jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Icare Morrot-Woisard | Emile K. Nguyen | N. Vukadinovic | Mauro Boero
Source: Carbon Trends (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Structural, electronic and dielectric properties of carbon nanotubes interacting with Co nanoclusters A new frontier in tuning the electromagnetic response of carbon-based materials, particularly carbon nanotubes, consists in adding metallic clusters or nanoparticles at their external surface. This allows to change optical, dielectric and magnetic properties with potential applications in electronics and telecommunications for aeronautics. By resorting to first principles dynamical simulations, we provide a microscopic picture of the interaction at finite temperature between a carbon nanotube and Co aggregates mimicking the experimental coverage. The electronic structure evolution provides the absorption spectrum and the dielectric function for comparison with experiments and guidelines for tuning these composite systems. • Advanced computational methods unveil the dielectric response of carbon nanotubes systems in the THz and IR domain. • The mechanism of the interaction between Co nanocluster and carbon nanotubes has been unraveled. • Dynamical behavior of Co nanoclusters on carbon nanotubes at finite temperature leads to aggregation. • Adsorption spectra and dielectric response are enhanced by Co nanoclusters decorating the nanotube. • A clear link exists between atomic-level features and macroscopic response of the composite system. Nanoclusters ; Carbon nanotube ; Materials science ; Dielectric ; Carbon fibers ; Nanotechnology ; Electronic structure ; Chemistry ; Computational chemistry ; Optoelectronics ; Composite material ; Composite number Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Rajab Elkilani | Harun Çuğ | Mehmet Akif Erden | Hamza Mohamed S ABUSHRENTA | Osama Albahl Alshtewe Albahlol | Buğra Çiçek
Source: Journal of Mining and Metallurgy Section B Metallurgy (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Effects of hot rolling on microstructures, wear and corrosion resistance of Mo-Ni-W p/m alloyed steels This study analyzes how hot rolling changes microstructure, tensile strength, wear, and corrosion behavior of Fe-0.55C- 3Mo-10Ni-0.5W alloy steel. The metal powders were pressed at 750 MPa pressure, and the cold-pressed samples were sintered for two hours at 5?C/min up to 1400?C in a mixed-gas environment of 90% nitrogen and 10% hydrogen. The steels produced were then hot rolled at 40% and 80% deformation rate. The microstructures demonstrate that MoC(N), WC(N), and MoWC(N) were produced and that the steels exhibited finer microstructures and better mechanical properties as the deformation rate increased. Wear decreased as the deformation rate increased. Furthermore, the the hot rolling method improved the corrosion resistance, as shown by the Tafel curve analysis. The greatest factor supporting the corrosion resistance was the increase in density value throughout the rolling process. Metallurgy ; Microstructure ; Materials science ; Corrosion ; Wear resistance Metal Alloys Wear and Properties ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Mubarak Almehairbi | V. Joshi | Ahamad Irfan | Zeinab M. Saeed | Tamador Alkhidir | Aya M. Abdelhaq | Praveen B. Managutti | Bhausaheb Dhokale | Thaksen Jadhav | Changquan Calvin Sun | Sharmarke Mohamed
Source: Chemistry - A European Journal (article, 2024-04-13)
Field: Chemistry — Physical and Theoretical Chemistry

Surface Engineering of the Mechanical Properties of Molecular Crystals via an Atomistic Model for Computing the Facet Stress Response of Solids Dynamic molecular crystals are an emerging class of crystalline materials that can respond to mechanical stress by dissipating internal strain in a number of ways. Given the serendipitous nature of the discovery of such crystals, progress in the field requires advances in computational methods for the accurate and high-throughput computation of the nanomechanical properties of crystals on specific facets which are exposed to mechanical stress. Here, we develop and apply a new atomistic model for computing the surface elastic moduli of crystals on any set of facets of interest using dispersion-corrected density functional theory (DFT-D) methods. The model was benchmarked against a total of 24 reported nanoindentation measurements from a diverse set of molecular crystals and was found to be generally reliable. Using only the experimental crystal structure of the dietary supplement, L-aspartic acid, the model was subsequently applied under blind test conditions, to correctly predict the growth morphology, facet and nanomechanical properties of L-aspartic acid to within the accuracy of the measured elastic stiffness of the crystal, 24.53±0.56 GPa. This work paves the way for the computational design and experimental realization of other functional molecular crystals with tailor-made mechanical properties. Materials science ; Nanoindentation ; Facet (psychology) ; Molecular dynamics ; Crystal (programming language) ; Stress (linguistics) ; Stiffness ; Surface stress ; Elastic modulus ; Density functional theory ; Work (physics) ; Nanotechnology ; Composite material ; Computational chemistry ; Chemistry ; Computer science ; Thermodynamics ; Physics Crystallography and molecular interactions ; Corrosion Behavior and Inhibition ; Calcium Carbonate Crystallization and Inhibition
Authors: Hana Stipanović | Patrick Arth | Gerald Koinig | Nikolai Kuhn | Jakob Lederer | Dominik Blasenbauer | Anna-Maria Lipp | Alexia Tischberger-Aldrian
Source: Applied Spectroscopy (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

Influence of Different Measuring Backgrounds on the Classification of Multilayer Polyolefin Films Using a Near-Infrared Handheld Spectrometer The low thickness of plastic films poses a challenge when using near-infrared (NIR) spectroscopy as it affects the spectral quality and classification. This research focuses on offering a solution to the challenge of classifying multilayer plastic film materials with a focus on polyolefin multilayer plastics. It presents the importance of spectral quality on accurate classification. The aim is to demonstrate the suitability of the handheld NIR spectrometer in classifying multilayer polyolefin films and assess the impact of various measuring backgrounds (white tile, Teflon, aluminum, copper, silver, and gold) on classification accuracy in the wavelength range of 1596-2396 nm. Metallic backgrounds have been found to enhance spectral quality and classification accuracy. The classification accuracy was consistently high, ranging from 96.55% to 100%, with aluminum and gold backgrounds yielding the best results in theoretical accuracy. In experimental classification, the accuracy reached 100% when any metallic backgrounds were used. Conversely, Teflon showed a theoretically high accuracy of 96.21% but only achieved an experimental accuracy of 72.2%. These findings suggest that using metallic backgrounds can improve the spectral quality and classification of plastics with low thickness (films) and complex material composition (multilayers). Polyolefin ; Near-infrared spectroscopy ; Materials science ; Spectrometer ; Infrared ; Germanium ; Optics ; Optoelectronics ; Analytical Chemistry (journal) ; Composite material ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Thompson Odion Igunma | Adeoye Taofik Aderamo | Olisakwe Henry Chukwuemeka
Source: Open Access Research Journal of Engineering and Technology (review, 2024-10-13)
Field: Materials Science — Materials Chemistry

Advanced corrosion-resistant materials for enhanced nuclear fuel performance: A conceptual review of innovations in fuel cladding against molten salt degradation The increasing demand for efficient and sustainable nuclear power has led to the development of advanced corrosion-resistant materials that can significantly enhance nuclear fuel performance. This conceptual review focuses on the innovations in fuel cladding materials designed to resist degradation in molten salt environments, which are crucial for advanced nuclear reactors like molten salt reactors (MSRs). Fuel cladding serves as a critical barrier between the nuclear fuel and the reactor environment, and its ability to withstand extreme temperatures and corrosive conditions is essential for safe and efficient reactor operation. Recent advancements in materials science have introduced novel alloys and coatings that demonstrate superior corrosion resistance in molten salts, which are used as both coolants and fuel solvents in MSRs. These innovations include high-temperature nickel-based alloys, refractory metals, and ceramic coatings, which are engineered to reduce the effects of molten salt corrosion, such as oxidation, pitting, and embrittlement. By enhancing the durability of fuel cladding, these materials contribute to improved fuel performance, longer reactor lifespans, and increased safety. This review explores key developments in corrosion-resistant materials, emphasizing the mechanisms by which these materials mitigate molten salt degradation. Additionally, it highlights the challenges associated with material selection, fabrication, and long-term performance in the highly corrosive environments of advanced nuclear reactors. Ongoing research into these materials offers promising avenues for the future of nuclear energy, particularly in addressing the limitations of current fuel cladding technologies. In conclusion, the use of advanced corrosion-resistant materials represents a significant step toward enhancing the performance and safety of nuclear fuel, ensuring the viability of MSRs and other advanced reactor designs. Continued innovation in this field is essential for the future of nuclear power. Cladding (metalworking) ; Corrosion ; Molten salt ; Degradation (telecommunications) ; Nuclear fuel ; Materials science ; Nuclear engineering ; Metallurgy ; Engineering ; Electrical engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Molten salt chemistry and electrochemical processes
Authors: Siyi Gu | Minkai Xu | Alexander K. Powers | Weili Nie | Tomas Geffner | Karsten Kreis | Jure Leskovec | Arash Vahdat | Stefano Ermon
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recently, target-aware generative models, especially diffusion models, have shown great promise in modeling protein-ligand interactions and generating candidate drugs. However, existing models primarily focus on learning the chemical distribution of all drug candidates, which lacks effective steerability on the chemical quality of model generations. In this paper, we propose a novel and general alignment framework to align pretrained target diffusion models with preferred functional properties, named AliDiff. AliDiff shifts the target-conditioned chemical distribution towards regions with higher binding affinity and structural rationality, specified by user-defined reward functions, via the preference optimization approach. To avoid the overfitting problem in common preference optimization objectives, we further develop an improved Exact Energy Preference Optimization method to yield an exact and efficient alignment of the diffusion models, and provide the closed-form expression for the converged distribution. Empirical studies on the CrossDocked2020 benchmark show that AliDiff can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score, while maintaining strong molecular properties. Code is available at https://github.com/MinkaiXu/AliDiff. Diffusion ; Energy (signal processing) ; Molecule ; Computer science ; Mathematical optimization ; Algorithm ; Physics ; Statistical physics ; Mathematics ; Statistics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Radiopharmaceutical Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Mirko Förster | Nnanna Ukoji | Christoph J. Sahle | Johannes Niskanen | Robin Sakrowski | Göran Surmeier | Christopher Weis | Tetsuo Irifune | Sho Imoto | Hasan Yavaş | Simo Huotari | Dominik Marx | Christian Sternemann | John S. Tse
Source: Proceedings of the National Academy of Sciences (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Generating interstitial water within the persisting tetrahedral H-bond network explains density increase upon compressing liquid water Despite its ubiquitous nature, the atomic structure of water in its liquid state is still controversially debated. We use a combination of X-ray Raman scattering spectroscopy in conjunction with ab initio and path integral molecular dynamics simulations to study the local atomic and electronic structure of water under high pressure conditions. Systematically increasing fingerprints of non-hydrogen-bonded H[Formula: see text]O molecules in the first hydration shell are identified in the experimental and computational oxygen K-edge excitation spectra. This provides evidence for a compaction mechanism in terms of a continuous collapse of the second hydration shell with increasing pressure via generation of interstitial water within locally tetrahedral hydrogen-bonding environments. Tetrahedron ; Liquid water ; Bond ; Crystallography ; Materials science ; Chemistry ; Chemical physics ; Physics ; Thermodynamics ; Business Material Dynamics and Properties ; Quantum, superfluid, helium dynamics ; Phase Equilibria and Thermodynamics
Authors: Ivan Grega | Ilyes Batatia | Padmeya P. Indurkar | Gábor Cśanyi | Sri Karlapati | V.S. Deshpande
Source: Journal of the Mechanics and Physics of Solids (article, 2024-11-16)
Field: Materials Science — Polymers and Plastics

Graph neural networks for strut-based architected solids Machine learning methods for strut-based architected solids are attractive for reducing computational costs in optimisation calculations. However, the space of all realizable strut-based periodic architected solids is vast: not only can the number of nodes, their positions and the radii of the struts be changed but the topological variables such as the connectivity of the nodes brings significant complexity. In this work, we first examine the structure-property relationships of a large dataset of strut-based architected solids (lattices). We enrich the dataset by perturbing nodal positions and observe four classes of mechanical behaviour. A graph neural network (GNN) method is then proposed that directly describes the topology of the strut-based architected solid as a graph. The differentiating feature of our work is that key physical principles are embedded into the GNN architecture. In particular, the GNN model predicts fourth-order tensor with the required major and minor symmetries. The predictions are equivariant to rigid body and self-similar transformations, invariant to the choice of unit cell and constrained to provide a positive semi-definite stiffness tensor. We further demonstrate that augmenting the training dataset with nodal perturbations enables the model to better generalize to unseen lattice topologies. Artificial neural network ; Materials science ; Graph ; Computer science ; Composite material ; Theoretical computer science ; Artificial intelligence Polymer composites and self-healing ; Cellular and Composite Structures ; Material Properties and Processing
Authors: Xiaodong Huang | Xiaojian Hao | Baowu Pan | Shaogang Chen | Shenxiang Feng | Pan Pei
Source: IEEE photonics journal (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Combustion Field Prediction and Diagnosis via Spatiotemporal Discrete U-ConvLSTM Model Considering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM model was proposed to combine with the TDLAS method to resolve the real-time reconstruction and short prediction. By dividing the combustion field into space and time slices, we used discretized spatiotemporal slices to complete the 2-D distribution reconstruction and then expanded them into higher dimensions. The simulation results demonstrate that our design can effectively reconstruct different 2-D distributions, achieving a reconstruction error of less than 5%. Three-step predictions also performed well, a PSNR no less than 30 dB, and an SSIM no less than 0.75. In general, our multidimensional combustion field reconstruction method, based on the spatiotemporal discretization U-ConvLSTM model, can enhance the accuracy of combustion field reconstruction and provide short-term predictions. This work will contribute to closed-loop control in industrial fields. Discretization ; Combustion ; Field (mathematics) ; Computer science ; Iterative reconstruction ; Artificial intelligence ; Algorithm ; Biological system ; Mathematics ; Chemistry Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques
Authors: Ceder Dens | Darien Yeung | Oleg V. Krokhin | Kris Laukens | Wout Bittremieux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-20)
Field: Chemistry — Spectroscopy

Zero-shot retention time prediction for unseen post-translational modifications with molecular structure encodings Abstract Mass spectrometry-based proteomics relies on accurate peptide property prediction models to enhance peptide identification and characterization, especially when dealing with peptidoforms. However, current approaches are limited in their ability to generalize to peptides with novel post-translational modifications (PTMs) due to insufficient training data. To address this challenge, we introduce MoSTERT (Molecular Structure Transformer Encoder for Retention Time prediction) and its enhanced variant, MoSTERT-2S, two transformer-based models designed for zero-shot prediction of retention times of peptides with unseen PTMs. Unlike conventional models, MoSTERT encodes peptide residues at the molecular structure level, allowing it to handle diverse PTMs. MoSTERT-2S further improves accuracy by employing a two-step strategy: first predicting the retention time of the unmodified peptide, then estimating the retention time shift induced by the PTMs. Evaluation on an external dataset demonstrates that MoSTERT-2S achieves state-of-the-art performance, reducing prediction errors compared to existing methods. Its ability to accurately predict retention times for peptides with a wide variety of PTMs not seen during training highlights its potential for advancing proteomic workflows analyzing proteoforms and protein modifications. Encoder ; Computer science ; Retention time ; Transformer ; Peptide ; Posttranslational modification ; Workflow ; Artificial intelligence ; Computational biology ; Machine learning ; Chemistry ; Chromatography ; Biology ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Alois Christian Ott | Irmgard Weißensteiner | Aurel Arnoldt | Johannes A. Österreicher | Nikolaus Papenberg
Source: Microscopy and Microanalysis (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Automatic Texture Alignment by Optimization Method Microstructure analysis via electron backscatter diffraction has become an indispensable tool in materials science and engineering. In order to interpret or predict the anisotropy in crystalline materials, the texture is assessed, e.g. via pole figure diagrams. To ensure a correct characterization, it is crucial to align the measured sample axes as closely as possible with the manufacturing process directions. However, deviations are inevitable due to sample preparation and manual measurement setup. Postprocessing is mostly done manually, which is tedious and operator-dependent. In this work, it is shown that the deviation can be calculated using the contour of the crystal orientations. This can also be utilized to define the axis symmetry of pole figure diagrams through an objective function, allowing for symmetric alignment by minimization. Experimental textures of extruded profiles and synthetically generated textures were used to demonstrate the general applicability of the method. It has proven to work excellently for deviations of up to 5∘, which are typical for careful manual sample preparation and mounting. While the performance of the algorithm is reduced with increasing misalignment, good results have also been obtained for deviations up to 15∘. Sample (material) ; Anisotropy ; Texture (cosmology) ; Computer science ; Electron backscatter diffraction ; Minification ; Symmetry (geometry) ; Algorithm ; Orientation (vector space) ; Operator (biology) ; Work (physics) ; Process (computing) ; Diffraction ; Materials science ; Artificial intelligence ; Image (mathematics) ; Optics ; Mechanical engineering ; Geometry ; Mathematics ; Physics ; Engineering X-ray Diffraction in Crystallography ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: ZHANG Yue | LU Yan | PENG Ruitao | ZHU Linwei | LEI Bei | JIANG Jiachuan
Source: Journal of Mechanical Engineering (article, 2024-01-01)
Field: Chemistry — Spectroscopy

New Connection Technology and Application Status of Lightweight Materials 摘要： 随着全球汽车产业低碳化转型,与车身轻量化相关的连接技术挑战不断增加,综合考虑轻量化的成本与效果,合理选用车身材料,搭配不同连接工艺,在满足行业质量标准降低产品重量的前提下为产品设计提供更多思路,已经成为轻量化技术发展的必然趋势。合理运用各类轻量化材料,可以有效减轻车身重量、提高车身性能,对不同的车身轻量化材料的若干新型连接工艺的研究进展进行系统归纳,从冶金连接技术、机械连接技术、粘接技术及复合连接技术四个方面进行综述分析,阐述近年来各种新型工艺及其延伸工艺的研究进展,重点分析各类工艺的技术原理、连接强度准则、工艺优势、局限性及应用现状,以期为汽车轻量化材料新型连接工艺的设计与制造提供有益借鉴,为车身轻量化连接工艺保驾护航。 Connection (principal bundle) ; Key (lock) Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Material Selection and Properties