jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Li Lu | Sander Reniers | Yunzheng Wang | Yuqing Jiao | Robert E. Simpson
Source: Journal of Optics (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

Reconfigurable InP waveguide components using the Sb <sub>2</sub> S <sub>3</sub> phase change material Abstract Reconfigurable waveguide components are promising building blocks for photonic neural networks and as an optical analogue to field-programmable gate arrays. By changing the effective index of the waveguide, reconfigurable waveguide components can achieve on-chip light routing and modulation. In this paper, we design and demonstrate an Sb 2 S 3 -reconfigurable InP membrane Mach–Zehnder interferometer (MZI) on a silicon substrate. Sb 2 S 3 , which has tunable refractive index and low absorption in the near-infrared spectrum, was patterned on the InP waveguide MZIs to make an optical switch in the telecoms conventional-band. By laser induced crystallisation of the Sb 2 S 3 , it was possible to control interference in the MZI and achieve 18 dB on/off switching at 1540 nm. Laser reamorphisation and reversible switching of the Sb 2 S 3 layer resulted in damage to the waveguide structure. However, simulations show that transition metal di-chalcogenide two-dimensional crystal layers can act as efficient thermal barriers that prevent thermal damage to the waveguide during laser amorphisation. Therefore, combining Sb 2 S 3 with InP waveguides seems to be a feasible approach to achieve low-loss reprogrammable waveguide components for on-chip photonics routing and neural networks. Waveguide ; Materials science ; Optoelectronics ; Optics ; Laser ; Photonics ; Interferometry ; Refractive index ; Chalcogenide Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Quoc-Hoa Pham | Trung Thanh Tran | Phu‐Cuong Nguyen
Source: Alexandria Engineering Journal (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

Nonlocal free vibration of functionally graded porous nanoplates using higher-order isogeometric analysis and ANN prediction The main objective of this article is to propose a new shear continuous function used for the free vibration analysis of functionally graded (FG) nanoplates with the presence of internal porosity (so-called FGP nanoplates) by employing isogeometric analysis (IGA). The small-scale effect that appears in nanostructures is included by using the nonlocal elasticity theory (nonlocal theory). Functionally graded materials consist of mechanical properties varies along z-directions. The porosity occurring in the material matrix is described by the trigonometric function. Furthermore, an artificial neural network (ANN) model is created by Matlab software to predict the natural frequencies of FGP nanoplates. The performance of the proposed formula is confirmed by comparing the obtained results with those of previous publications. Finally, some examples are conducted to provide novel results in the free vibration of FGP nanoplates. Isogeometric analysis ; Trigonometric functions ; Vibration ; Porosity ; Materials science ; MATLAB ; Artificial neural network ; Elasticity (physics) ; Composite material ; Structural engineering ; Mathematics ; Computer science ; Acoustics ; Physics ; Artificial intelligence ; Finite element method ; Engineering ; Geometry Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Marco Fronzi | Roger D. Amos | Rika Kobayashi | Naoki Matsumura | K. Watanabe | Rafael K. Morizawa
Source: Nanomaterials (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the <i>ab-initio</i> VASP program. Benchmarking was carried out on Au20 nanoclusters against <i>ab-initio</i> molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement. Nanoclusters ; Benchmarking ; Interatomic potential ; Ab initio ; Nanoparticle ; Workflow ; Materials science ; Molecular dynamics ; Ab initio quantum chemistry methods ; Computer science ; Nanotechnology ; Chemical physics ; Computational chemistry ; Chemistry ; Molecule ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Pan Zhang | Zhenhua Zhang | Yong Liu | Ziyu Wang | Zhihong Lu | Rui Xiong
Source: Physical Review Applied (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Phonon Thermal Transport in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:msub><mml:mi>Bi</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>Te</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> from a Deep-Neural-Network Interatomic Potential ${\mathrm{Bi}}_{2}{\mathrm{Te}}_{3}$ is a widely used thermoelectric material with strong anharmonicity. Determination of its thermal conductivity requires consideration of the high-order phonon scattering process, which makes it extremely time consuming and challenging to accurately calculate its thermal conductivity by obtaining high-order force constants based on density-functional theory. In this work, a deep-neural-network potential is developed to reproduce phonon dispersion and predict the lattice thermal conductivity of ${\mathrm{Bi}}_{2}{\mathrm{Te}}_{3}$. The equilibrium molecular dynamics simulations combined with this potential are performed to calculate the lattice thermal conductivity and the results nicely match the experimental values. Meanwhile, we find the generalized gradient approximation with the DFT-D3 functional can accurately reproduce the experimental lattice constants of ${\mathrm{Bi}}_{2}{\mathrm{Te}}_{3}$ and provide a description of the phonon dispersion in ${\mathrm{Bi}}_{2}{\mathrm{Te}}_{3}$ as well as the local density approximation. Furthermore, we explore the influence of the native point defects on thermal conductivity, and find that $\mathrm{Te}$ vacancies have the most significant effect on the reduction of thermal conductivity, owing to the appreciable inhibition of phonon propagation speed by $\mathrm{Te}$ vacancies, and the additional scattering among original low-frequency optical phonons and the fresh low-frequency optical phonons moving downward from high frequency region, which provides some theoretical guidance for reducing thermal conductivity in experimental research. Thermal conductivity ; Phonon ; Anharmonicity ; Phonon scattering ; Condensed matter physics ; Materials science ; Lattice (music) ; Physics ; Scattering ; Thermodynamics ; Optics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Kyowon Jeong | Maša Babović | Vladimir Gorshkov | Jihyung Kim | Ole N. Jensen | Oliver Kohlbacher
Source: Nature Communications (article, 2022-07-29)
Field: Chemistry — Spectroscopy

FLASHIda enables intelligent data acquisition for top–down proteomics to boost proteoform identification counts The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates. Computer science ; Identification (biology) ; Data acquisition ; Proteomics ; Selection (genetic algorithm) ; Data mining ; Machine learning ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Joe D. Morrow | Volker L. Deringer
Source: The Journal of Chemical Physics (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Indirect learning and physically guided validation of interatomic potential models Machine learning (ML) based interatomic potentials are emerging tools for material simulations, but require a trade-off between accuracy and speed. Here, we show how one can use one ML potential model to train another: we use an accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster potentials. Without the need for quantum-mechanical reference computations at the secondary stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the ML of interatomic potential models and suggests a route toward accelerated simulations of condensed-phase systems. Interatomic potential ; Computer science ; Computation ; Work (physics) ; Statistical physics ; Molecular dynamics ; Computational science ; Algorithm ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Massimiliano Lupo Pasini | Pei Zhang | Samuel Temple Reeve | Jong Youl Choi
Source: Machine Learning Science and Technology (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems <sup>*</sup> Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property. Convolutional neural network ; Computer science ; Tetragonal crystal system ; Ferromagnetism ; Artificial neural network ; Artificial intelligence ; Machine learning ; Materials science ; Algorithm ; Physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electrocatalysts for Energy Conversion
Authors: Guiqing Xu | Xiaoqing Gong | Yingying Zhu | Xiaojun Yao | Lizeng Peng | Ge Sun | Jianxue Yang | Longfei Mao
Source: Frontiers in Pharmacology (article, 2022-05-23)
Field: Chemistry — Organic Chemistry

Novel 1,2,3-Triazole Erlotinib Derivatives as Potent IDO1 Inhibitors: Design, Drug-Target Interactions Prediction, Synthesis, Biological Evaluation, Molecular Docking and ADME Properties Studies Indoleamine 2,3-dioxygenase 1 (IDO1) plays a predominant role in cancer immunotherapy which catalyzes the initial and rate limiting steps of the kynurenine pathway as a key enzyme. To explore novel IDO1 inhibitors, five derivatives of erlotinib-linked 1,2,3-triazole compounds were designed by using a structure-based drug design strategy. Drug-target interactions (DTI) were predicted by DeePurpose, an easy-to-use deep learning library that contains more than 50 algorithms. The DTI prediction results suggested that the designed molecules have potential inhibitory activities for IDO1. Chemical syntheses and bioassays showed that the compounds exhibited remarkable inhibitory activities against IDO1, among them, compound <b>e</b> was the most potent with an IC<sub>50</sub> value of 0.32 ± 0.07 μM in the Hela cell assay. The docking model and ADME analysis exhibited that the effective interactions of these compounds with heme iron and better drug-likeness ensured the IDO1 inhibitory activities. The studies suggested that compound <b>e</b> was a novel and interesting IDO1 inhibitor for further development. ADME ; Erlotinib ; Docking (animal) ; Triazole ; Chemistry ; Pharmacology ; Computational biology ; EGFR inhibitors ; Drug ; Combinatorial chemistry ; Stereochemistry ; Biochemistry ; Medicine ; Biology ; Epidermal growth factor receptor ; Organic chemistry ; Receptor Click Chemistry and Applications ; Adenosine and Purinergic Signaling ; Tryptophan and brain disorders
Authors: Kemal Hacıefendioğlu | Selen Ayas | Hasan Basri Başağa | Vedat Toğan | Fatemeh Mostofi | Ahmet Can
Source: European Journal of Wood and Wood Products (article, 2022-04-15)
Field: Chemistry — Organic Chemistry

Wood construction damage detection and localization using deep convolutional neural network with transfer learning Convolutional neural network ; Artificial intelligence ; Categorization ; Computer science ; Transfer of learning ; Pattern recognition (psychology) ; Deep learning ; Durability ; Cluster analysis ; Visualization ; Database Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Cultural Heritage Materials Analysis
Authors: Aibobek Seitak | Aya Shanti | Kenana Al Adem | Nouran Farid | Shaohong Luo | Javad Iskandarov | Georgios N. Karanikolos | Kin Liao | Vincent Chan | Sungmun Lee
Source: Journal of Biomedical Materials Research Part A (article, 2022-11-13)
Field: Materials Science — Materials Chemistry

<scp>2D MXenes</scp> for controlled releases of therapeutic proteins MXenes belong to a new class of two dimensional (2D) functional nanomaterials, mainly encompassing transition-metal carbides, nitrides and carbonitrides, with unique physical, chemical, electronic and mechanical properties for various emerging applications across different fields. To date, the potentials of MXenes for biomedical application such as drug delivery have not been thoroughly explored due to the lack of information on their biocompatibility, cytotoxicity and biomolecule-surface interaction. In this study, we developed novel drug delivery system from MXene for the controlled release of a model therapeutic protein. First, the structural, chemical and morphological properties of as synthesized MXenes were probed with electron microscopy and X-ray diffraction. Second, the potential cytotoxicity of MXene toward the proliferation and cell morphology of murine macrophages (RAW 264.7) were evaluated with MTT assays and electron microscopy, respectively. Moreover, the drug loading capacities and sustained release capabilities of MXene were assessed in conjunction with machine learning approaches. Our results demonstrated that MXene did not significantly induce cellular toxicity at any concentration below 1 mg/ml which is within the range for effective dose of drug delivery vehicle. Most importantly, MXene was efficiently loaded with FITC-catalase for subsequently achieving controlled release under different pHs. The release profiles of catalase from MXene showed higher initial rate under basic buffer (pH 9) compared to that in physiological (pH 7.4) and acidic buffers (pH 2). Taken together, the results of this study lead to a fundamental advancement toward the use of MXene as a nanocarrier for therapeutic proteins in drug delivery applications. MXenes ; Biocompatibility ; Materials science ; Drug delivery ; Nanocarriers ; Nanomaterials ; Nanotechnology ; Cytotoxicity ; Biophysics ; Controlled release ; Biomolecule ; Chemical engineering ; Chemistry ; Biochemistry ; Biology ; In vitro MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Gabriel Tan Hong Tzuan | Fazida Hanim Hashim | Thinal Raj | Aqilah Baseri Huddin | Mohd Shaiful Sajab
Source: Plants (article, 2022-07-26)
Field: Chemistry — Analytical Chemistry

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm−1 and 1360 cm−1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed. Ripeness ; Raman spectroscopy ; Chemistry ; Chlorophyll ; Cytosine ; Guanine ; Food science ; Horticulture ; Analytical Chemistry (journal) ; Botany ; Chromatography ; Biochemistry ; Biology ; Optics ; Physics ; Organic chemistry ; Ripening ; DNA Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Coconut Research and Applications
Authors: Tomasz Strachowski | M. Baran | Marcin Małek | Robert Kosturek | Ewa Grzanka | Jan Mizeracki | Agata Romanowska | Stefan Marynowicz
Source: Materials (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Hydrothermal Synthesis of Zinc Oxide Nanoparticles Using Different Chemical Reaction Stimulation Methods and Their Influence on Process Kinetics The aim of this work was to study the effect of the applied chemical reaction stimulation method on the morphology and structural properties of zinc oxide nanoparticles (ZnONPs). Various methods of chemical reaction induction were applied, including microwave, high potential, conventional resistance heater and autoclave-based methods. A novel, high potential-based ZnONPs synthesis method is herein proposed. Structural properties-phase purity, grain size-were examined with XRD methods, the specific surface area was determined using BET techniques and the morphology was examined using SEM. Based on the results, the microwave and autoclave syntheses allowed us to obtain the desired phase within a short period of time. The impulse-induced method is a promising alternative since it offers a non-equilibrium course of the synthesis process in an highly energy-efficient manner. Kinetics ; Zinc ; Hydrothermal circulation ; Nanoparticle ; Chemical kinetics ; Process (computing) ; Chemical engineering ; Materials science ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Computer science ; Engineering ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Michaël Teixeira | Renata A. Maia | Sangaraju Shanmugam | Benoît Louis | Stéphane A. Baudron
Source: Microporous and Mesoporous Materials (article, 2022-08-05)
Field: Chemistry — Inorganic Chemistry

Impact of urea-based deep eutectic solvents on Mg-MOF-74 morphology and sorption properties Choline chloride ; Urea ; Deep eutectic solvent ; Chemistry ; Sorption ; Crystallinity ; Morphology (biology) ; Solvent ; Eutectic system ; Chloride ; Inorganic chemistry ; Decomposition ; Ion exchange ; Chemical engineering ; Polymer chemistry ; Crystallography ; Organic chemistry ; Ion ; Adsorption ; Alloy Metal-Organic Frameworks: Synthesis and Applications ; Ionic liquids properties and applications ; Carbon dioxide utilization in catalysis
Authors: Raphaël Loll‐Krippleber | Victoria A. Sajtovich | Michael Ferguson | Brandon Ho | Andrew R. Burns | Brandon J. Payliss | Joseph Bellissimo | Sydney Peters | Peter J. Roy | Haley D.M. Wyatt | Grant W. Brown
Source: Microbial Cell Factories (article, 2022-12-31)
Field: Materials Science — Biomaterials

Development of a yeast whole-cell biocatalyst for MHET conversion into terephthalic acid and ethylene glycol We demonstrate the feasibility of using S. cerevisiae as a platform for the expression and surface display of PET degrading enzymes and predict that the whole-cell catalyst will be a viable alternative to protein purification-based approaches for plastic degradation. Terephthalic acid ; Polyethylene terephthalate ; Yeast ; Ethylene glycol ; Fermentation ; Saccharomyces cerevisiae ; Polylactic acid ; Ethylene ; Enzyme ; Polyester ; Chemistry ; Biochemistry ; Catalysis ; Materials science ; Organic chemistry ; Polymer ; Composite material biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Effects and risks of endocrine disrupting chemicals