin interface properties in both HCP and BCC Ti using a Deep Potential (DP) and DFT. We determine the core structures, critical resolved shear stresses and mobilities of , , dislocations in HCP and &lt;111&gt;/2 dislocations in BCC Ti. The slip consists of slow core migration on pyramidal-I planes and fast migration on prism-planes, and is kinetically limited by cross-slips among them. This behaviour is consistent with "locking-unlocking" phenomena in TEM and is likely an intrinsic property. Large-scale DFT calculations provide a peek at the screw core and glide behaviour, which is further quantified using DP-Ti. The screw is unstable on pyramidal-II planes. The mixed is nearly sessile on pyramidal-I planes, consistent with observations of long dislocations in this orientation. The edge and mixed are unstable against a pyramidal-to-basal (PB) transition and become sessile at high temperatures, corroborate the difficulties in -axis compression of Ti. Finally, in BCC Ti, the &lt;111&gt;/2 screw has a degenerate core with average glide on {112} planes; the &lt;111&gt;/2 edge and mixed dislocations have non-dissociated cores on {110} planes. This work paints a self-consistent, complete picture on all dislocations in Ti, rationalises previous experimental observations and points to future HRTEM examinations of unusual dislocations such as the mixed and PB transformed cores. Materials science ; Dislocation ; Crystal twinning ; Condensed matter physics ; Crystallography ; Partial dislocations ; Core (optical fiber) ; Slip (aerodynamics) ; Composite material ; Microstructure ; Chemistry ; Physics ; Thermodynamics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Microstructure and mechanical properties
Authors: Howie Joress | Brian DeCost | Najlaa Hassan | Trevor Michael Braun | Justin M. Gorham | Jason Hattrick‐Simpers
Source: arXiv (Cornell University) (preprint, 2022-04-01)
Field: Chemistry — Electrochemistry

Development of an automated millifluidic platform and data-analysis pipeline for rapid electrochemical corrosion measurements: a pH study on Zn-Ni We describe the development of a millifluidic based scanning droplet cell platform for rapid and automated corrosion. This system allows for measurement of corrosion properties (e.g., open circuit potential, corrosion current through Tafel and linear polarization resistance measurements, and cyclic voltammograms) on a localized section of a planar sample. Our system is highly automated and flexible, allowing for scripted changing and mixing of solutions and point-to-point motion on the sample. We have also created an automated data analysis pipeline. Here we demonstrate this tool by corroding a plate of electroplated Zn$_{85}$Ni$_{15}$ alloy over a range of pH values and correlate our results with XPS measurements and literature. Tafel equation ; Corrosion ; Electroplating ; Materials science ; Planar ; Polarization (electrochemistry) ; Alloy ; Electrochemistry ; Pipeline (software) ; Metallurgy ; Analytical Chemistry (journal) ; Nanotechnology ; Computer science ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Corrosion Behavior and Inhibition
Authors: Shijing Zhao | Wenju Zhou | Xiaojun Xiang | Xuyan Cao | Ning Chen | Weifeng Chen | Xiaohui Yu | Bingmin Yan | Huiyang Gou
Source: Materials (article, 2022-04-21)
Field: Materials Science — Materials Chemistry

Structure Determination, Mechanical Properties, Thermal Stability of Co2MoB4 and Fe2MoB4 The precise determination of atomic position of materials is critical for understanding the relationship between structure and properties, especially for compounds with light elements of boron and single or multiple transition metals. In this work, the single crystal X-ray diffraction is employed to analyze the atomic positions of Co<sub>2</sub>MoB<sub>4</sub> and Fe<sub>2</sub>MoB<sub>4</sub> with a Ta<sub>3</sub>B<sub>4</sub>-type structure, and it is found that the lengths of B-B bonds connecting the two zig-zag boron chains are 1.86 Å and 1.87 Å, but previously unreported 1.4 Å. Co and Fe atoms occupy the same crystallographic position in lattice for the doped samples and the valence is close to the metal itself, and Co/Fe K-edge X-ray Absorption Fine Structure(XAFS) spectra of borides with different ratios of Co to Fe are collected to detect the local environment and chemical valence of Co and Fe. Vickers hardness and nano indentation measurements are performed, together with the Density Functional Theory (DFT) calculations. Finally, Co<sub>2</sub>MoB<sub>4</sub> possess better thermal stability than Fe<sub>2</sub>MoB<sub>4</sub> evaluated by Thermogravimetric Differential Thermal Analysis (TG-DTA) results. X-ray absorption fine structure ; Materials science ; Valence (chemistry) ; Density functional theory ; Crystallography ; Thermal stability ; Thermogravimetric analysis ; Crystal structure ; Boron ; Differential thermal analysis ; Diffraction ; XANES ; Analytical Chemistry (journal) ; Spectral line ; Spectroscopy ; Chemistry ; Computational chemistry ; Optics Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Rare-earth and actinide compounds
Authors: Yanling Schneider | Vighnesh Prabhu | Kai Höss | W. Wasserbäch | Siegfried Schmauder | Zhangjian Zhou
Source: Materials (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

Many-Scale Investigations of the Deformation Behavior of Polycrystalline Composites: I—Machine Learning Applied for Image Segmentation Our work investigates the polycrystalline composite deformation behavior through multiscale simulations with experimental data at hand. Since deformation mechanisms on the micro-level link the ones on the macro-level and the nanoscale, it is preferable to perform micromechanical finite element simulations based on real microstructures. The image segmentation is a necessary step for the meshing. Our 2D EBSD images contain at least a few hundred grains. Machine learning (ML) was adopted to automatically identify subregions, i.e., individual grains, to improve local feature extraction efficiency and accuracy. Denoising in preprocessing and postprocessing before and after ML, respectively, is beneficial in high quality feature identification. The ML algorithms used were self-developed with the usage of inherent code packages (Python). The performances of the three supervised ML models-decision tree, random forest, and support vector machine-are compared herein; the latter two achieved accuracies of up to 99.8%. Calculations took about 0.5 h from the original input dataset (EBSD image) to the final output (segmented image) running on a personal computer (CPU: 3.6 GHz). For a realizable manual pixel sortation, the original image was firstly scaled from the initial resolution 10802 pixels down to 3002. After ML, some manual work was necessary due to the remaining noises to achieve the final image status ready for meshing. The ML process, including this manual work time, improved efficiency by a factor of about 24 compared to a purely manual process. Simultaneously, ML minimized the geometrical deviation between the identified and original features, since it used the original resolution. For serial work, the time efficiency would be enhanced multiplicatively. Computer science ; Pixel ; Artificial intelligence ; Preprocessor ; Standard deviation ; Segmentation ; Random forest ; Algorithm ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Calcium Carbonate Crystallization and Inhibition
Authors: Anas Karuth | Gerardo M. Casañola‐Martín | Levi Lystrom | Wenfang Sun | Dmitri S. Kilin | Svetlana Kilina | Bakhtiyor Rasulev
Source: ChemRxiv (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Combined Machine Learning, Computational and Experimental Analysis of the Iridium(III) Complexes with Red to Near-IR Emission Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical properties. In this work a combined experimental and computational approach applied to investigate the optical properties of monocationic Ir(III) complexes. In result, an interpretative machine learning-based Quantitative Structure-Activity Relationship (QSAR) model was successfully developed, which can reliably predict the emission wavelength of the Ir(III) complexes and provides foundations for theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to mechanistically explain the differences in emission wavelengths between structurally different individual Ir(III) complexes. To the best of our knowledge, this is the first attempt to develop predictive machine learning (QSAR) model for the optical properties of Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values for the training and test sets of 0.84 and 0.87, respectively, and by performing the validation using y-scrambling techniques. A notable relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths was revealed. This combined experimental and computational approach shows a great potential for rational design of new Ir(III) complexes with desired optical properties. Moreover, the developed methodology could be extended to other octahedral transition-metal complexes. Iridium ; Quantitative structure–activity relationship ; Diimine ; Wavelength ; Chemistry ; Molecule ; Rational design ; Computational chemistry ; Computer science ; Materials science ; Machine learning ; Nanotechnology ; Catalysis ; Optoelectronics ; Organic chemistry Lanthanide and Transition Metal Complexes ; Organic Light-Emitting Diodes Research ; Machine Learning in Materials Science
Authors: Eugen Hruška | Liang Zhao | Fang Liu
Source: ChemRxiv (preprint, 2022-12-11)
Field: Materials Science — Materials Chemistry

Ground truth explanation dataset for chemical property prediction on molecular graphs Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods regularly disagree substantially, causing uncertainty about which explainability is correct. To determine a ground truth for attributions, we define chemical operations which avoid alchemical steps or approximations and allow extracting one attribution per atom or bond from existing datasets of chemical properties. This general procedure allows generating large datasets of ground truth attributions. The approach allowed us to create a ground truth explanation dataset with more than 5 million data points for the HOMO-LUMO gap chemical property. This open-source dataset of atomistic ground truth explainability may serve as a reference for XAI approaches. Ground truth ; Property (philosophy) ; Attribution ; Interpretation (philosophy) ; Computer science ; Artificial intelligence ; Data mining ; Epistemology ; Philosophy ; Psychology ; Social psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Patrick Hosein | Jaimie Greasley
Source: arXiv (Cornell University) (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

An Optimization-Based Supervised Learning Algorithm for PXRD Phase Fraction Estimation In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of each phase in the sample. Machine Learning algorithms (e.g., Artificial Neural Networks) have been applied to perform such difficult tasks in powder diffraction analysis, but typically require a significant number of training samples for acceptable performance. We have developed an approach that performs well even with a small number of training samples. We apply a fixed-point iteration algorithm on the labelled training samples to estimate monophasic spectra. Then, given an unknown sample spectrum, we again use a fixed-point iteration algorithm to determine the weighted combination of monophase spectra that best approximates the unknown sample spectrum. These weights are the desired phase fractions for the sample. We compare our approach with several traditional Machine Learning algorithms. Phase (matter) ; Algorithm ; Sample (material) ; Artificial neural network ; Fraction (chemistry) ; Powder diffraction ; Point (geometry) ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Chemistry ; Crystallography ; Chromatography X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Akerke T. Kazhmuratova | M.S. Zhunissova | J. Plocek | Vitaliy N. Fomin | Akmaral Zh. Sarsenbekova | Т. О. Хамитова
Source: Bulletin of the Karaganda University Chemistry series (article, 2022-07-15)
Field: Chemistry — Organic Chemistry

Influence of the RAFT Agent on the Reaction Direction of the Copolymerization of Polypropylene Glycol Maleate with Acrylic Acid This work demonstrates the capability of synthesizing new polymers based on unsaturated polyester and acrylic acid in the presence of a chain-transfer agent in a dioxane solution. The initial unsaturated polyester resin was derived from the polycondensation reaction of alcohol and maleic anhydride. The molecular weight of the polypropylene glycol maleate was determined by gel permeation chromatography. The dependence of the structure, network density and product yield on the concentration of the RAFT agent was proven. It was found that the greater the amount of chain-transfer agent in the monomer mixture, the lower the yield of the crosslinked polymer and the greater the yield of the branched copolymer. The composition of the studied co-polymers was determined by FTIR spectroscopy in conjunction with the chemometric method of partial list squares in the R environment. The synthesized objects were characterized by infrared spectroscopy methods and were approximated by Gaussian contours. The study results show that the RAFT agent concentration, as well as the initial ratio of monomers in the copolymer affects the products yield. The surface topography of the studied copolymers was recorded by scanning electron microscopy. The molecular chain structure of the branched copolymers was confirmed by NMR spectroscopy. Copolymer ; Polymer chemistry ; Monomer ; Maleic anhydride ; Acrylic acid ; Polymer ; Raft ; Chain transfer ; Materials science ; Gel permeation chromatography ; Yield (engineering) ; Polypropylene glycol ; Chemistry ; Organic chemistry ; Polyethylene glycol ; Radical polymerization ; Composite material Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Thermal and Kinetic Analysis
Authors: Abdulrahman Aldossary | Martí Gimferrer | Yuezhi Mao | Hongxia Hao | Akshaya Kumar Das | Pedro Salvador | Teresa Head‐Gordon | Martin Head‐Gordon
Source: ChemRxiv (preprint, 2022-11-28)
Field: Materials Science — Materials Chemistry

Force Decomposition Analysis: A method todecompose intermolecular forces into physicallyrelevant component contributions Computational quantum chemistry can be more than just numerical experiments when methods are specifically adapted to investigate chemical concepts. One important example is the development of energy decomposition analysis (EDA) to reveal the physical driving forces behind intermolecular interactions. In EDA, typically the interaction energy from a good-quality density functional theory (DFT) calculation is decomposed into multiple additive components that unveil permanent and induced electrostatics, Pauli repulsion, dispersion, and charge-transfer contributions to non-covalent interactions. Herein, we formulate, implement and investigate decomposing the forces associated with intermolecular interactions into the same components. The resulting force decomposition analysis (FDA) is potentially useful as a complement to the EDA to understand chemistry, while also providing far more information than an EDA for data analysis purposes such as training physics-based force fields. We apply the FDA based on absolutely localized molecular orbitals (ALMOs) to analyze interactions of water with sodium and chloride ions as well as in the water dimer. We also analyze the forces responsible for geometric changes in carbon dioxide upon adsorption onto (and activation by) gold and silver anions. We also investigate how the force components of an EDA-based force field for water clusters, namely MB-UCB, compare to those from force decomposition analysis. Intermolecular force ; Water dimer ; Force field (fiction) ; Chemistry ; Chemical physics ; Computational chemistry ; Decomposition ; Density functional theory ; Atomic orbital ; Electrostatics ; London dispersion force ; Interaction energy ; Potential energy ; Molecule ; Nanotechnology ; Physics ; Materials science ; Physical chemistry ; van der Waals force ; Classical mechanics ; Quantum mechanics ; Electron ; Organic chemistry ; Hydrogen bond Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Saliha Özarslan | Mustafa Durgun | Mustafa Kaya
Source: International Journal of Innovative Engineering Applications (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

Metal İçerikli Asit Çözeltisi ile Muamele Edilmiş Atık Asfaltın Hidrojen Üretiminde Katalizör Olarak Kullanımı Orijinal Bilimsel Makale Bu çalışmada, asetik asit ile muamele edilmiş metal katkılı atık asfalt katalizörü kullanılarak sodyum bor hidrürün metanolizinde hidrojen sentezi gerçekleştirilmiştir. Farklı metal türleri denenerek en verimli metal türü belirlenmesinin ardından farklı oranlarda metal kullanılmış ve en etkin katalizör üretilmiştir. En verimli katalizör, 3M asetik asit ve % 30 Cu ile muamele edilmiş atık asfalt katalizörüdür. Seçilen katalizör varlığında, en yüksek hidrojen verimini elde etmek için farklı katalizör miktarları, farklı sodyum bor hidrür konsantrasyonları ve farklı sıcaklıklar kullanılarak deneyler yapılmış ve sonuçlar yorumlanmıştır. Maksimum hidrojen üretimi verimi 9518,3 mLdk-1g-1 ve katalizörün aktivasyon enerjisi 38,2 kj/mol olarak belirlenmiştir. Chemistry ; Nuclear chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Nanomaterials for catalytic reactions
Authors: Swati Kurmi | Praveen Kumar Litoriya | Ashish Verma
Source: Indian Journal of Science and Technology (article, 2022-04-22)
Field: Chemistry — Organic Chemistry

Structural Interpretation and Photoluminescence Properties of Sr0:90xCa0:10Al12O19:Dyx 3+Nanophosphors Synthesis by Combustion system Objectives: To synthesize a phosphor of more intense, high color purity, and to last longer. Methods: We have synthesized Sr0:90􀀀xCa0:10Al12O19:Dy3+ x (x=0.00, 0.01, 0.03, 0.05, 0.07) phosphors using a low temperature (560  10 )◦C combustion system. X-ray diffraction, Field Emission Scanning Electron Microscopy (FE-SEM), and EDS spectra were used to perform phase identification, morphological examination, and elemental analysis. Spectroscopic photoluminescence techniques of the described phosphor were used to investigate its optical properties. Findings: According to scherrer’s formula, the crystal size of strontium calcium aluminates phosphor is between 12.58 and 84.67 nm. The Dy3+ emission peaks at 475 nm (blue), 573 nm (yellowgreen), and 666 nm (red) in the phosphor. PL emission spectra can be attributed to the 4f-4f transition of Dy3+ ions. The phosphor color purity is 15%, and the average life time is 0.96 ms, indicating that it will survive a long last. The intensity at the wavelengths of 475nm and 575 nm is 40000 counts and 35000 counts, respectively. Novelty: With a 0.10 molar calcium concentration, we have synthesized strontium aluminates phosphor with improved properties. The described phosphor has long-lasting applications as a refractory structural material, great and useful material for pale-orange colorants, and may be employed in many solid-state lighting technologies. Keywords: Strontium Aluminates Phosphor; Paleorange Phosphor; Photoluminescence; Correlated Color Temperature; Combustion System Phosphor ; Photoluminescence ; Materials science ; Strontium ; Analytical Chemistry (journal) ; Scanning electron microscope ; Combustion ; Emission spectrum ; Mineralogy ; Chemistry ; Spectral line ; Optoelectronics ; Physical chemistry ; Physics ; Composite material Polydiacetylene-based materials and applications
Authors: Gloria Borgstahl | William E. Lutz | Jahaun Azadmanesh | Jeffrey J. Lovelace | Carol Kolar | Leighton Coates | Kevin L. Weiss
Source: Research Square (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Perfect Crystals: Microgravity capillary counterdiffusion crystallization of human manganese superoxide dismutase for neutron crystallography Crystallization ; Manganese ; Crystallography ; Chemistry ; Capillary action ; Materials science ; Superoxide dismutase ; Biochemistry ; Organic chemistry Enzyme Structure and Function ; Metabolism and Genetic Disorders ; Biochemical effects in animals
Authors: Jifu Du | Dongcheng Fan | Zhen Dong | Xin Yang | Long Zhao
Source: Research Square (preprint, 2022-01-21)
Field: Materials Science — Biomaterials

Facile Fabrication of Quaternized Sisal Fiber by Electron Beam Radiation and its Effective Adsorption for Indigo Carmine from Aqueous Solution Indigo carmine ; Aqueous solution ; Adsorption ; SISAL ; Fabrication ; Cathode ray ; Fiber ; Materials science ; Chemistry ; Chemical engineering ; Nuclear chemistry ; Electron ; Composite material ; Organic chemistry ; Physics ; Engineering Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications