jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian M. Heil | Yingzhen Ma | Bhuvnesh Bharti | Arthi Jayaraman
Source: JACS Au (article, 2023-02-20)
Field: Materials Science — Materials Chemistry

Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (“<i>P</i>(<i>q</i>) and <i>S</i>(<i>q</i>) CREASE”) In this paper, we present an open-source machine learning (ML)-accelerated computational method to analyze small-angle scattering profiles [<i>I</i>(<i>q</i>) vs <i>q</i>] from concentrated macromolecular solutions to simultaneously obtain the form factor <i>P</i>(<i>q</i>) (<i>e.g</i>., dimensions of a micelle) and the structure factor <i>S</i>(<i>q</i>) (<i>e.g</i>., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain <i>P</i>(<i>q</i>) from dilute macromolecular solutions (where <i>S</i>(<i>q</i>) ∼1) or to obtain <i>S</i>(<i>q</i>) from concentrated particle solutions when <i>P</i>(<i>q</i>) is known (<i>e.g</i>., sphere form factor). This paper's newly developed CREASE that calculates <i>P</i>(<i>q</i>) and <i>S</i>(<i>q</i>), termed as "<i>P</i>(<i>q</i>) and <i>S</i>(<i>q</i>) CREASE", is validated by taking as input <i>I</i>(<i>q</i>) vs <i>q</i> from <i>in silico</i> structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. We demonstrate how "<i>P</i>(<i>q</i>) and <i>S</i>(<i>q</i>) CREASE" performs if given two or three of the relevant scattering profiles-<i>I</i> <sub>total</sub>(<i>q</i>), <i>I</i> <sub>A</sub>(<i>q</i>), and <i>I</i> <sub>B</sub>(<i>q</i>)-as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of "<i>P</i>(<i>q</i>) and <i>S</i>(<i>q</i>) CREASE" on <i>in silico</i> structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant-coated nanoparticles with varying extents of aggregation. Scattering ; Micelle ; Structure factor ; Form factor (electronics) ; Small-angle X-ray scattering ; Small-angle neutron scattering ; Physics ; Small-angle scattering ; Neutron scattering ; Chemistry ; Crystallography ; Optics ; Quantum mechanics ; Physical chemistry ; Aqueous solution Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Advanced Polymer Synthesis and Characterization
Authors: Hiroaki Kusunose | Rikuto Oiwa | Satoru Hayami
Source: Physical review. B./Physical review. B (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Symmetry-adapted modeling for molecules and crystals We have developed a symmetry-adapted modeling procedure for molecules and crystals. By using the completeness of multipoles to express spatial and time-reversal parity-specific anisotropic distributions, we can generate systematically the complete symmetry-adapted multipole basis set to describe any of electronic degrees of freedom in isolated cluster systems and periodic crystals. The symmetry-adapted modeling is then achieved by expressing the Hamiltonian in terms of the linear combination of these bases belonging to the identity irreducible representation, and the model parameters (linear coefficients) in the Hamiltonian can be determined so as to reproduce the electronic structures given by the density-functional computation. We demonstrate our method for the modeling of graphene and emphasize usefulness of the symmetry-adapted basis to analyze and predict physical phenomena and spontaneous symmetry breaking in a phase transition. The present method is complementary to de facto standard Wannier tight-binding modeling, and it provides us with a fundamental basis to develop a symmetry-based analysis for materials science. Hamiltonian (control theory) ; Multipole expansion ; Physics ; Symmetry operation ; Theoretical physics ; Basis (linear algebra) ; Symmetry (geometry) ; Quantum mechanics ; Wannier function ; Discrete symmetry ; Explicit symmetry breaking ; Parity (physics) ; Basis set ; Symmetry breaking ; Spontaneous symmetry breaking ; Statistical physics ; Molecule ; Rotational symmetry ; Mathematics ; Homogeneous space ; Geometry Graphene research and applications ; Advanced Chemical Physics Studies ; Surface and Thin Film Phenomena
Authors: Brittney Gorman | Suzy V. Torti | Frank M. Torti | Christopher Anderton
Source: Biochimica et Biophysica Acta (BBA) - General Subjects (review, 2023-02-13)
Field: Chemistry — Analytical Chemistry

Mass spectrometry imaging of metals in tissues and cells: Methods and biological applications Biomolecule ; Mass spectrometry imaging ; Mass spectrometry ; Nanotechnology ; Biological tissue ; Biological fluids ; Chemistry ; Biological materials ; Heavy metals ; Immunochemistry ; Metal ; Environmental chemistry ; Materials science ; Biological system ; Chromatography ; Biomedical engineering ; Biology Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Electrochemical Analysis and Applications
Authors: Binhui Wang | Zhitong Yao | Markus Reinmöller | Nanda Kishore | Fiseha Tesfaye | Rafael Luque
Source: Journal of Analytical and Applied Pyrolysis (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Pyrolysis behavior, kinetics, and thermodynamics of waste pharmaceutical blisters under CO2 atmosphere Pyrolysis ; Kinetics ; Atmosphere (unit) ; Thermodynamics ; Blisters ; Chemistry ; Materials science ; Organic chemistry ; Physics ; Composite material Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Recycling and Waste Management Techniques
Authors: Koen Veys | Manon H. E. Bousquet | Denis Jacquemin | Daniel Escudero
Source: Journal of Chemical Theory and Computation (article, 2023-12-11)
Field: Chemistry — Physical and Theoretical Chemistry

Modeling the Fluorescence Quantum Yields of Aromatic Compounds: Benchmarking the Machinery to Compute Intersystem Crossing Rates The <i>from-first-principles</i> calculation of fluorescence quantum yields (FQYs) and lifetimes of organic dyes remains very challenging. In this article, we extensively test the machinery to calculate FQYs. Specifically, we perform an extensive analysis on the parameters influencing the intersystem crossing (ISC), internal conversion (IC), and fluorescence rate constants calculations. The impact of (i) the electronic structure (chosen exchange-correlation functional and spin-orbit Hamiltonian), (ii) the vibronic parameters (coordinate system, broadening function, and dipole expansion), and (iii) the excited-state kinetic models are systematically assessed for a series of seven rigid aromatic molecules. Our studies provide more insights into the choice of parameters and the expected accuracy for the computational protocols aiming to deliver FQY values. Some challenges are highlighted, such as, on the one hand, the difficulty to benchmark against the experimental nonradiative rate constants, for which the separation between the IC and ISC contributions is often not provided in the literature and, on the other hand, the need to go beyond the harmonic approximation for the calculation of the IC rates. Intersystem crossing ; Hamiltonian (control theory) ; Excited state ; Internal conversion ; Reaction rate constant ; Quantum ; Dipole ; Harmonic oscillator ; Chemistry ; Computational chemistry ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Mathematics ; Mathematical optimization Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Spectroscopy and Quantum Chemical Studies
Authors: Roya Boodaghi Malidarre | H.O. Tekın | Kadir Günoğlu | Hakan Akyıldırım
Source: International Journal of Computational and Experimental Science and Engineering (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Assessment of Gamma Ray Shielding Properties for Skin Gamma ray is an energetic radiation type that can ionize and thus damage living cells as it slows down and transfers its energy to cells. Because of this harmful effect cell should be protected. Besides developing new alternative to lead and lead based materials, it should be interesting to obtain shielding properties of skin. This paper presents a results on the shielding properties of skin. Electromagnetic shielding ; Gamma ray ; Ionization ; Radiation ; Materials science ; Nuclear engineering ; Physics ; Optics ; Nuclear physics ; Composite material ; Engineering Radiation Shielding Materials Analysis ; Radioactivity and Radon Measurements ; Radiation Dose and Imaging
Authors: Tobias Brink | Lena Langenohl | Hanna Bishara | Gerhard Dehm
Source: Physical review. B./Physical review. B (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Universality of grain boundary phases in fcc metals: Case study on high-angle [111] symmetric tilt grain boundaries It has been reported that different ordered structures can appear at the same grain boundaries. Thermodynamically, they can be regarded as interface phases (``complexions''). The authors use atomistic simulations to show that several fcc transition and post-transition metals exhibit the same two grain boundary phases in [111] tilt grain boundaries. This remains true even when using unphysical pair potentials, highlighting that the occurrence of different grain boundary phases is not an exceptional special case, but common across different materials. Grain boundary ; Universality (dynamical systems) ; Tilt (camera) ; Condensed matter physics ; Grain boundary strengthening ; Materials science ; Boundary (topology) ; Geometry ; Physics ; Metallurgy ; Microstructure ; Mathematics ; Mathematical analysis Microstructure and mechanical properties ; Surface and Thin Film Phenomena ; Metal and Thin Film Mechanics
Authors: Arian Amirvaresi | Hadi Parastar
Source: Frontiers in Analytical Science (article, 2023-03-03)
Field: Chemistry — Analytical Chemistry

Miniaturized NIR spectroscopy and chemometrics: A smart combination to solve food authentication challenges Over the years, technology has allowed more accurate, more effective, and prompt food integrity assessments to assure the quality and authenticity of food material. Thanks to the development of portable and hand-held near infrared (NIR) as a rapid, reliable, non-destructive, and user-friendly instrument, on-site food analysis is provided with more feasibility. However, miniaturized NIR devices have some significant challenges due to the presence of varying noise resources which can lead to misinterpretation. In this context, chemometric methods with the capability of resolution, identification, classification, and calibration play a pivotal role in achieving precise and in-depth comprehension of the data. In the present mini-review, we will discuss miniaturized NIR instrumentation, some chemometric concepts, and introduce the most popular algorithm in food authentication problem. The main feature of this review is avoiding mathematical details as much as possible to make the material accessible to a broad audience but highlighting the key features of chemometric methods with some simple illustrative examples in the scope of food authenticity. Chemometrics ; Computer science ; Authentication (law) ; Context (archaeology) ; Scope (computer science) ; Identification (biology) ; Instrumentation (computer programming) ; Data mining ; Data science ; Biochemical engineering ; Process engineering ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rinske M. Alkemade | Frank Smallenburg | Laura Filion
Source: The Journal of Chemical Physics (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Improving the prediction of glassy dynamics by pinpointing the local cage The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here, we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To achieve this, we introduce a method to pinpoint the cage state of the initial configuration-i.e., the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime. Computer science ; Cage effect ; Dynamics (music) ; State (computer science) ; Simple (philosophy) ; Statistical physics ; Particle (ecology) ; Work (physics) ; Artificial intelligence ; Machine learning ; Algorithm ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Pickering emulsions and particle stabilization
Authors: Zi-Yi 子逸 Chen 陈 | Fan-Kai 帆恺 Xie 谢 | Meng Wan | Yang 扬 Yuan 袁 | Miao Liu | Zong-Guo 宗国 Wang 王 | Sheng 胜 Meng 孟 | Yan-Gang 彦棡 Wang 王
Source: Chinese Physics B (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

MatChat: A large language model and application service platform for materials science The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science. Computer science ; Generative grammar ; Software deployment ; Process (computing) ; Data science ; Service (business) ; Artificial intelligence ; Software engineering ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Hossein Kabir | Nishant Garg
Source: npj Materials Degradation (article, 2023-07-05)
Field: Materials Science — Surfaces, Coatings and Films

Rapid prediction of cementitious initial sorptivity via surface wettability Abstract The tendency of cementitious systems to absorb and transmit liquid through capillary pores is often characterized by initial sorptivity, which is an important indicator of long-term durability. However, sorptivity measurements, which are based on the continuous mass change of specimens exposed to water, are labor-intensive (up to 6 h of continuous measurements). Here, we exploit the fundamental surface-wetting characteristics of cementitious systems to estimate their sorptivity in a rapid fashion, i.e., in a matter of few minutes. In a series of 63 distinct paste systems of varying w/c ratios (0.4–0.8), subject to a range of curing periods (1–7d), we establish strong correlations (adjusted R 2 ≥ 0.9) between the initial sorptivity (~6 h) and dynamics of drop spreading (contact angle ~0.5 s, drop residence time &lt;10 min). These results elucidate rapid pathways in estimating initial sorptivity and durability of a broad variety of hydrated cementitious matrices. Sorptivity ; Cementitious ; Wetting ; Durability ; Materials science ; Composite material ; Drop (telecommunication) ; Capillary action ; Curing (chemistry) ; Contact angle ; Cement ; Engineering Surface Modification and Superhydrophobicity ; Building materials and conservation ; Concrete and Cement Materials Research
Authors: Yu Zhang | Qingrui Chang | Yi Chen | Yanfu Liu | Danyao Jiang | Zijuan Zhang
Source: Agronomy (article, 2023-08-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R2), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry. Hyperspectral imaging ; Partial least squares regression ; Mean squared error ; Feature selection ; Random forest ; Tree (set theory) ; Hyperparameter optimization ; Mathematics ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Statistics ; Support vector machine Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Hiroyoshi Tanaka | Takuya Nakazawa | Atsushi Enomoto | Atsushi Masamune | Mitsunobu R. Kano
Source: Cancers (review, 2023-01-24)
Field: Materials Science — Biomaterials

Therapeutic Strategies to Overcome Fibrotic Barriers to Nanomedicine in the Pancreatic Tumor Microenvironment Pancreatic cancer is notorious for its dismal prognosis. The enhanced permeability and retention (EPR) effect theory posits that nanomedicines (therapeutics in the size range of approximately 10-200 nm) selectively accumulate in tumors. Nanomedicine has thus been suggested to be the "magic bullet"-both effective and safe-to treat pancreatic cancer. However, the densely fibrotic tumor microenvironment of pancreatic cancer impedes nanomedicine delivery. The EPR effect is thus insufficient to achieve a significant therapeutic effect. Intratumoral fibrosis is chiefly driven by aberrantly activated fibroblasts and the extracellular matrix (ECM) components secreted. Fibroblast and ECM abnormalities offer various potential targets for therapeutic intervention. In this review, we detail the diverse strategies being tested to overcome the fibrotic barriers to nanomedicine in pancreatic cancer. Strategies that target the fibrotic tissue/process are discussed first, which are followed by strategies to optimize nanomedicine design. We provide an overview of how a deeper understanding, increasingly at single-cell resolution, of fibroblast biology is revealing the complex role of the fibrotic stroma in pancreatic cancer pathogenesis and consider the therapeutic implications. Finally, we discuss critical gaps in our understanding and how we might better formulate strategies to successfully overcome the fibrotic barriers in pancreatic cancer. Nanomedicine ; Tumor microenvironment ; Cancer research ; Medicine ; Tumor cells ; Nanotechnology ; Materials science ; Nanoparticle Nanoparticle-Based Drug Delivery ; Pancreatic and Hepatic Oncology Research ; Phagocytosis and Immune Regulation
Authors: Yuye Wang | Yu Sun | Yu Wang | Shuhong Jia | Yanan Qiao | Zhi Zhou | Wen Shao | Xiangfei Zhang | Jing Guo | Bin Zhang | Xiaoqian Niu | Yi Wang | Dantao Peng
Source: Alzheimer s Research & Therapy (article, 2023-11-04)
Field: Chemistry — Spectroscopy

Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Cognitive impairment ; Identification (biology) ; Disease ; Proteomics ; Urine ; Alzheimer's disease ; Cognition ; Medicine ; Psychology ; Computer science ; Neuroscience ; Internal medicine ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Alzheimer's disease research and treatments
Authors: Xi Tian | Xuefeng Liu | Xin He | Chi Zhang | Jiangbo Li | Wenqian Huang
Source: Journal of the Science of Food and Agriculture (article, 2023-06-02)
Field: Chemistry — Analytical Chemistry

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry. Bruise ; Hyperspectral imaging ; Multispectral image ; Artificial intelligence ; Computer science ; Principal component analysis ; Segmentation ; Remote sensing ; Pattern recognition (psychology) ; Computer vision ; Watershed ; Geology ; Medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity