jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junho Kim | Yeachan Kim | Jun-Hyung Park | Yerim Oh | Suho Kim | SangKeun Lee
Source: arXiv (Cornell University) (preprint, 2024-10-19)
Field: Materials Science — Materials Chemistry

MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the training strategy, given that materials science corpus has distinct characteristics from other domains. To this end, we first construct a comprehensive materials knowledge base from the scientific corpus by building semantic graphs. Leveraging this extracted knowledge, we integrate a curriculum into the adaptation process that begins with familiar and generalized concepts and progressively moves toward more specialized terms. We conduct extensive experiments across diverse benchmarks to verify the effectiveness and generality of MELT. A comprehensive evaluation convincingly supports the strength of MELT, demonstrating superior performance compared to existing continued pre-training methods. The in-depth analysis also shows that MELT enables PLMs to effectively represent materials entities compared to the existing adaptation methods, thereby highlighting its broad applicability across a wide spectrum of materials science. Adaptation (eye) ; Training (meteorology) ; Computer science ; Psychology ; Physics ; Meteorology Machine Learning in Materials Science
Authors: В. І. Маковишин | М. М. Демчина
Source: METHODS AND DEVICES OF QUALITY CONTROL (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

ПІДВИЩЕННЯ ТЕРМОЕЛЕКТРИЧНИХ ВЛАСТИВОСТЕЙ ПАРОФАЗНИХ КОНДЕНСАТІВ SnTe ЗА ДОПОМОГОЮ МЕТОДІВ ШІ У статті досліджуються можливості підвищення термоелектричних властивостей станум телуриду (SnTe) за допомогою сучасних методів штучного інтелекту. SnTe — напівпровідниковий матеріал із кристалічною структурою NaCl, вузькою забороненою зоною (~0,2 еВ) і високою концентрацією носіїв заряду, широко використовується для створення термоелектричних елементів р-типу провідності. Однак ефективність SnTe обмежується структурними дефектами, зокрема вакансіями стануму, що впливає на електропровідність (σ), коефіцієнт Зеєбека (S) та термоелектричну потужність (S²σ). Методи машинного навчання та генеративні алгоритми дозволяють прогнозувати властивості матеріалу й оптимізувати умови синтезу, скорочуючи час і витрати. Використовуючи нейронні мережі, змодельовано зв’язки між складом, морфологією та термоелектричними характеристиками. Це дозволило створити нові склади SnTe з легуючими елементами (галлій, бісмут), які демонструють покращені властивості. Методологія включала осадження плівок SnTe (40–800 нм), аналіз морфології атомно-силовою мікроскопією та дифрактометрією, створення навчальних наборів даних. Генетичні алгоритми використовувались для пошуку оптимальних складів, а моделі комп’ютерного зору автоматизували аналіз поверхонь, визначаючи оптимальну орієнтацію нанокристалів для мінімізації теплового опору. Результати показали підвищення електропровідності до ~12·10³ Ом⁻¹·см⁻¹, коефіцієнта Зеєбека до ~85 мкВ/К і термоелектричної потужності до ~25 мкВт/К²·см (покращення на 39–50%). Оптимізація товщини плівок і розподілу нанокристалів суттєво вплинула на зниження теплових втрат. Отримані результати відкривають перспективи застосування SnTe у високоефективних термоелектричних генераторах, охолоджувальних системах, промисловій утилізації тепла та відновлюваній енергетиці. Materials science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Surface Treatment and Coatings
Authors: Tomas André | Ibrahim Dawod | Sebastian Cardoch | Emiliano De Santis | Nicuşor Tı̂mneanu | Carl Caleman
Source: Desy publication database (The Deutsches Elektronen-Synchrotron) (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

Protein structure classification based on X-ray laser induced Coulomb explosion We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and t-distributed stochastic neighbor embedding to classify these. The results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each of the proteins. We envision that method could be used concurrently with single particle coherent imaging experiments to provide additional information on shape, mass, or conformation. Coulomb explosion ; X-ray ; Laser ; Coulomb ; Physics ; Optics ; Nuclear physics ; Quantum mechanics ; Ionization Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Saqib Qamar | Abu Imran Baba | Stéphane Verger | Magnus Andersson
Source: arXiv (Cornell University) (preprint, 2024-01-30)
Field: Materials Science — Polymers and Plastics

Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Typically, this involves macerating (soaking) samples in a solution to separate cells, then spreading them on slides for imaging with a microscope that covers a wide area, capturing thousands of cells. However, these cells often cluster and overlap in images, making the segmentation difficult and time-consuming using standard image-processing methods. In this work, we developed an automatic deep learning segmentation approach that utilizes the one-stage YOLOv8 model for fast and accurate segmentation and characterization of macerated fiber and vessel form aspen trees in microscopy images. The model can analyze 32,640 x 25,920 pixels images and demonstrate effective cell detection and segmentation, achieving a mAP_{0.5-0.95} of 78 %. To assess the model's robustness, we examined fibers from a genetically modified tree line known for longer fibers. The outcomes were comparable to previous manual measurements. Additionally, we created a user-friendly web application for image analysis and provided the code for use on Google Colab. By leveraging YOLOv8's advances, this work provides a deep learning solution to enable efficient quantification and analysis of wood cells suitable for practical applications. Segmentation ; Artificial intelligence ; Characterization (materials science) ; Deep learning ; Computer science ; Materials science ; Nanotechnology Textile materials and evaluations
Authors: I. Starke | Sylvia Fürstenberg | St. Thomas
Source: Rapid Communications in Mass Spectrometry (article, 2024-09-12)
Field: Chemistry — Spectroscopy

Utilizing ESI‐MS/MS and ion mobility spectrometry for structural characterization and isomer differentiation of 1, 2‐unsaturated (1‐4) linked disaccharide derivatives and their 2‐ <i>C</i> ‐functionalized analogues ESI CID spectra of [M + Li]<sup>+</sup> adduct ions for individual glycal disaccharide analogues facilitate the discrimination between alpha and beta (1-4) linked unsaturated disaccharides and their 2-C-branched analogues. Through optimized experimental conditions, complete baseline ion mobility separation of stereoisomer pairs of the Cs adducts is achieved. Chemistry ; Disaccharide ; Adduct ; Tandem mass spectrometry ; Electrospray ionization ; Mass spectrometry ; Computational chemistry ; Stereochemistry ; Organic chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Glycosylation and Glycoproteins Research
Authors: Ester Costa de Almeida | Tiago José Bandeira Sales | Roberta Helena Mendonça
Source: Revista Foco (article, 2024-11-28)
Field: Materials Science — Biomaterials

DEVELOPMENT AND CHARACTERIZATION OF PLA/BULGUR BIODEGRADABLE COMPOSITES FOR LIQUID SPILL MITIGATION APPLICATIONS This study investigates the development and characterization of composite films composed of polylactic acid (PLA) and Bulgur, with potential applications for liquid spill mitigation on road surfaces. Fourier-transform infrared spectroscopy (FTIR) was employed to analyze the chemical structures of the composites, revealing changes in functional group vibrations as the Bulgur content increased. Scanning electron microscopy (SEM) provided insights into the morphological evolution, showing increased porosity and phase separation at higher Bulgur concentrations. Fluid absorption tests demonstrated that while pure PLA exhibited low water absorption, the incorporation of Bulgur significantly enhanced the composite’s hydrophilic properties, particularly in a 70:30 PLA/Bulgur ratio. The results suggest that PLA/Bulgur composites, with their biodegradable and environmentally-safe nature, offer an effective solution for mitigating liquid spills, with a balance between water absorption and mechanical integrity. Characterization (materials science) ; Materials science ; Composite material ; Nanotechnology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Silicone and Siloxane Chemistry
Authors: Tamella Maharramova Elchin Melikov Elchin Melikov
Source: PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions (article, 2024-12-04)
Field: Materials Science — General Materials Science

CONTROL OF THE GASOLINE HYDROTREATING PROCESS In a market economy, gasoline hydrotreating at modern oil refineries is considered one of the most important technological processes. In conditions where the demand for high-quality gasoline on the world market is high, control of the gasoline hydrotreating process is a very pressing issue. As oil refineries strive to meet stricter environmental regulations, hydrotreating processes are becoming increasingly important. An automated control system has been developed for such a technological process. During hydrotreating, petroleum fractions selectively react with hydrogen in the catalyst presence at relatively high temperatures and medium pressure. This process converts unwanted aromatics, olefins, nitrogen, metals, and organic sulfur compounds into stabilized products. Some hydrotreated fractions may require additional processing to meet final product specifications. The presence of compounds containing sulfur, nitrogen, and oxygen in diesel and gasoline fractions is also extremely undesirable, as this leads to a deterioration in the characteristics of diesel engines and internal combustion engines. The composition of these compounds is also undesirable from an environmental point of view. As a hydrotreating result, the petroleum products quality increases. As the molecular weight of the fraction increases, the desulfurization rate decreases. The gasoline hydrotreating process continues, releasing a small amount of heat, which is used to compensate for heat losses to the environment. Keywords: hydrotreating process, oil refinery, petroleum products, automation system, control object, optimal control. Hydrodesulfurization ; Gasoline ; Diesel fuel ; Oil refinery ; Waste management ; Petroleum product ; Flue-gas desulfurization ; Petroleum ; Fuel oil ; Fluid catalytic cracking ; Octane rating ; Environmental science ; Process engineering ; Chemistry ; Sulfur ; Engineering ; Cracking ; Organic chemistry Material Properties and Applications ; Engineering and Environmental Studies ; Engineering Diagnostics and Reliability
Authors: Jichen Zhu | Zheng Wang | Zhenwei Shen | Lai Wei | Fengyuan Tian | Mengchen Liu | Shi‐Xia Liu
Source: arXiv (Cornell University) (preprint, 2024-08-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

ReorderBench: A Benchmark for Matrix Reordering Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for specific tasks. In this paper, we build a matrix reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each featuring one of four visual patterns: block, off-diagonal block, star, or band. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering. Benchmark (surveying) ; Computer science ; Matrix (chemical analysis) ; Parallel computing ; Materials science ; Geography ; Cartography ; Composite material Liquid Crystal Research Advancements
Authors: Adriana R. Sánchez | Jorge Francés | Adrián Moya | Emilio J. Mena | Joan Josep Sirvent-Verdú | Eva María Calzado Estepa | Sergi Gallego | Inmaculada Pascual | Andrés Márquez
Source:  (article, 2024-08-16)
Field: Materials Science — Surfaces, Coatings and Films

Challenges and impact of the grating vector direction in high-frequency LC-SLM binary phase gratings Spatial Light Modulators (SLMs) typically use PA-LCoS microdisplays, known for high resolution and small pixel size, for phase-only modulation. However, different phenomena related to the cross-talk, fringing fields between pixels, and out-of-plane reorientation of the liquid crystal director degrade the performance of pixelated SLM devices. The technology faces the challenge of overcoming these phenomena as novel applications require higher pixel size resolutions and smaller pixel sizes. This work applies a numerical workflow that permits the analysis of the different parameters that set up a PA-LCoS microdisplay, e.g., fill factor, pixel size, external voltage and the influence of the LC director and the interaction of light through the structure. Specifically, we focus our analysis on the impact of high-frequency binary phase gratings on the radiometric and polarimetric response with orthogonal grating vectors. The heterogenous LC distribution as a function of the grey level along the 3D structure and the inhomogeneous distribution of the Stokes parameters are analysed, setting up a framework to analyse the nonlinear behaviour of the system and its impact on diffraction and polarimetric performance in future microdisplays with higher resolutions and smaller pixel sizes. Grating ; Binary number ; Phase (matter) ; Optics ; Materials science ; Computer science ; Physics ; Mathematics ; Arithmetic Optical Coatings and Gratings ; Advanced Fiber Optic Sensors ; Photonic and Optical Devices
Authors: Kang Guo | Qian Li | Siying Wang | Jun Zhang | Zexin Zhang
Source: Applied Mathematics and Nonlinear Sciences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Research on the Application of Intelligent Algorithms in Preventive Damage Prediction and Diagnosis of Power Cable Channels Abstract In this paper, according to the definition of power cable channel damage and the actual situation, the vibration signals of mechanical excavation, crushing hammer construction, manual excavation and heavy vehicles passing by are selected as the initial data for the prediction of preventive damage of power cable channels. After completing the data collection, Variable Difference Modal Decomposition (VMD) is applied to reduce noise and obtain the IMF components. The eigenvalues such as energy entropy, cliff factor, waveform factor, center of gravity frequency and frequency standard deviation of each IMF component are extracted to form an eigenvector set, thus forming the data set for the study and analysis. The data set is divided into a training set and a test set according to the ratio of 8:2, and the long and short-term memory neural network is used to study the power cable channel damage prediction and diagnosis. The MAE in the single-step prediction of LSTM is 1.08, the MRE is 2.69%, and the RMSE is 1.39, and the prediction model in this paper is much better than the control prediction model, which indicates that the LSTM network can well predict the vibration of the damage of the power cable channel. It shows that the LSTM network can accurately predict and diagnose the signal trends and fluctuations caused by damaged power cable channel vibration. Vibration ; Waveform ; Computer science ; Algorithm ; Channel (broadcasting) ; Artificial neural network ; Data set ; Engineering ; Modal ; Structural engineering ; Voltage ; Artificial intelligence ; Acoustics ; Telecommunications ; Electrical engineering High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Geotechnical Engineering and Underground Structures
Authors: Isaac L. Huidobro-Meezs | Jun Dai | Guillaume Rabusseau | Rodrigo A. Vargas–Hernández
Source: arXiv (Cornell University) (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

GFlowNets for Hamiltonian decomposition in groups of compatible operators Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design. Decomposition ; Hamiltonian (control theory) ; Mathematics ; Pure mathematics ; Algebra over a field ; Chemistry ; Mathematical optimization Graphite, nuclear technology, radiation studies ; Mathematical Control Systems and Analysis ; Information Systems and Technology Applications
Authors: Adam Smith | Yogesh K. Vohra | Cheng-Chien Chen
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Materials Chemistry

First-Principles Calculation of Superconducting $T_c$ in Superhard B-C-N Metals We perform first-principles electron-phonon calculations to evaluate the superconducting transition temperature $T_c$ for ternary superhard metals B$_2$C$_3$N and B$_4$C$_5$N$_3$. These materials are predicted to exhibit a wide distribution of electron-phonon coupling parameters on their multiple Fermi surface sheets, which necessitates solving the anisotropic Eliashberg equations for accurate determination of $T_c$. An ambient-pressure $T_c$ of $\sim 40$ K and $\sim 20$ K is obtained respectively for B$_2$C$_3$N and B$_4$C$_5$N$_3$ from the anisotropic gap equations. The relatively high $T_c$ of these compounds is due in part to their high Debye temperatures associated with superhardness. The materials under study are potentially synthesizable, as their formation energies are comparable to those of other recently synthesized superhard B-C-N compounds. Therefore, studying superhard metals could hold the promise of realizing new higher-$T_c$ superconductors at ambient pressure. Superconductivity ; Materials science ; Condensed matter physics ; Engineering physics ; Physics Boron and Carbon Nanomaterials Research ; Ferrocene Chemistry and Applications ; Organoboron and organosilicon chemistry
Authors: Evan Lambertson | Dayana Bashirova | Kye E. Hunter | Benhardt Hansen | Tim J. Zuehlsdorff
Source: arXiv (Cornell University) (preprint, 2024-06-26)
Field: Chemistry — Spectroscopy

Computing linear optical spectra in the presence of nonadiabatic effects on Graphics Processing Units using molecular dynamics and tensor-network approaches We compare two recently developed strategies, implemented in open source software packages, for computing linear optical spectra in condensed phase environments in the presence of nonadiabatic effects. Both approaches rely on computing excitation energy and transition dipole fluctuations along molecular dynamics (MD) trajectories, treating molecular and environmental degrees of freedom on the same footing. Spectra are then generated in two ways: In the recently developed Gaussian Non-Condon Theory (GNCT), the linear response functions are computed in terms of independent adiabatic excited states, with non-Condon effects described through spectral densities of transition dipole fluctuations. For strongly coupled excited states, we instead parameterize a linear vibronic coupling (LVC) Hamiltonian directly from spectral densities of energy fluctuations and diabatic couplings computed along the MD trajectory. The optical spectrum is then calculated using powerful, numerically exact tensor-network approaches. Both the electronic structure calculations to sample system fluctuations and the quantum dynamics simulations using tensor-network methods are carried out on graphics processing units (GPUs), enabling rapid calculations on complex condensed phase systems. We assess the performance of the approaches using model systems in the presence of a conical intersection (CI), and the pyrazine molecule in different solvent environments. Graphics ; Tensor (intrinsic definition) ; Computer science ; Dynamics (music) ; Spectral line ; Molecular dynamics ; Computational science ; Statistical physics ; Physics ; Computer graphics (images) ; Chemistry ; Mathematics ; Computational chemistry ; Geometry Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Photonic and Optical Devices
Authors: Adna Kumar | Shrakajana Gupta | Isha Patel | Aarav Sharma | Rohan Desai
Source: Research Square (preprint, 2024-09-10)
Field: Materials Science — Surfaces, Coatings and Films

Enhanced Inverse Design of Thin-Film High Reflectors Using a Mixture Density Network and Optimization Framework Inverse ; Materials science ; Thin film ; Computer science ; Nanotechnology ; Mathematics ; Geometry Optical Coatings and Gratings ; Advanced optical system design ; 3D IC and TSV technologies