jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giovanni De Matteis | Francesco Giglio | Antonio Moro
Source: arXiv (Cornell University) (preprint, 2023-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complete integrability and equilibrium thermodynamics of biaxial nematic systems with discrete orientational degrees of freedom We study a discrete version of a biaxial nematic liquid crystal model with external fields via an approach based on the solution of differential identities for the partition function. In the thermodynamic limit, we derive the free energy of the model and the associated closed set of equations of state involving four order parameters, proving the integrability and exact solvability of the model. The equations of state are specified via a suitable representation of the orientational order parameters, which imply two-order parameter reductions in the absence of external fields. A detailed exact analysis of the equations of state reveal a rich phase diagram where isotropic versus uniaxial versus biaxial phase transitions are explicitly described, including the existence of triple and tricritical points. Results on the discrete models are qualitatively consistent with their continuum analog. This observation suggests that, in more general settings, discrete models may be used to capture and describe phenomena that also occur in the continuum for which exact equations of state in closed form are not available. Liquid crystal ; Isotropy ; Degrees of freedom (physics and chemistry) ; Phase diagram ; Partition function (quantum field theory) ; Statistical physics ; Limit (mathematics) ; Physics ; Mathematics ; Classical mechanics ; Mathematical analysis ; Thermodynamics ; Phase (matter) ; Quantum mechanics ; Condensed matter physics Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Theoretical and Computational Physics
Authors: Hiroyuki Kumazoe | Kazunori Iwamitsu | Masaki Imamura | Kazutoshi Takahashi | Yoh-ichi Mototake | Masato Okada | Ichiro Akai
Source: Scientific Reports (article, 2023-08-14)
Field: Materials Science — Surfaces, Coatings and Films

Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra We analyzed the X-ray photoelectron spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly accurate spectral decomposition of the XPS spectra, we proposed a framework for discovering physical constraints from the absence of prior quantified physical knowledge, in which we designed the prior probabilities based on the found constraints and the physically required conditions. This suppresses the exchange of peak components during replica exchange Monte Carlo iterations and makes possible to decompose XPS in the case where a reliable structure model or a presumable number of components is not known. As a result, we have successfully decomposed XPS of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samples deposited on SiC substrates with the meV order precision of the binding energy, in which the posterior probability distributions of the binding energies were obtained distinguishably between the different components of buffer layer even though they are observed as hump and shoulder structures because of their overlapping. X-ray photoelectron spectroscopy ; Spectral line ; Binding energy ; Monolayer ; Graphene ; Monte Carlo method ; Materials science ; Bayesian probability ; Replica ; Computer science ; Physics ; Nanotechnology ; Atomic physics ; Artificial intelligence ; Nuclear magnetic resonance ; Mathematics ; Statistics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Yanqi Jia | Shuangli Dong | Yongbao XIAO
Source: Chinese Journal of Luminescence (article, 2023-01-01)
Field: Materials Science — Ceramics and Composites

Quantitative Calculation and Prediction of Spectroscopic Properties of Thulium-doped Germanate Laser Glass Abstract Rare-earth-doped laser glass is the key medium of fiber lasers.However how to predict the spectroscop ic properties quantitatively remains a challenge to accelerate the development of high-performance laser glass.Here we regard the nearest-neighboring congruently melting compound(CMC) as the component and structural "motif" of glass based on the phase diagram model and apply it to the Tm 3+ -doped binary germanate laser glass systems.The ex perimental properties of Tm 3+ -doped glassy CMCs are utilized to calculate and predict the physical and spectroscopic properties such as density refractive index effective linewidth absorption/emission cross-sections radiation life time etc. of germanate laser glass by utilizing the leverage rule.The results illustrate that the predicted physical and spectroscopic properties are in good agreement with the experimental values with the maximum absolute errors of less than 4. 61% and 9. 66% respectively.Moreover the phase diagram approach can capture the trends of physi cal and spectroscopic properties as a function of composition including the linearly or germanate-anomaly composi tional dependence which provides an opportunity to decipher the composition-structure-property relationships of la ser glass.This study is expected to shed light on the property prediction and composition design of laser glass. Germanate ; Materials science ; Thulium ; Doping ; Laser ; Optics ; Analytical Chemistry (journal) ; Optoelectronics ; Environmental chemistry ; Physics Glass properties and applications ; Laser Material Processing Techniques ; Solid State Laser Technologies
Authors: Bo Ni | David L. Kaplan | Markus J. Buehler
Source: PubMed (preprint, 2023-10-16)
Field: Materials Science — Biomaterials

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties. Mechanical strength ; Computer science ; Nonlinear system ; Synthetic biology ; Protein design ; Sequence (biology) ; Biological system ; Nanotechnology ; Protein structure ; Materials science ; Chemistry ; Physics ; Computational biology ; Biology Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Force Microscopy Techniques and Applications
Authors: Donghwi Park | Feng Guo | Naksoo Kim
Source: Applied Sciences (article, 2023-11-14)
Field: Materials Science — Metals and Alloys

Stress Corrosion Cracking Analysis of a Hot Blast Stove Shell with an Internal Combustion Chamber The stress corrosion cracking during the operation of the internal combustion hot blast stove was analysed. The computational fluid dynamics and finite element analysis models were established to analyse the temperature, stress and other variables related to the condensation of the water and acids. The corrosion characteristics of condensation of acid and the stress corrosion cracking of the metallic shell of the hot blast stove during the operation were predicted by applying the fluid temperature and mapping it to the solid temperature. The stress corrosion cracking surface mobility mechanism was adopted and modified with a weight concept to consider the effect of the acid condensation and its concentration. The regions that have higher crack propagation rates were analysed. The influence of the increase in the blast temperature on the crack propagation rate was studied with the increase in the blast temperature by 45 K and 90 K from the reference blast temperature. The maximum temperature of the refractory linings was 1847 K in the on-gas period, and the maximum change in the shell temperature was 5.2 K when the blast temperature was increased by 90 K. The maximum crack propagation rate for the reference blast temperature was evaluated as 7.61×10−7 m/s. The maximum value of the crack propagation rate was increased by 16.7% when the blast temperature increased by 90 K. The conical region was found to have higher crack propagation rates, which means that the conical region should be the region of interest for managing the internal combustion hot blast stoves. Materials science ; Cracking ; Combustion ; Corrosion ; Stove ; Stress corrosion cracking ; Fracture mechanics ; Stress (linguistics) ; Condensation ; Metallurgy ; Composite material ; Thermodynamics ; Waste management ; Chemistry ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Iron and Steelmaking Processes ; Nuclear Materials and Properties
Authors: Yuxuan Jin | Zefeng Chen
Source: Advances in Engineering Technology Research (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

High gain hot-carrier WSe2 phototransistor with gate-tunable responsivity Hot-carrier injection at semiconductor/metal interface shows great potentials in infrared photodetection. However, the photoresponsivity of hot-carrier photodetector with diode mode is still limited in the scale of 1 mA/W due to the low injection efficiency of hot carriers and the lack of gain. Here, we demonstrate a high gain hot-carrier WSe2 phototransistor with gate-tunable responsivity. In this device, plasmonic resonances is used to enhance the light absorption of gold nanodisk, which provide hot holes. The hot holes are trapped into WSe2 and recycled in WSe2 channel in lateral direction, which introduce high gain for photodetection. Experiment shows that the photoresponsivity of the device can be over 0.23 A/W with a gain of 270 at the wavelength of 1310 nm. More interestingly, the responsivity of the device can be tuned by gate, which can be used to encode synaptic weights of the sensor pixel. Responsivity ; Photodetection ; Optoelectronics ; Materials science ; Photodetector ; Photodiode ; Semiconductor ; Diode 2D Materials and Applications ; Nanowire Synthesis and Applications ; Neural Networks and Reservoir Computing
Authors: N. V. Krysko | S. V. Skrynnikov | N. A. Shchipakov | Д. М. Козлов | A. G. Kusyy
Source: Дефектоскопия (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Classification and sizing of surface defects of pipelines based on the results of complex diagnostics by ultrasonic, eddy current and visual and measuring methods of nondestructive testing The issues of classification and determination of parameters of surface operational defects according to the results of ultrasonic, eddy current and visual and measuring methods of nondestructive testing are considered. At the same time, the visual and measuring method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented, which has been applied to classify the images obtained from a TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics, in which the obtained models are applied, and determines the accuracy of this algorithm by the RMSE metric, which was calculated within the studying test data set and amounted to 0.011 mm. Nondestructive testing ; Sizing ; Eddy-current testing ; Eddy current ; Ultrasonic sensor ; Artificial intelligence ; Pipeline transport ; Computer science ; Visual inspection ; Convolutional neural network ; Triangulation ; Computer vision ; Acoustics ; Engineering ; Mechanical engineering ; Mathematics Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Structural Integrity and Reliability Analysis
Authors: Georg Wallmann | Andrew Leduc | Nikolai Slavov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-02-03)
Field: Chemistry — Spectroscopy

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS . Computer science ; Mass spectrometry ; Duty cycle ; Data acquisition ; Data quality ; Multiplexing ; Data mining ; Throughput ; Proteomics ; Chemistry ; Metric (unit) ; Chromatography ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yiling Nan | Alexander D. MacKerell
Source: ChemRxiv (preprint, 2023-12-19)
Field: Chemistry — Spectroscopy

Balancing Group I Monoatomic Ion-Polar Compound Interactions for Condensed Phase Simulation in the Polarizable Drude Force Field Molecular dynamics (MD) simulations are a commonly used method for investigating molecular behavior at the atomic level. Achieving reliable MD simulation results necessitates the use of an accurate force field. In the present work, we present a new protocol to enhance the quality of group 1 monoatomic ions (specifically Li+, Na+, K+, Rb+, and Cs+) with respect to their interactions with common polar model compounds in biomolecules in condensed phases in the context of the Drude polarizable force field. Instead of adjusting pre-existing individual parameters for ions, model compounds, and water, we employ atom-pair specific LJ (known as NBFIX in CHARMM) and through-space Thole dipole screening (NBTHOLE) terms to fine-tune the balance of ion-model compound, ion-water, and model compound-water interactions. This involved establishing a protocol for the optimization of NBFIX and NBTHOLE parameters targeting the difference between molecular mechanical (MM) and quantum mechanical (QM) potential energy scans (PES). It is shown that targeting PES involving complexes that include multiple model compounds and/or ions as trimers and tetramers yields parameters that produce condensed phase properties in agreement with experimental data. Validation of this protocol involved the reproduction of experimental thermodynamic benchmarks, including solvation free energies of ions in methanol and N-methyl acetamide, osmotic pressures, ionic conductivities, and diffusion coefficients within the condensed phase. These results show the importance of including more complex ion-model compound complexes beyond dimers in the QM target data to account for many-body effects during parameter fitting. The presented parameters represent a significant refinement of the Drude polarizable force field, which will lead to improved accuracy for modeling ion-biomolecular interactions. Polarizability ; Chemistry ; Force field (fiction) ; Molecular dynamics ; Solvation ; Ion ; ReaxFF ; Chemical physics ; Dipole ; Monatomic gas ; Water model ; Computational chemistry ; Thermodynamics ; Molecule ; Physics ; Organic chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: M. G. Lavrentev | M. V. Voronov | Aleksey A. Ivanov | В. П. Панченко | N. Yu. Tabachkova | M. K. Tapero | Ivan Yu. Yarkov
Source: Modern Electronic Materials (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Mechanical properties of medium-temperature thermoelectric materials based on tin and lead tellurides The strength and thermoelectric properties of PbTe and Sn 0.9 Pb 0.1 Te medium-temperature polycrystalline specimens with p and n conductivity types, respectively, have been studied. The specimens have been produced using extrusion and spark plasma sintering. The strength parameters of the materials were studied using uniaxial compression at 20 to 500 °C. The structure of the materials was studied using X-ray diffraction and electron microscopy. The electrical conductivity and the Seebeck coefficient were measured simultaneously using the four-probe and differential methods. The temperature conductivity and the specific heat capacity were measured using the laser flash and differential scanning calorimetry methods. The PbTe and Sn 0.9 Pb 0.1 Te materials produced using extrusion and spark plasma sintering prove to be single-phase and have homogeneous compositions. For comparable synthesis methods, the dislocation density in the Sn 0.9 Pb 0.1 Te specimens is by an order of magnitude lower than in the PbTe ones. Study of the mechanical properties of n and p conductivity type specimens over a wide temperature range from 20 to 500 °C has shown that their deformation is plastic and has no traces of brittle fracture. For these plastic materials, the strength criterion has been accepted to be the arbitrary yield stress corresponding to the stress at a 0.2% deformation. The 20 °C yield stress of PbTe and Sn 0.9 Pb 0.1 Te is far higher for the specimens produced by extrusion. For all the test temperatures and synthesis methods the Sn 0.9 Pb 0.1 Te specimens have a higher strength than the PbTe ones. The PbTe and Sn 0.9 Pb 0.1 Te specimens produced by extrusion have better thermoelectric properties than the spark plasma sintered ones. The heat conductivity of the PbTe and Sn 0.9 Pb 0.1 Te specimens is almost the same regardless of compaction method. Materials science ; Spark plasma sintering ; Thermoelectric effect ; Composite material ; Thermoelectric materials ; Extrusion ; Differential scanning calorimetry ; Crystallite ; Atmospheric temperature range ; Brittleness ; Conductivity ; Thermal conductivity ; Analytical Chemistry (journal) ; Sintering ; Metallurgy ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal properties of materials
Authors: Mircea Petrache | Shubhendu Trivedi
Source: arXiv (Cornell University) (preprint, 2023-05-27)
Field: Materials Science — Materials Chemistry

Approximation-Generalization Trade-offs under (Approximate) Group Equivariance The explicit incorporation of task-specific inductive biases through symmetry has emerged as a general design precept in the development of high-performance machine learning models. For example, group equivariant neural networks have demonstrated impressive performance across various domains and applications such as protein and drug design. A prevalent intuition about such models is that the integration of relevant symmetry results in enhanced generalization. Moreover, it is posited that when the data and/or the model may only exhibit $\textit{approximate}$ or $\textit{partial}$ symmetry, the optimal or best-performing model is one where the model symmetry aligns with the data symmetry. In this paper, we conduct a formal unified investigation of these intuitions. To begin, we present general quantitative bounds that demonstrate how models capturing task-specific symmetries lead to improved generalization. In fact, our results do not require the transformations to be finite or even form a group and can work with partial or approximate equivariance. Utilizing this quantification, we examine the more general question of model mis-specification i.e. when the model symmetries don't align with the data symmetries. We establish, for a given symmetry group, a quantitative comparison between the approximate/partial equivariance of the model and that of the data distribution, precisely connecting model equivariance error and data equivariance error. Our result delineates conditions under which the model equivariance error is optimal, thereby yielding the best-performing model for the given task and data. Our results are the most general results of their type in the literature. Equivariant map ; Homogeneous space ; Generalization ; Symmetry (geometry) ; Group (periodic table) ; Computer science ; Symmetry group ; Generalization error ; Mathematics ; Artificial neural network ; Pure mathematics ; Artificial intelligence ; Mathematical analysis ; Geometry ; Physics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning in Healthcare
Authors: Daniel T. Speckhard | Christian Carbogno | Luca M. Ghiringhelli | S. Lübeck | Matthias Scheffler | Claudia Draxl
Source: arXiv (Cornell University) (preprint, 2023-03-26)
Field: Materials Science — Materials Chemistry

Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model is used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions. Extrapolation ; Basis (linear algebra) ; Limit (mathematics) ; Mathematics ; Quantile ; Statistical physics ; Applied mathematics ; Set (abstract data type) ; Function (biology) ; Algorithm ; Statistics ; Computer science ; Mathematical analysis ; Geometry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis