jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivana Vasiljević | Erna Turković | Jelena Parojčić
Source: European Journal of Pharmaceutical Sciences (article, 2024-10-06)
Field: Chemistry — Analytical Chemistry

Data-driven insights into the properties of liquisolid systems based on machine learning algorithms Liquisolid systems (LS) represent a formulation approach where liquid drug or its dispersion is transformed into a powder with good flowability and compactibility, leading to enhanced drug dissolution and bioavailability. Many research groups have focused on the preparation and investigation of LS, leading to a higher need for comprehensive evaluation of factors impacting LS characteristics. The aim of this work was to investigate the applicability of machine learning algorithms in the LS evaluation, using data mined from published literature, and provide an insight into critical factors governing the liquisolid system performance. The dataset was prepared using publication search engines and relevant keywords, with a total of 425 formulations included in the database. The database focused on preparation methods, formulation parameters, and liquisolid system characteristics. Subsequently, critical properties of the liquisolid system, i.e. flowability, compact hardness, and drug dissolution, were analyzed using machine learning algorithms, including Gradient Boosting, Adaptive Boosting and Random Forest. In addition to conventional preparation methods and excipients, novel technologies (fluid bed preparation, extrusion/spheronization) and materials (Neusilin®, Fujicalin®, and Syloid®) enhanced the properties of liquisolid systems. The analysis revealed that formulation factors, such as carrier and coating agent type and content, liquid phase load, model drug type and content, as well as preparation method, significantly influenced liquisolid system characteristics. The models developed exhibited high prediction accuracy when applied on test data (higher than 80 %). This indicates that the machine learning models may provide an insight into the critical attributes affecting the LS performance and may be used as a valuable tool in the development and optimization of these samples. Computer science ; Machine learning ; Algorithm ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Food Chemistry and Fat Analysis
Authors: Sujitra Arwatchananukul | Saowapa Chaiwong | Nattapol Aunsri | Sila Kittiwachana | Kietsuda Luengwilai | Tatiya Trongsatitkul | Pramod V. Mahajan | J. Blasco | Rattapon Saengrayap
Source: Smart Agricultural Technology (article, 2024-10-10)
Field: Chemistry — Analytical Chemistry

Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms • Non-invasive technique for maturity and internal defect of Phulae pineapple was proposed. • Acoustics response integrated with machine learning (ML) was proposed. • The efficacy of the six ML classifiers was compared. • Random Forest (RF) provided the highest classification accuracy higher than 99.0 %. Acoustic response is non-destructive evaluation technique that replicates the conventional method for determining maturity by tapping the fruit. The physical (dimensions, color, firmness, and specific gravity) chemical (TSS, %TA, and TSS/TA), and acoustic properties of Phulae pineapple were determined and used to classify the maturity and defect, e.g. translucency flesh symptoms. Results showed that all physical parameters of the two maturity stages were not significantly different ( p > 0.05). Translucency flesh (TF) defects were observed in 23.5 % and 27.3 % of pineappls in the green and green-yellow stages, respectively. The dominant resonance frequency ( f n ) of Phulae pineapple ranged of 0.057 to 3.010 kHz. All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. Random Forest (RF) provided a better classification than other MLs with 99.93 % accuracy of maturity classification, while TF classification was 99.59 %. Results showed acoustic method integrated with ML was a fast reliable, and cost effective technique for assessing Phulae pineapple quality. Maturity (psychological) ; Algorithm ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Psychology Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Takayuki Shiraiwa | Koki Yasuda | Fabien Briffod | Mark Hyunpong Jhon | Fergyanto E. Gunawan | Rahul Sahay | Nagarajan Raghavan | Arief Suriadi Budiman | Manabu Enoki
Source: MATERIALS TRANSACTIONS (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Materials Informatics Approach to Cu/Nb Nanolaminate Microstructure Correlations with Yield Strength and Electrical Conductivity Empirical formulas were derived for the interface shape, mechanical properties, and electrical characteristics of accumulative roll bonded (ARB) Cu/Nb laminated materials, based on relevant literature data. These formulas were incorporated into a forward analysis model using finite element analysis, enabling the calculation of yield stress and conductivity from the spatial distribution of Cu/Nb two phases. By randomly varying the layer thickness and interface shape in the two-phase spatial distribution and conducting repeated forward analyses, a database linking microstructural descriptors with yield stress and conductivity was created. These microstructural descriptors include volume fraction, geometric features, topological features, spatial correlation functions, and persistent homology. The significance of each microstructural descriptor on yield stress and conductivity was quantified using machine learning techniques. The results revealed that the Cu volume fraction, layer thickness, and 0th Betti number are crucial for yield stress, while for conductivity, the Cu volume fraction has the strongest influence, followed by layer thickness and layer continuity. Based on these outcomes, the Pareto front for ARB Cu/Nb laminates in the strength-conductivity space was presented. Materials science ; Microstructure ; Yield (engineering) ; Electrical resistivity and conductivity ; Conductivity ; Metallurgy ; Composite material ; Materials informatics ; Health informatics ; Electrical engineering ; Physical chemistry Machine Learning in Materials Science
Authors: Kevin Kawchak
Source: ChemRxiv (preprint, 2024-08-28)
Field: Chemistry — Analytical Chemistry

LMM Spectrometric Determination of an Organic Compound Many machine learning models used in academia and industry that identify organic compounds typically lack the ability to converse over prompts and results, and also require expertise across a number of steps to obtain answers. The purpose of this study was primarily to gain insight into the advantages of current unmodified state of the art Large Multimodal Models (LMMs) across several prompts containing multiple spectra of varying difficulty to evaluate the impact of training data, reasoning, and speed. These readily available and easy to use software for the identification of an organic compound based on a molecular formula and spectra were found to be reproducible across three similar LMMs. To the author's best knowledge, this marks the first time that three GPT variants were each able to correctly identify the organic compound quinoline using a variety of different spectroscopic images. The results were obtained using a 2-step process consisting of a) Uploading high resolution spectral images, and b) Submitting a text prompt with the images that requested a compound determination. The main findings were that 1) Four LMMs provided rationale step-by-step interpretations of 1H-NMR, 13C-NMR, and 3 DEPT-NMR spectra from Prompt A, 2) Three of these LMMs, led by a GPT-5 preview model, combined these interpretations into the correct chemical structure with Prompt A, and 3) Two of these LMMs achieved a top score of 5/5 for also generating sequential explanations reflecting the order of the provided spectra along with most of the correct spectral and molecular formula explanations. Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Eric Verret | Anthony Collin | Sophie Duquesne
Source: Journal of Physics Conference Series (article, 2024-11-01)
Field: Materials Science — Polymers and Plastics

Optimization of Flame Retardant Polypropylene via Machine Learning Abstract In this study, we explore the application of Multi-Objective Bayesian Optimization (MOBO), a machine learning-based method, for the development of flame-retardant polypropylene (PP) formulations. This approach leverages the power of Gaussian Processes (GPs) to accurately model the behavior in the Limiting Oxygen Index (LOI) flame test and uses the qNEHVI acquisition function for efficient design space exploration. We focus on a bi-objective optimization strategy, aiming to maximize LOI values while minimizing the content of flame retardant (FR) additives. Our research successfully navigates the complexities of optimizing material properties by proposing an optimal formulation within a constrained evaluation framework, comprising five iterations of three parallel evaluations with a total budget of 20 points. This work highlights MOBO’s potential as a transformative tool for advanced materials science, particularly in achieving high-performance flame-retardant materials. Fire retardant ; Polypropylene ; Materials science ; Composite material ; Polymer science ; Computer science Flame retardant materials and properties ; Fire dynamics and safety research ; Fiber-reinforced polymer composites
Authors: Mengfan Wang | Lixin Zhang | Changxin Fu
Source: Scientific Reports (article, 2024-06-10)
Field: Materials Science — Polymers and Plastics

Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon Predicting and optimizing the mechanical performance of the helically wound nylon-reinforced rubber fertilizer hose (HWNR hose) is crucial for enhancing the performance of hose pumps. This study aims to enhance the service life of HWNR hoses and the efficiency of liquid fertilizer transport. First, a finite element simulation model and a mathematical model were established to analyze the influence of fiber layer arrangement on the maximum shear strain on the coaxial surface (MSS) and the reaction force on the extrusion roller (RF). For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). Results showed CPO-GRNN's superiority in handling complex nonlinear problems. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for optimization design. Compared to the original HWNR hose with an MSS of 0.906 and an RF of 30,376N, the optimized design reduced the MSS by 7.99% and increased the RF by 2.46%, significantly enhancing their service life and liquid fertilizer transport capacity. However, further research on fatigue damage is needed. Natural rubber ; Composite material ; Materials science ; Fertilizer ; Structural engineering ; Computer science ; Engineering ; Biology ; Agronomy Natural Fiber Reinforced Composites ; Soil Mechanics and Vehicle Dynamics ; Innovations in Concrete and Construction Materials
Authors: Tan Shi | Wenlong Liu | Chen Zhang | Sixin Lyu | Zhipeng Sun | Qing Peng | Yuanming Li | Fanqiang Meng | Chuanbao Tang | Chenyang Lu
Source: AIP Advances (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

An investigation of self-interstitial diffusion in <i>α</i>-zirconium by an on-the-fly machine learning force field The on-the-fly machine learning force field approach, based on the Gaussian approximation potential and Bayesian error estimation, was used to study the diffusion of self-interstitial atoms in α-zirconium. Ab initio molecular dynamics simulations of lattice vibration and interstitial diffusion at different temperatures were employed to develop the force field. The radial and angular descriptors of the potential were further optimized to achieve better agreement with first-principles results. Subsequent long-term diffusion simulations were performed to assess the diffusion behavior based on the obtained force field. Tracer diffusion coefficients and diffusion anisotropy were studied at temperatures of 600–1200 K, and the Bayesian errors were estimated throughout the diffusion simulations. The mean and maximum estimated Bayesian errors of atomic force were approximately twice as large as those observed during the learning period. The basal diffusion was greatly favored compared to the interstitial diffusion along the c-axis, consistent with previous simulations based on first-principles results and classical potentials. The accuracy and applicability of the current on-the-fly machine learning approach were critically evaluated. Force field (fiction) ; Diffusion ; Diffusion process ; Anisotropy ; Statistical physics ; Field (mathematics) ; Molecular dynamics ; Materials science ; Physics ; Artificial intelligence ; Chemistry ; Computer science ; Thermodynamics ; Computational chemistry ; Mathematics ; Optics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Hongfei Li | Yuanxu Zhu | M.-W. Chu | Haikuan Dong | Guohua Zhang
Source: Journal of Physics Condensed Matter (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Thermal conductivity of irregularly shaped nanoparticles from equilibrium molecular dynamics The computation of thermal conductivity for finite nanoparticulate systems, particularly those of irregular shapes, poses significant challenges. The nonequilibrium molecular dynamics (NEMD) methods has been extensively utilized in numerous prior studies for the computation of thermal conductivity of nanoparticles. One of our recent works (Dong<i>et al</i>2021<i>Phys. Rev.</i>B<b>103</b>035417) proposed that equilibrium molecular dynamics (EMD) methods can be used for the simulation of thermal conductivity of finite-scale systems and demonstrated their equivalence to NEMD methods. In this study, we investigated the application of the (EMD) approach for the computation of thermal conductivity in zero-dimensional nanoparticles. In our initial step, we merged both methodologies to substantiate the equivalence in thermal conductivity calculation for cube and cylinder nanoparticles. After filtering the data, we confirmed the usefulness of EMD for evaluating the thermal conductivity of zero-dimensional materials. The NEMD method faces challenges in accurately predicting thermal conductivity in nanoparticle systems with a varying cross-sectional area along the transport direction, whereas EMD methods can be utilized to estimate thermal conductivity when the volume is known. In a subsequent study, we used the state-of-the-art machine learning potential to calculate the thermal conductivity of spherical nanoparticles and compared the results with those obtained using the classical Tersoff potential. Ultimately, we predicted the thermal conductivity of nanoparticles with various geometries in all directions. Our findings collectively demonstrate the simplicity and effectiveness of employing EMD methods for calculating thermal conductivity in nanoparticle systems, thereby opening up new avenues for investigating thermal transport properties in particle systems as well as nanopders. Thermal conductivity ; Nanoparticle ; Molecular dynamics ; Thermal ; Dynamics (music) ; Thermal equilibrium ; Conductivity ; Materials science ; Chemical physics ; Chemistry ; Thermodynamics ; Mineralogy ; Nanotechnology ; Physical chemistry ; Physics ; Computational chemistry Thermal properties of materials ; Carbon Nanotubes in Composites ; Machine Learning in Materials Science
Authors: Sean T. Holmes | Cameron M. Boley | Angelika Dewicki | Zachary T. Gardner | Cameron S. Vojvodin | Robbie J. Iuliucci | Robert W. Schurko
Source: Magnetic Resonance in Chemistry (article, 2024-01-17)
Field: Chemistry — Spectroscopy

Carbon‐13 chemical shift tensor measurements for nitrogen‐dense compounds This paper reports the principal values of the <sup>13</sup> C chemical shift tensors for five nitrogen-dense compounds (i.e., cytosine, uracil, imidazole, guanidine hydrochloride, and aminoguanidine hydrochloride). Although these are all fundamentally important compounds, the majority do not have <sup>13</sup> C chemical shift tensors reported in the literature. The chemical shift tensors are obtained from <sup>1</sup> H→<sup>13</sup> C cross-polarization magic-angle spinning (CP/MAS) experiments that were conducted at a high field of 18.8 T to suppress the effects of <sup>14</sup> N-<sup>13</sup> C residual dipolar coupling. Quantum chemical calculations using density functional theory are used to obtain the <sup>13</sup> C magnetic shielding tensors for these compounds. The best agreement with experiment arises from calculations using the hybrid functional PBE0 or the double-hybrid functional PBE0-DH, along with the triple-zeta basis sets TZ2P or pc-3, respectively, and intermolecular effects modeled using large clusters of molecules with electrostatic embedding through the COSMO approach. These measurements are part of an ongoing effort to expand the catalog of accurate <sup>13</sup> C chemical shift tensor measurements, with the aim of creating a database that may be useful for benchmarking the accuracy of quantum chemical calculations, developing nuclear magnetic resonance (NMR) crystallography protocols, or aiding in applications involving machine learning or data mining. This work was conducted at the National High Magnetic Field Laboratory as part of a 2-week school for introducing undergraduate students to practical laboratory experience that will prepare them for scientific careers or postgraduate studies. Chemistry ; Chemical shift ; Density functional theory ; Computational chemistry ; Tensor (intrinsic definition) ; Chemical physics ; Physical chemistry Advanced NMR Techniques and Applications ; Inorganic Fluorides and Related Compounds ; Electron Spin Resonance Studies
Authors: Florent Purnode | François Henrotte | Gilles Louppe | Christophe Geuzaine
Source: International Journal of Numerical Modelling Electronic Networks Devices and Fields (article, 2024-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network‐based simulation of fields and losses in electrical machines with ferromagnetic laminated cores Abstract Due to the distribution of eddy currents inside ferromagnetic laminations, the accurate modeling of magnetic fields and losses in the laminated cores of electrical machines requires resolving individual laminations with a fine 3D discretization. This yields finite element models so huge and costly that they are unusable in daily industrial R&amp;D. In consequence, hysteresis and eddy currents in laminations are often simply disregarded in the modeling: the laminated core is assumed to be made of a reversible (non lossy) saturable material, and magnetic losses are evaluated a posteriori, by means of Steinmetz–Bertotti like empirical formulas. However, in a context where industry is struggling to minutely assess the impact of magnetic losses on their devices, this simplified approach is more and more regarded as inaccurate and unsatisfactory. This article proposes a solution to this issue, based on homogenization and on detailed mesoscopic simulations of eddy currents and hysteresis inside the laminations. The proposed approach results in a close‐to‐conventional 2D magnetic vector potential finite element model, but equipped with an irreversible parametric material law to represent the ferromagnetic stack. In each finite element, the parameters of the law are obtained from a neural network trained to best fit the detailed mesoscopic simulations of the laminations subjected to the same local magnetic field. This way, all aspects of the irreversible ferromagnetic response are appropriately accounted for in the finite element simulation, but at a computational cost drastically reduced with regard to a brute force 3D calculation, and comparable to that of conventional 2D finite element simulations. Eddy current ; Finite element method ; Homogenization (climate) ; Mesoscopic physics ; Ferromagnetism ; Magnetic field ; Mechanics ; Discretization ; Transformer ; Parametric statistics ; Magnetic hysteresis ; Materials science ; Computer science ; Physics ; Magnetization ; Mathematical analysis ; Condensed matter physics ; Engineering ; Mathematics ; Structural engineering ; Electrical engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electromagnetic Simulation and Numerical Methods
Authors: Allison B. Esselman | Felipe Moser | Léonore Tideman | Lukasz G. Migas | Katerina Djambazova | Madeline E. Colley | Ellie L. Pingry | Nathan Heath Patterson | Melissa A. Farrow | Haichun Yang | Agnes B. Fogo | Mark de Caestecker | Raf Van de Plas | Jeffrey M. Spraggins
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-23)
Field: Chemistry — Spectroscopy

Revealing <i>In Situ</i> Molecular Profiles of Glomerular Cell Types and Substructures with Integrated Imaging Mass Spectrometry and Multiplexed Immunofluorescence Microscopy Abstract Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease (DKD). However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Herein, we demonstrate an integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 µm pixel size) was used to determine lipid distributions within human glomeruli, revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS without sacrificing signal quality from either modality. A combination of machine-learning approaches was assembled, enabling cell-type segmentation and identification based on MxIF data followed by the mining of cell type or cluster-associated MALDI IMS signatures using classification models and interpretable machine learning. This allowed the automated discovery of spatially specific biomarker candidates for glomerular substructures and cell types. Overall, the work presented here establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments and provides a framework that applies to other kidney tissue features and organ systems. Mass spectrometry imaging ; MALDI imaging ; Cell type ; Podocyte ; In situ ; Biomarker discovery ; Computational biology ; Chemistry ; Mass spectrometry ; Molecular imaging ; Cell ; Pathology ; Proteomics ; Matrix-assisted laser desorption/ionization ; Biology ; Kidney ; Biochemistry ; Medicine ; In vivo Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies