jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeremy A. McCulloch | Ellen Kuhl
Source: Acta Biomaterialia (article, 2024-10-03)
Field: Materials Science — Polymers and Plastics

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics Textile ; Signature (topology) ; Materials science ; 3d printed ; Composite material ; Biomedical engineering ; Engineering ; Geometry Textile materials and evaluations ; Optical measurement and interference techniques ; Mechanical Behavior of Composites
Authors: Nikhil K. Barua | E. L. Hall | Yifei Cheng | Anton O. Oliynyk | Holger Kleinke
Source: Chemistry of Materials (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset Machine learning (ML), a subdiscipline of artificial intelligence studies, has gained importance in predicting or suggesting efficient thermoelectric materials. Previous ML studies have used different literature sources or density functional theory calculations as input. In this work, we develop a ML pipeline trained with multivariable inputs on a massive public dataset of ∼200,000 data utilizing a high-performance computing cluster to predict the thermal conductivity (κ) using four test sets: three publicly available datasets and a dataset built using previously published data from our own group. By taking advantage of this massive dataset, our model presents an opportunity to further expand the understanding of the selection of features with various thermoelectric materials. Among the several supervised ML models implemented, the eXtreme Gradient Boosting algorithm (XGBoost) turned out to be the best method during the 5-fold cross-validation method, with their averaged evaluation coefficients of R2 = 0.96, root mean squared error (RMSE) = 0.38 W m−1K−1, and mean absolute error (MAE) = 0.23 W m−1K−1. Additionally, with the aid of feature selection and importance analysis, useful chemical features were chosen that ultimately led to reasonably good accuracy in the series of test sets measured as per the evaluation coefficients of R2, RMSE, and MAE, with values ranging from 0.72 to 0.89, 0.52 to 1.08, and 0.40 to 0.66 W m−1K−1, respectively. Checking the worst outliers led to the discovery of some errors in the literature. Postmodel prediction, the SHapley Additive exPlanations (SHAP) algorithm was implemented on the XGBoost model to analyze the features that were the key drivers for the model’s decisions. Overall, the developed interpretable methodology produces the prediction of κ of a large variety of materials through the influence of chemical and physical property features. The conclusions drawn apply to the research and applications of thermoelectric and heat insulation materials. Thermal conductivity ; Artificial intelligence ; Machine learning ; Computer science ; Data mining ; Materials science ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Neural Networks and Applications
Authors: Ricardo Valencia Albornoz | Diego A. Oyarzún | Karl Burgess
Source: Computational and Structural Biotechnology Journal (article, 2024-02-15)
Field: Chemistry — Spectroscopy

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160 % yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies. Surfactin ; Yield (engineering) ; Metabolomics ; Throughput ; Synthetic biology ; Biochemical engineering ; Systems biology ; Computational biology ; Active learning (machine learning) ; Computer science ; Chemistry ; Bacillus subtilis ; Machine learning ; Biological system ; Biology ; Bacteria ; Engineering ; Chromatography ; Materials science Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Bacterial Genetics and Biotechnology
Authors: Zhitong Yao | Jiayao Tong | Jingjing Jiang | Ahmed Mohamed Abdel Sattar | Jean Constantino Gomes da Silva | Sachin Kumar | Xiaobo Wang | Mohamed Salama Abd-Elhady | Jie Liu | Meiqing Jin | Obid Tursunov | Wei Qi
Source: Chemical Engineering Science (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Pyrolysis-gasification conversion of waste pharmaceutical blisters: Thermo-kinetic and thermodynamic study, fuel gas analysis and machine learning modeling Blisters ; Pyrolysis ; Waste management ; Kinetic energy ; Process engineering ; Thermodynamics ; Chemical engineering ; Materials science ; Chemistry ; Engineering ; Physics ; Composite material Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Recycling and Waste Management Techniques
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: Kidney International (article, 2024-11-20)
Field: Chemistry — Spectroscopy

In situ molecular profiles of glomerular cells by integrated imaging mass spectrometry and multiplexed immunofluorescence microscopy 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. 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. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures 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. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems. In situ ; Immunofluorescence ; Mass spectrometry imaging ; Microscopy ; Mass spectrometry ; Pathology ; Chemistry ; Materials science ; Medicine ; Chromatography ; Antibody ; Immunology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: C. Faller | Merzuk Kaltak | Georg Kresse
Source: The Journal of Chemical Physics (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Density-based long-range electrostatic descriptors for machine learning force fields This study presents a long-range descriptor for machine learning force fields that maintains translational and rotational symmetry, similar to short-range descriptors while being able to incorporate long-range electrostatic interactions. The proposed descriptor is based on an atomic density representation and is structurally similar to classical short-range atom-centered descriptors, making it straightforward to integrate into machine learning schemes. The effectiveness of our model is demonstrated through comparative analysis with the long-distance equivariant (LODE) [Grisafi and Ceriotti, J. Chem. Phys. 151, 204105 (2019)] descriptor. In a toy model with purely electrostatic interactions, our model achieves errors below 0.1%, worse than LODE but still very good. For real materials, we perform tests for liquid NaCl, rock salt NaCl, and solid zirconia. For NaCl, the present descriptors improve on short-range density descriptors, reducing errors by a factor of two to three and coming close to message-passing networks. However, for solid zirconia, no improvements are observed with the present approach, while message-passing networks reduce the error by almost a factor of two to three. Possible shortcomings of the present model are briefly discussed. Lode ; Range (aeronautics) ; Representation (politics) ; Computer science ; Equivariant map ; Electrostatics ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Mathematics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abuchi Elebo | Sani Uba | Patricia Adamma Ekwumemgbo | V.O. Ajibola
Source: Current Research in Green and Sustainable Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unravelling adsorption potential of expired clindamycin as corrosion inhibitor at mild Steel/HCl interface via response surface methodology, artificial neural network, electrochemical, and computational strategies Corrosion has produced unprecedented disintegration of metals, constituting an imminent danger to mankind and triggering catastrophic global economic losses. The effectiveness of expired clindamycin (ECLI) as a low-cost corrosion control agent for mild steel was investigated utilising response surface methodology (RSM), artificial neural network (ANN), potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), quantum chemical computation (QCC), and molecular dynamic simulation (MDS) studies in conjunction with thermometric and gasometric protocols at different HCl concentrations. The RSM model demonstrated an outstanding level of accuracy in predicting the mild steel corrosion inhibition efficiency (IE), the volume of hydrogen gas (VHG), reaction number (RN), and corrosion rate (CR). The model was significantly influenced by the operational parameters that were investigated, such as temperature (299–333 K), ECLI concentrations (100–500 mg/L), immersion time (1–6 h), and acid concentration (0.5–2.5 M). It was observed that as ECLI concentration increases, the VHG, RN, and CR decreased per time as well as % IE increased. The thermometric, gasometric, PDP, and EIS results showed percentage inhibition efficiency of 69.10, 69.49, 83.17, and 77.87 %, respectively. PDP revealed that ECLI operates as a mixed type of inhibitor, and EIS indicated that the inhibition process involves charge transfer. The Langmuir isotherm suits better and accurately describes the ECLI adsorption process on mild steel. The electron transfer propensity of the ECLI on the metal surface is measured by QCC using the DFT approach. MDS was implemented to establish the optimal adsorption orientation between ECLI and Fe (110). The inspection of surface morphology by SEM displayed the formation of a blanket-like layer on the steel by ECLI. To validate the experimental results, RSM and ANN prediction models were utilised, which were evaluated using a normal plot of residual, predicted versus actual, and residual versus run, and were found to be effective modelling tools. This study illustrates that ECLI can be utilised as a potent and affordable mild steel inhibitor, even at high acid concentrations. Response surface methodology ; Artificial neural network ; Adsorption ; Corrosion inhibitor ; Electrochemistry ; Corrosion ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Chromatography ; Metallurgy ; Organic chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ahmed S. Haiba | A. Eliwa Gad
Source: Scientific Reports (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Artificial neural network analysis for classification of defected high voltage ceramic insulators Partial discharge (PD) could lead to the formation of small arcs or sparks within the insulating material, which can cause damage and degradation to the insulator over time. In ceramic insulators, there are several factors that can cause PD including manufacturing defects, aging, and exposure to environmental conditions such as moisture and temperature extremes. As a result, detecting and monitoring PD in ceramic insulators is important for ensuring the reliability and safety of electrical systems that rely on these insulators. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques is performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. Artificial neural network (ANN) has been used to build an intelligent classifier for easily and accurately classification of defective insulators. The overall recognition rate of the classifier was obtained at 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification. The outcomes of ANN were verified by SVM and KNN algorithms. Ceramic ; Insulator (electricity) ; Classifier (UML) ; Artificial neural network ; Computer science ; Pattern recognition (psychology) ; Support vector machine ; Wavelet transform ; Artificial intelligence ; Materials science ; Partial discharge ; Wavelet ; Voltage ; Optoelectronics ; Composite material ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Geophysical Methods and Applications
Authors: O. Ceballos-Sánchez | Diego E. Navarro-López | Jorge L. Mejía-Méndez | Gildardo Sanchez‐Ante | Vicente Rodríguez‐González | Angélica Lizeth Sánchez-López | A. Sánchez-Martínez | S.M. Durón-Torres | Karla Juárez‐Moreno | Naveen Tiwari | Edgar R. López-Mena
Source: Biomaterials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing antioxidant properties of CeO<sub>2</sub> nanoparticles with Nd<sup>3+</sup> doping: structural, biological, and machine learning insights The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO<sub>2</sub> nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO<sub>2</sub> nanoparticles. Incorporating Nd<sup>3+</sup> induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO<sub>2</sub> was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce<sup>3+</sup>/Ce<sup>4+</sup> ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO<sub>2</sub>. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO<sub>2</sub> nanoparticles. This study presents the first exploration of the influence of Nd<sup>3+</sup> on the structural, optical, and biological attributes of CeO<sub>2</sub>, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials. Doping ; Nanoparticle ; Antioxidant ; Chemistry ; Nanotechnology ; Materials science ; Biochemistry ; Optoelectronics Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Jianhua Liang | Jiaming Guo | Hongling Xia | Chengying Ma | Xiaoyan Qiao
Source: Food Chemistry (article, 2024-10-09)
Field: Chemistry — Analytical Chemistry

A black tea quality testing method for scale production using CV and NIRS with TCN for spectral feature extraction Black tea ; Scale (ratio) ; Extraction (chemistry) ; Quality (philosophy) ; Chemistry ; Chromatography ; Food science ; Physics ; Geography ; Cartography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Ahmed Abdel-Rahim | Özgün Yücel
Source: Fuel (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

A machine learning based regression methods to predicting syngas composition for plasma gasification system Syngas ; Gas composition ; Regression ; Composition (language) ; Process engineering ; Regression analysis ; Plasma ; Computer science ; Machine learning ; Chemistry ; Mathematics ; Statistics ; Thermodynamics ; Engineering ; Catalysis ; Physics ; Organic chemistry ; Nuclear physics Catalytic Processes in Materials Science ; Iron and Steelmaking Processes ; Catalysis and Oxidation Reactions
Authors: Arpita Priyadarsini Dikshit | Dibyaranjan Das | Ritu Roumya Samal | Kajal Parashar | Chinmaya Mishra | S.K.S. Parashar
Source: Journal of Alloys and Compounds (article, 2024-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of (Ba1−xCax)(Ti0.9Sn0.1)O3 ceramics in X-band using Machine Learning Electromagnetic shielding ; EMI ; Electromagnetic interference ; Ceramic ; Materials science ; Pellets ; Composite material ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Microwave Dielectric Ceramics Synthesis ; Advanced Antenna and Metasurface Technologies
Authors: Yang Wang | Liqiang He | Meijing Wang | Jiongpeng Yuan | Siwei Wu | Xiaojing Li | Tong Lin | Zihui Huang | Andi Li | Yuhang Yang | Xujie Liu | Yan He
Source: International Journal of Pharmaceutics (article, 2024-04-15)
Field: Chemistry — Inorganic Chemistry

The drug loading capacity prediction and cytotoxicity analysis of metal–organic frameworks using stacking algorithms of machine learning Random forest ; Cytotoxicity ; Stacking ; Computer science ; Machine learning ; Artificial intelligence ; Drug ; Algorithm ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Shikha Saini | Joakim Halldin Stenlid | Shyam Deo | Philipp N. Pleßow | Frank Abild‐Pedersen
Source: ACS Catalysis (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts The study of multimetallic alloys and the multitude of possible surface compositions have sparked a tremendous interest in engineering low-cost materials with high activity and selectivity in heterogeneous catalysis. Multimetallic systems provide complementary functionalities and an unprecedented tunability when designing catalyst formulations. However, due to their immense structural and compositional complexity, the investigation and identification of an optimal catalyst is a tedious and time-consuming process, both experimentally and theoretically. Therefore, theoretical design principles are highly desirable to accelerate the screening of catalyst structures across the vast compositional space. In this paper, we introduce a simple and general model for predicting the site stability of multimetallic surfaces and nanoparticles, which is based on physical principles. The model requires only a small set of density functional theory (DFT) calculations of metal atom binding energies on monometallic and dilute alloy surface slabs to optimize the parameters in the simple model. The resulting model allows for the quantification of the stability of any particular atom site in any conceivable chemical environment across a wide range of morphologies, sizes, and arrangements by interpolating the derived parameters from a monometallic system to a completely diluted alloyed system. Herein, we demonstrate the robustness of the model across an extensive data set of transition metal alloy surfaces and 147-atoms cuboctahedral nanoparticles (NPs) composed of IrRhRu and PtPdRu. Our approach yields mean absolute errors of ≈0.15 (IrRhRu), 0.20 (PtPdRu), 0.19 (IrRhRu NP), and 0.26 (PtPdRu NP) eV relative to site binding energies calculated using DFT. Catalysis ; Density functional theory ; Robustness (evolution) ; Materials science ; Atom (system on chip) ; Nanoparticle ; Selectivity ; Nanotechnology ; Heterogeneous catalysis ; Metal ; Chemical physics ; Computational chemistry ; Chemistry ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies