jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ghulam Mustafa | Ishfaq Ahmed | M. U. Subhani | M. Shabaz | Muhammad Hammad Iqbal | Enam-ul-Haq | S. Abbas | R. Mehmood
Source: Journal of Ovonic Research (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Investigation of temperature effect on structural and magnetic properties of La-doped cobalt ferrites In the present work, La+3 - substituted Co-ferrites of chemical composition CoLa0.06Fe1.94O4 (x = 0.06) was prepared applying the co-precipitation method and prepared samples was annealed at different temperatures such as 700-1200 °C for 6 h with step interval of 100 °C. The uniqueness of this work is that only x= 0.06 sample to study the effect of cubic spinel microstructure at different annealed temperatures was selected. The prepared samples were characterized by X-ray diffraction (XRD) technique, Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR) and VSM. The XRD and FTIR techniques confirm the spinel ferrite structure. The average crystallite size was found 26.5 nm to 106 nm by using Scherer’s formula. When the temperature was increased, average crystallite size also increased. SEM images showed that the size of grain increased with increase of annealing temperature and confirmed the particle size as analysed by XRD. EDX spectra clarified the presence of elements in the samples by showing their respective characteristics peaks of Co, O, La, and as per prepared composition. From M-H loop, the obtained values of Ms = 59.74 emu/g and coercivity ≥ 700 Oe showed that prepared magnetic samples may be applied as magnetic recording media. Crystallite ; Coercivity ; Analytical Chemistry (journal) ; Materials science ; Spinel ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Microstructure ; Grain size ; Annealing (glass) ; Ferrite (magnet) ; Particle size ; Doping ; Mineralogy ; Metallurgy ; Chemistry ; Composite material ; Optics ; Physics ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Semiconductor materials and interfaces ; Advancements in Battery Materials
Authors: Melvin M. Vopson | Y. K. Fetisov | I. D. Hepburn
Source: Magnetochemistry (article, 2021-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solid-State Heating Using the Multicaloric Effect in Multiferroics The multicaloric effect is defined as the adiabatic reversible temperature change in multiferroic materials induced by the application of an external electric or magnetic field, and it was first theoretically proposed in 2012. The multicaloric effects in multiferroics, as well as other similar caloric effects in single ferroics, such as magnetocaloric, elastocaloric, barocaloric, and electrocaloric, have been the focus of much research due to their potential commercialization in solid-state refrigeration. In this short communication article, we examine the thermodynamics of the multicaloric effect for solid-state heating applications. A possible thermodynamic multicaloric heating cycle is proposed and then implemented to estimate the solid-state heating effect for a known electrocaloric system. This work offers a path to implementing caloric and multicaloric effects to efficient heating systems, and we offer a theoretical estimate of the upper limit of the temperature change achievable in a multicaloric cooling or heating effect. Multiferroics ; Adiabatic process ; Materials science ; Refrigeration ; Condensed matter physics ; Commercialization ; Thermodynamics ; Magnetic refrigeration ; Electric field ; Solid-state ; Magnetic field ; Work (physics) ; Engineering physics ; Dielectric ; Physics ; Magnetization ; Optoelectronics ; Quantum mechanics ; Ferroelectricity Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Nakiraboina Venkatesh | Shyam sunder Goud | Hari Kumar Nama | D. Ravinder | Veera Somaiah P.
Source: Research Square (preprint, 2021-02-24)
Field: Materials Science — Materials Chemistry

FTIR, Optical, Electrical and Magnetic properties of Sm+3 doped Mg Nano Ferrites by Citrate-Gel Auto combustion method Doping ; Nano- ; Combustion ; Fourier transform infrared spectroscopy ; Sol-gel ; Materials science ; Chemistry ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Composite material ; Optoelectronics ; Physical chemistry Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magneto-Optical Properties and Applications
Authors: Mohamed Fouad | Ahmed Thabet | Ahmed A. Hassan | Ahmed Mohamed Elnoby
Source: Indonesian Journal of Electrical Engineering and Informatics (IJEEI) (article, 2021-02-09)
Field: Materials Science — Materials Chemistry

High Performance of Power Cables Using Nanocomposites Insulation Materials Partial discharges occur the biggest failure problem in power cable insulation due to distortion of electrical stress. In this paper, it has been investigated on the effect of spherical nanoparticles of Barium titanate (BaTiO 3) and Clay for enhancing electrostatic field distribution in single and three-core power cables. It has been applied new strategies of nanotechnology techniques for designing innovative polyvinyl chloride insulation materials by using nanocomposites and multi-nanocomposites. Moreover, it has been studied the electrostatic field distribution within power cable nanocomposites insulation in presence of air voids, water voids and cupper impurity voids. The electrostatic field distribution in power cable insulation has been calculated by finite element method (FEM). A comparative study has been investigated on the effect of nanocomposite insulation for enhancing electric field stress in power cables. Materials science ; Nanocomposite ; Composite material ; Power cable ; Barium titanate ; Electric field ; Finite element method ; Partial discharge ; Stress (linguistics) ; Electrical engineering ; Structural engineering ; Voltage ; Engineering ; Ceramic High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Advanced Fiber Optic Sensors
Authors: Byung Kyu Park | Charn-Jung Kim | Byeong Jun Lee
Source: Energies (article, 2021-02-11)
Field: Materials Science — Polymers and Plastics

Effect of Foaming Temperature on Microstructure, Mechanical Properties and Flame Spread Rate in PET–PEN Copolymer Polymer foams are expanding their applications into functional materials. Partial foam structure has been fabricated in polyethylene terephthalate–polyethylene naphthalate (PET–PEN) copolymer by solid state foaming. Through SEM image analyses, a potential to fabricate gradient foam structures with micropores and unfoamed skin layers has been identified. The post-foaming temperature Tf tune the pore size distribution. Radial distribution of micromechanical properties, indentation hardness and elastic modulus were measured for the partial foam and their values were around 0.12 GPa and 2.0 GPa, respectively, for the outer foamed region. Foaming temperature affects the glass transition temperature Tg, the coefficient of thermal expansion and the flame spreading rate. For the range of Tf ≤ 60°C, thermal expansion coefficients for T &gt; Tg are about 0.5 m/m°C (steep expansion group, SEG). When Tf is above 80 °C, they are around 0.02 m/m°C (mild expansion group, MEG). The burning rate of SEG is 2.8 times higher than that of MEG. Materials science ; Composite material ; Thermal expansion ; Polyethylene ; Copolymer ; Polyethylene terephthalate ; Expansion ratio ; Microstructure ; Glass transition ; Elastic modulus ; Polymer ; Modulus ; Atmospheric temperature range ; Polyethylene naphthalate ; Thermodynamics Polymer Foaming and Composites ; Polymer composites and self-healing ; Flame retardant materials and properties
Authors: Mohammad J. Eslamibidgoli | Fabian P. Tipp | Jenia Jistev | Jasna Janković | Michael Eikerling | Kourosh Malek
Source: ChemRxiv (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for High Throughput Screening of Catalyst Layer Inks for Polymer Electrolyte Fuel Cells The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, in particular the catalyst layers. Essential structural building blocks of catalyst layers are formed during processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and employment of data augmentation that works on multiple scales in the training of robust and accurate classification pipelines. Convolutional neural network ; Context (archaeology) ; Throughput ; Computer science ; Layer (electronics) ; Inkwell ; Characterization (materials science) ; Electrolyte ; Materials science ; Fabrication ; Electrode ; Deep learning ; Nanotechnology ; Catalysis ; Polymer ; Artificial intelligence ; Chemistry ; Composite material ; Wireless ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Pushkar Ghanekar | Siddharth Deshpande | Jeffrey Greeley
Source:  (preprint, 2021-08-23)
Field: Materials Science — Materials Chemistry

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions. Catalysis ; Heterogeneous catalysis ; Chemical physics ; Adsorption ; Chemistry ; Substrate (aquarium) ; Materials science ; Nanotechnology ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Xin Li | Bo Li | Zhiwen Chen | Wang Gao | Qing Jiang
Source: arXiv (Cornell University) (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

A Transferable Machine-learning Scheme from Pure Metals to Alloys in Predicting Adsorption Energies Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms' valence, electronegativity and coordination and the adsorbates' valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts. Electronegativity ; Adsorption ; Valence (chemistry) ; Atomic radius ; Materials science ; Alloy ; Intermetallic ; Atom (system on chip) ; Thermodynamics ; Chemical physics ; Physical chemistry ; Computer science ; Chemistry ; Metallurgy ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Nathan C. Frey | Siddharth Samsi | J.C. McDonald | Lin Li | Connor W. Coley | Vijay Gadepally
Source: arXiv (Cornell University) (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

Scalable Geometric Deep Learning on Molecular Graphs Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottlenecks with respect to the amount of training data, the size and complexity of model architectures, and the scale of the compute infrastructure are all key factors limiting the scaling of deep learning for molecules and materials. Here, we present $\textit{LitMatter}$, a lightweight framework for scaling molecular deep learning methods. We train four graph neural network architectures on over 400 GPUs and investigate the scaling behavior of these methods. Depending on the model architecture, training time speedups up to $60\times$ are seen. Empirical neural scaling relations quantify the model-dependent scaling and enable optimal compute resource allocation and the identification of scalable molecular geometric deep learning model implementations. Scalability ; Deep learning ; Scaling ; Computer science ; Artificial intelligence ; Artificial neural network ; Limiting ; Implementation ; Theoretical computer science ; Key (lock) ; Machine learning ; Parallel computing ; Computational science ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Srilok Sriniva | Rohit Batra | Duan Luo | Troy D. Loeffler | Sukriti Manna | Henry Chan | Liuxiang Yang | Wenge Yang | Jianguo Wen | Pierre Darancet | Subramanian K. R. S. Sankaranarayanan
Source: Research Square (preprint, 2021-03-30)
Field: Materials Science — Materials Chemistry

Machine Learning the Metastable Phase Diagram of Materials Phase diagram ; Metastability ; Diagram ; Phase (matter) ; Materials science ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jiahua Rao | Shuangjia Zheng | Yuedong Yang
Source: arXiv (Cornell University) (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction. However, current graph neural networks (GNNs) remain of limited acceptance in drug discovery is limited due to their lack of interpretability. Although this major weakness has been mitigated by the development of explainable artificial intelligence (XAI) techniques, the "ground truth" assignment in most explainable tasks ultimately rests with subjective judgments by humans so that the quality of model interpretation is hard to evaluate in quantity. In this work, we first build three levels of benchmark datasets to quantitatively assess the interpretability of the state-of-the-art GNN models. Then we implemented recent XAI methods in combination with different GNN algorithms to highlight the benefits, limitations, and future opportunities for drug discovery. As a result, GradInput and IG generally provide the best model interpretability for GNNs, especially when combined with GraphNet and CMPNN. The integrated and developed XAI package is fully open-sourced and can be used by practitioners to train new models on other drug discovery tasks. Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Benchmark (surveying) ; Graph ; Artificial neural network ; Property (philosophy) ; Drug discovery ; Theoretical computer science ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Jinzhe Zeng | Timothy J. Giese | Şölen Ekesan | Darrin M. York
Source: ChemRxiv (preprint, 2021-02-26)
Field: Materials Science — Materials Chemistry

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design.&lt;br&gt; Range (aeronautics) ; Replica ; Statistical physics ; Quantum ; Computer science ; Energy (signal processing) ; Artificial neural network ; Cutoff ; Biological system ; Chemistry ; Artificial intelligence ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Hongxing Liang | Min Xu | Edouard Asselin
Source: ChemRxiv (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

A Study of Two-Dimensional Single Atom-Supported MXenes as Hydrogen Evolution Reaction Catalysts Using DFT and Machine Learning &lt;p&gt;&lt;/p&gt;&lt;p&gt;Enclosed you will find the article entitled “A study of two-dimensional single atom-supported MXenes as hydrogen evolution reaction catalysts using DFT and machine learning”.&lt;br&gt;&lt;/p&gt; &lt;p&gt; &lt;/p&gt; &lt;p&gt;Existing studies predominantly focused on the hydrogen evolution reaction (HER) activities and stabilities of oxygen-terminated MXenes with single-atom loading. However, to the best of our knowledge, two-dimensional (2D) MXenes with different terminations (e.g. Br, I, Se, Te, B, Si, P, and NH) have not yet been investigated for the purposes of HER catalysis. Therefore, in this work, we considered the combined effect of the different surface terminations (B, NH, O, F, Si, P, S, Cl, Se, Br, Te, and I) and single atom loading (Ti, V, Fe, Co, Ni, Cu, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Hf, Ta, W, Re, Os, Ir, Pt, and Au) using DFT calculation. Gibbs free energy of hydrogen adsorption (reflecting activity) and the cohesive energy (a proxy for thermal stability) of these structures (264 in total) were calculated. We demonstrate that 21 uninvestigated 2D single-atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum and a thermal stability surpassing those of synthesized borophene sheet and MoS&lt;sub&gt;2&lt;/sub&gt;. Moreover, all catalysts examined in this work were further randomly separated into training and test sets with a ratio of 7:3. The HER electrocatalytic performance and thermal stability of the catalysts in the test set were predicted by machine learning algorithms. Most importantly, we present a way to provide a comparable precision (root mean square error values for the activity and thermal stability predictions are 0.158 eV and 0.02 eV, respectively) to the published machine learning works by avoiding their adoption of complex electronic features and the associated high computational cost, and &lt;i&gt;by only using features that are &lt;/i&gt;&lt;i&gt;easily available in chemical repositories&lt;/i&gt;. The algorithms used in this work are expected to help future researchers quickly screen single atom loaded MXenes HER catalysts at the initial design stage in a cost-effective manner. &lt;/p&gt; &lt;p&gt; &lt;/p&gt; &lt;p&gt;We have no financial interest in the subject or instrumentation used and there is no known conflict of interest. &lt;/p&gt;&lt;br&gt;&lt;p&gt;&lt;/p&gt; MXenes ; Catalysis ; Gibbs free energy ; Hydrogen ; Hydrogen atom ; Chemistry ; Density functional theory ; Atom (system on chip) ; Thermal stability ; Physical chemistry ; Materials science ; Nanotechnology ; Computational chemistry ; Physics ; Thermodynamics ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion