jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daisuke MATSUNAKA
Source: Journal of the Society of Materials Science Japan (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Elucidation of Mechanical Properties of Nano-Scale Interfaces by First-Principles Machine-Learning Calculations Materials science ; Nanoscopic scale ; Scale (ratio) ; Nanotechnology ; Nano- ; Composite material ; Physics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Force Microscopy Techniques and Applications
Authors: Balázs Pál | László Dobos
Source: arXiv (Cornell University) (preprint, 2024-09-18)
Field: Chemistry — Analytical Chemistry

Denoising medium resolution stellar spectra with neural networks We trained denoiser autoencoding neural networks on medium resolution simulated optical spectra of late-type stars to demonstrate that the reconstruction of the original flux is possible at a typical relative error of a fraction of a percent down to a typical signal-to-noise ratio of 10 per pixel. We show that relatively simple networks are capable of learning the characteristics of stellar spectra while still flexible enough to adapt to different values of extinction and fluxing imperfections that modifies the overall shape of the continuum, as well as to different values of Doppler shift. Denoised spectra can be used to find initial values for traditional stellar template fitting algorithms and - since evaluation of pre-trained neural networks is significantly faster than traditional template fitting - denoiser networks can be useful when a fast analysis of the noisy spectrum is necessary, for example during observations, between individual exposures. Resolution (logic) ; Artificial neural network ; Spectral line ; Noise reduction ; Astrophysics ; Physics ; Computer science ; Artificial intelligence ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Wenrui Hao | Chun Liu | Yiwei Wang | Yahong Yang
Source: arXiv (Cornell University) (preprint, 2024-09-07)
Field: Materials Science — Materials Chemistry

On pattern formation in the thermodynamically-consistent variational Gray-Scott model In this paper, we explore pattern formation in a four-species variational Gary-Scott model, which includes all reverse reactions and introduces a virtual species to describe the birth-death process in the classical Gray-Scott model. This modification transforms the classical Gray-Scott model into a thermodynamically consistent closed system. The classical two-species Gray-Scott model can be viewed as a subsystem of the variational model in the limiting case when the small parameter $ε$, related to the reaction rate of the reverse reactions, approaches zero. We numerically explore pattern formation in this physically more complete Gray-Scott model in one spatial dimension, using non-uniform steady states of the classical model as initial conditions. By decreasing $ε$, we observed that the stationary pattern in the classical Gray-Scott model can be stabilized as the transient state in the variational model for a significantly small $ε$. Additionally, the variational model admits oscillating and traveling-wave-like patterns for small $ε$. The persistent time of these patterns is on the order of $O(ε^{-1})$. We also analyze the energy stability of two uniform steady states in the variational Gary-Scott model for fixed $ε$. Although both states are stable in a certain sense, the gradient flow type dynamics of the variational model exhibit a selection effect based on the initial conditions, with pattern formation occurring only if the initial condition does not converge to the boundary steady state, which corresponds to the trivial uniform steady state in the classical Gray-Scott model. Gray (unit) ; Statistical physics ; Mathematical economics ; Mathematics ; Physics ; Medicine Solidification and crystal growth phenomena
Authors: Sandeep Kumar | Rajat Yadav | Rahul kumar - | Anuj Raturi | A.L.N. Rao | Anil Kumar | Nandini Shirish Boob
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The Role of Nanomaterials in Advancing Sustainable Healthcare Practices This exploration investigates the extraordinary capability of nanomaterials in progressing supportable medical care works, zeroing in on the amalgamation and utilization of PEGylated gold nanoparticles (AuNPs) for drug conveyance and quantum dabs for imaging. The AuNPs, portrayed by a round shape (20 ± 5 nm) and effective PEGylation, exhibited a high medication stacking proficiency (75 ± 5%) and supported discharge following the Higuchi model. In vitro examinations on human disease cells uncovered a focus subordinate reduction in cell feasibility, with a noteworthy IC50 esteem fundamentally lower for DOX-stacked AuNPs contrasted with free DOX. Quantum specks displayed superb fluorescence properties, affirming their true capacity for cell imaging. The manageability evaluation featured the eco-accommodating traits of nanocarriers, with an impressive decrease in energy utilization (150 kWh), ozone-depleting substance outflows (50 kgCO 2 e), and squander age (2 kg) contrasted with conventional medication conveyance techniques. Near investigation with related works highlighted the prevalent medication stacking proficiency and supported discharge energy of the created nanocarriers. These discoveries add to the developing account of nanomaterials as central members in feasible and accuracy-driven medical care. The effective reconciliation of nanotechnology in drug conveyance and imaging offers a promising road for ecologically cognizant helpful mediations. Health care ; Nanomaterials ; Business ; Nanotechnology ; Political science ; Materials science Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management ; Chemistry and Chemical Engineering
Authors: Xianzhi Chen | Dong Niu | Hongtao Gao | Mu Du
Source: Frontiers in Heat and Mass Transfer (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Molecular Dynamics Study on Hydrothermal Response of PNIPAM: From Single Chain to Cross-Linked Polymer Thermosensitive hydrogel can integrate vapor molecular capture, in-situ liquefaction, and thermal-induced water release for freshwater capture. This study aimed to examine the dynamic behavior of poly (N-isopropylacrylamide) ... | Find, read and cite all the research you need on Tech Science Press Hydrothermal circulation ; Polymer ; Materials science ; Molecular dynamics ; Thermal fluids ; Chain (unit) ; Dynamics (music) ; Hydrothermal reaction ; Chemical engineering ; Chemical physics ; Nanotechnology ; Chemistry ; Thermodynamics ; Heat transfer ; Computational chemistry ; Composite material ; Physics ; Engineering Conducting polymers and applications ; Hydrogels: synthesis, properties, applications ; Dendrimers and Hyperbranched Polymers
Authors: Yang Long | Haoran Xue | Baile Zhang
Source: arXiv (Cornell University) (preprint, 2024-12-30)
Field: Chemistry — Spectroscopy

Unsupervised Classification of Non-Hermitian Topological Phases under Symmetries The integration of artificial intelligence (AI) into fundamental science has opened new possibilities to address long-standing scientific challenges rooted in mathematical limitations. For example, topological invariants are used to characterize topology, but there is no universally applicable one. This limitation explains why, in the past decades-long classification of topological phases of matter -- mainly focused on Hermitian systems -- many phases initially classified ``trivial" were later identified as topological. Recently, the discovery of non-Hermitian band topology has spurred substantial efforts in non-Hermitian topological classification, including the development of new topological invariants. However, such classifications similarly risk overlooking key topological features. Here, without relying on any topological invariant, we develop an AI-based unsupervised classification of symmetry-protected non-Hermitian topological phases. This algorithm distinguishes topological differences among non-Hermitian Hamiltonians with symmetries, and constructs, in an unsupervised manner, a topological periodic table for non-Hermitian systems. Additionally, it can account for the boundary effects, enabling the exploration of open-boundary effects on the topological phase diagram. These results introduce an unsupervised approach for classifying symmetry-protected non-Hermitian topological phases without omission and provide valuable guidance for the development of theories and experiments. Homogeneous space ; Hermitian matrix ; Mathematics ; Pure mathematics ; Topology (electrical circuits) ; Combinatorics ; Geometry Molecular spectroscopy and chirality ; Quantum Mechanics and Non-Hermitian Physics ; Advanced Mathematical Theories and Applications
Authors: Prakash Sarkar | Prita Pant | Hemant Nanavati
Source: arXiv (Cornell University) (preprint, 2024-05-25)
Field: Materials Science — General Materials Science

Physical phenomena during nanoindentation deformation of amorphous glassy polymers We identify for visco-elasto-plastic (VEP) glassy polymers, physical phenomena during Berkovich nanoindentation, a locally imposed deformation. Live visuals via in situ nanoindentation indicate mainly sink-in during loading, with pile-up after unloading. Scanning Probe Microscopy (SPM) indicates significant volume conserving upflow below the tip, for these high nu, compliant materials, with compliance correlated high geometric fractional contact (including blunt height, h_b), (h_c+h_b)/(h_m+h_b)~0.86-0.95. We adapt the ideal conical indentation framework to VEP Berkovich nanoindentation, to calculate the contact area and visually depict the upflow and the displacement paths, in the material. The combination of SPM and P-h data, indicates a mixed comparison with uniaxial modulus and yield stress, with conventionally defined hardness, H&lt;3*sig_y, and nanoindentation modulus E_N&gt;E. By rationally removing viscoelastic (VE) effects from the loading P-h data, we find instant, zero-time hardness, H_L0&gt;3*sig_y. We apply the power law model to only the recovery onset, to estimate pure elastic recovery. We then deconvolute the VEP nanoindentation into the conventional EP and elastic contributions, isolating the VE component. Constraint-induced sink-in, pile-up and VE recovery of the highly yielded tip-apex region, mirror the converse constrained deformation effects, governing the trends in conventionally defined H_L0 and E_N for glassy polymers. Nanoindentation ; Amorphous solid ; Materials science ; Deformation (meteorology) ; Polymer ; Composite material ; Crystallography ; Chemistry Material Properties and Applications ; Polymer Science and Applications ; Injection Molding Process and Properties
Authors: Lan Shang | Sizheng Zheng | Jin Wang | Jie Wang
Source: arXiv (Cornell University) (preprint, 2024-09-03)
Field: Materials Science — Materials Chemistry

Physics-informed neural networks incorporating energy dissipation for the phase-field model of ferroelectric microstructure evolution Physics-informed neural networks (PINNs) are an emerging technique to solve partial differential equations (PDEs). In this work, we propose a simple but effective PINN approach for the phase-field model of ferroelectric microstructure evolution. This model is a time-dependent, nonlinear, and high-order PDE system of multi-physics, challenging to be solved using a baseline PINN. Considering that the acquisition of steady microstructures is one of the primary focuses in simulations of ferroelectric microstructure evolution, we simplify the time-dependent PDE system to be a static problem. This static problem, however, is ill-posed. To overcome this issue, a term originated from the law of energy dissipation is embedded into the loss function as an extra constraint for the PINN. With this modification, the PINN successfully predicts the steady ferroelectric microstructure without tracking the evolution process. In addition, although the proposed PINN approach cannot tackle the dynamic problem in a straightforward fashion, it is of benefit to the PINN prediction of the evolution process by providing labeled data. These data are crucial because they help the PINN avoid the propagation failure, a common failure mode of PINNs when predicting dynamic behaviors. The above mentioned advantages of the proposed PINN approach are demonstrated through a number of examples. Dissipation ; Ferroelectricity ; Phase (matter) ; Field (mathematics) ; Artificial neural network ; Microstructure ; Energy (signal processing) ; Statistical physics ; Materials science ; Computer science ; Physics ; Artificial intelligence ; Thermodynamics ; Quantum mechanics ; Optoelectronics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Alessio Di Ianni | Christian Ihling | Tomáš Vranka | Václav Matoušek | Andrea Sinz | Claudio Iacobucci
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-29)
Field: Chemistry — Spectroscopy

Twisting Urea- to Imide-Based Mass Spectrometry-Cleavable Cross-Linkers Enables Affinity Tagging ABSTRACT Disuccinimidyl dibutyric urea (DSBU) is a mass spectrometry (MS)-cleavable cross-linker that has multiple applications in structural biology, ranging from isolated protein complexes to comprehensive system-wide interactomics. DSBU facilitates a rapid and reliable identification of cross-links through the dissociation of its urea group in the gas-phase. In this study, we further advance the structural capabilities of DSBU by twisting the urea group into an imide, thus introducing a novel class of cross-linkers. This modification preserves the MS-cleavability of the amide bond, granted by the two acyl groups of the imide function. The central nitrogen atom enables the introduction of affinity purification tags. Here, we introduce disuccinimidyl disuccinic imide (DSSI) as prototype of this class of cross-linkers. It features a phosphonate handle for immobilized metal ion affinity chromatography (IMAC) enrichment. We detail DSSI synthesis and describe its behavior in solution and in the gas-phase while cross-linking isolated proteins and human cell lysates. DSSI and DSBU cross-links are compared at the same enrichment depths to bridge these two cross-linker classes. We validate DSSI cross-links by mapping them in high-resolution structures of large protein assemblies. The cross-links observed yield insights into the morphology of intrinsically disordered proteins (IDPs) and their complexes. The DSSI linker might spearhead a novel class of MS-cleavable and enrichable cross-linkers. Imide ; Mass spectrometry ; Chemistry ; Urea ; Chromatography ; Combinatorial chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Rui Li | Junling Du | Zheng Qin | Shikun Zhang | Chunxiao Du | Yang Zhou | Zhisong Xiao
Source: arXiv (Cornell University) (preprint, 2024-01-07)
Field: Materials Science — Materials Chemistry

Entanglement Structure Detection via Computer Vision Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, there still lacks a universal and effective way to detecting entanglement structures, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid CNN-Transformer model for both the classification of GHZ and W states and the detection of various entanglement structures. By leveraging the feature extraction capabilities of CNNs and the powerful modeling abilities of Transformers, we can not only effectively reduce the time and computational resources required for the training process but also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for independent detection of multipartite entanglement. Quantum entanglement ; Computer science ; Multipartite entanglement ; Multipartite ; Quantum ; Theoretical computer science ; Quantum computer ; Artificial intelligence ; Computer engineering ; Squashed entanglement ; Quantum mechanics ; Physics Machine Learning in Materials Science
Authors: Caio M. Porto | René A. Nome | Nelson H. Morgon
Source: Research Square (preprint, 2024-03-06)
Field: Chemistry — Spectroscopy

The influence of basis sets and ansatze building to quantum computing in chemistry Basis (linear algebra) ; Quantum chemistry ; Computer science ; Chemistry ; Theoretical physics ; Computational chemistry ; Mathematics ; Physics ; Organic chemistry ; Reaction mechanism Molecular spectroscopy and chirality ; Quantum Computing Algorithms and Architecture
Authors: Dilip Chalissery | Thorsten Pretsch
Source: Polymers (article, 2024-05-14)
Field: Materials Science — Polymers and Plastics

4D-Printed Tool for Compressing a Shape Memory Polyurethane Foam during Programming Although several force application concepts are known that can be used to deform shape memory polymers (SMPs) within the scope of programming, controlled deformation is challenging in the case of samples with a cylinder-like shape, which need to be homogeneously compressed starting from the lateral surface. To solve this problem, this contribution follows a material approach that takes advantage of four-dimensional (4D) printing. Fused filament fabrication (FFF) was used as an additive manufacturing (AM) technique to produce a thermoresponsive tool in a cylindrical shape from a polyether urethane (PEU) having a glass transition temperature (<i>T</i><sub>g</sub>) close to 55 °C, as determined by differential scanning calorimetry (DSC). Once it was 4D-printed, a sample of laser cut polyester urethane urea (PEUU) foam with a cylindrical wall was placed inside of it. Subsequent heating to 75 °C and keeping that temperature constant for 15 min resulted in the compression of the foam, because the internal stresses of the PEU were transferred to the PEUU, whose soft segments were completely molten at 65 °C as verified by DSC. Upon cooling to -15 °C and thus below the offset temperature of the soft segment crystallization transition of the PEUU, the foam was fixed in its new shape. After 900 days of storage at temperatures close to 23 °C, the foam recovered its original shape upon reheating to 75 °C. In another experiment, a 4D-printed cylinder was put into hibernation for 900 days before its thermoresponsiveness was investigated. In the future, 4D-printed tools may be produced in many geometries, which fit well to the shapes of the SMPs to be programmed. Beyond programming SMP foams, transferring the forces released by 4D-printed tools to other programmable materials can further expand technical possibilities. Materials science ; Polyurethane ; Composite material ; Differential scanning calorimetry ; Cylinder ; Shape-memory polymer ; Glass transition ; Compression (physics) ; Fused filament fabrication ; Polymer ; Polyester ; Deformation (meteorology) ; Shape-memory alloy ; Fabrication ; Mechanical engineering Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Tommaso Ferracci | Leonie Tabea Goldmann | Anton Hinel | Francesco Sanna Passino
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

Targeted synthetic data generation for tabular data via hardness characterization Data augmentation via synthetic data generation has been shown to be effective in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we introduce a simple augmentation pipeline that generates only high-value training points based on hardness characterization, in a computationally efficient manner. We first empirically demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterization tasks, while offering significant computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on a number of tabular datasets. Our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods. Characterization (materials science) ; Computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Data Quality and Management
Authors: Dóra Vörös | Felix Proché | Leticia González | Sebastian Mai
Source: Communications Physics (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Hydrogen bonding to the electron accepting group controls the absorption spectrum of a push–pull stilbene adsorbed on amorphous silica Abstract The photophysical and photochemical properties of push–pull photoswitches, such as stilbene derivatives, are very sensitive to their surroundings, allowing, e.g., for the control of the spectral properties or alternatively for probing the molecular environment. Here, we investigate how various adsorption motifs of 4-(N, N-Dimethylamino)-4’-nitrostilbene (DANS) on an amorphous glass surface influence its absorption spectra. Particular attention is given to the prominent first bright charge transfer state and the factors governing these modifications. The absorption spectra is simulated using time-dependent density functional theory on a large set of adsorption geometries that exhibit different kinds of molecule–surface interactions. We find that C–H⋯O interactions of the methyl or phenyl moieties with the glass affect the spectrum only marginally, whereas the dispersion interaction of the π system with the surface tends to diminish the intensity of the first absorption band. Moreover, an enhancement of the push–pull properties of DANS and a significant redshift occur whenever O–H⋯O hydrogen bonds with the NO 2 group are present, whereas a hydrogen bond O–H⋯N to the NMe 2 group results in a blueshift. Blueshift ; Amorphous solid ; Hydrogen bond ; Absorption (acoustics) ; Adsorption ; Photochemistry ; Absorption spectroscopy ; Chemistry ; Dispersion (optics) ; Spectral line ; Molecule ; Absorption band ; Density functional theory ; Chemical physics ; Crystallography ; Materials science ; Physical chemistry ; Computational chemistry ; Optics ; Physics ; Organic chemistry ; Photoluminescence Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry