jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongzhen Zhang | Zhenfa Yang | Yina Wang | Xinting Ge | Jianfeng Zhang | Hang Xiao
Source: Frontiers in Chemistry (article, 2024-06-03)
Field: Chemistry — Analytical Chemistry

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub><mml:mrow><mml:mi>C</mml:mi> <mml:mi>a</mml:mi> <mml:mi>C</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mn>3</mml:mn></mml:msub> </mml:mrow> </mml:math> ), silicon dioxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub><mml:mrow><mml:mi>S</mml:mi> <mml:mi>i</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> ), aluminum oxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub><mml:mrow><mml:mi>A</mml:mi> <mml:mi>l</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msub> <mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:mrow> </mml:math> ), and ferric oxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub><mml:mrow><mml:mi>F</mml:mi> <mml:mi>e</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msub> <mml:msub><mml:mi>O</mml:mi> <mml:mn>3</mml:mn></mml:msub> </mml:mrow> </mml:math> ) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>R</mml:mi> <mml:mi>p</mml:mi></mml:msub> </mml:mrow> </mml:math> and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing. Algorithm ; Artificial intelligence ; Materials science ; Computer science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Devyani Thapliyal | Rahul Shrivastava | George D. Verros | Sarojini Verma | Raj Kumar Arya | Pramita Sen | Shiv Charan Prajapati | Chahat | Ajay Gupta
Source: Processes (article, 2024-01-25)
Field: Materials Science — Polymers and Plastics

Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-Xylene Coatings Using Artificial Neural Network The drying process of polymeric coatings, particularly in the presence of surfactants, poses a complex challenge due to its intricate dynamics involving simultaneous heat and mass transfer. This study addresses the inherent complexity by employing Artificial Neural Networks (ANNs) to model the surfactant-enhanced drying of poly(styrene)-p-xylene coatings. A substantial dataset of 16,258 experimentally obtained samples forms the basis for training the ANN model, showcasing the suitability of this approach when ample training data is available. The chosen single-layer feed-forward network with backpropagation adeptly captures the non-linear relationships within the drying data, providing a predictive tool with exceptional accuracy. Our results demonstrate that the developed ANN model achieves a precision level exceeding 99% in predicting coating weight loss for specified input values of time, surfactant amount, and initial coating thickness. The model’s robust generalization capability eliminates the need for additional experiments, offering reliable predictions for both familiar and novel conditions. Comparative analysis reveals the superiority of the ANN over the regression tree, emphasizing its efficacy in handling the intricate dynamics of polymeric coating drying processes. In conclusion, this study contributes a valuable tool for optimizing polymeric coating processes, reducing production defects, and enhancing overall manufacturing quality and cost-effectiveness. Coating ; Artificial neural network ; Polystyrene ; Biological system ; Materials science ; Process (computing) ; Computer science ; Mass transfer ; Process engineering ; Pulmonary surfactant ; Generalization ; Styrene ; Backpropagation ; Artificial intelligence ; Chemical engineering ; Nanotechnology ; Polymer ; Chromatography ; Copolymer ; Chemistry ; Mathematics ; Composite material ; Engineering Textile materials and evaluations ; Injection Molding Process and Properties ; Industrial Vision Systems and Defect Detection
Authors: Zhenqi Shi | Yuyan Yi | Eddie Madrigal | Frank Hrovat | Kelly Zhang | Jessica Lin
Source: Journal of Chromatography A (article, 2024-12-31)
Field: Chemistry — Spectroscopy

A generalizable methodology for predicting retention time of small molecule pharmaceutical compounds across reversed-phase HPLC columns Chemistry ; Chromatography ; High-performance liquid chromatography ; Retention time ; Reversed-phase chromatography ; Phase (matter) ; Stationary phase ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Hamza Faisal Ghauri | Reza Tafreshi | Bilal Mansoor
Source: Journal of Materials Science Materials in Engineering (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Toward automated microstructure characterization of stainless steels through machine learning-based analysis of replication micrographs Abstract Machine learning-driven automated replication micrographs analysis makes possible rapid and unbiased damage assessment of in-service steel components. Although micrographs captured by scanning electron microscopy (SEM) have been analyzed at depth using machine learning, there is no literature available on the technique being attempted on optical replication micrographs. This paper presents a machine-learning approach to segment and quantify carbide precipitates in thermally exposed HP40-Nb stainless-steel microstructures from batches of low-resolution optical images obtained by replication metallography. A dataset of nine micrographs was used to develop a random forest classification model to segment precipitates within the matrix (intragranular) and at grain boundaries (intergranular). The micrographs were preprocessed using background subtraction, denoising, and sharpening to improve quality. The method achieves high segmentation accuracy (91% intergranular, 97% intragranular) compared to human expert classification. Furthermore, segmented micrographs were quantified to obtain carbide size, shape, and density distribution. The correlations in the quantified data aligned with expected carbide evolution mechanisms. Results from this study are promising but necessitate validation of the method on a larger dataset representative of evolution of thermal degradation in steel, given that characterization of the evolution of microstructure components, such as precipitates, applies to broad applications across diverse alloy systems, particularly in extreme service. Replication (statistics) ; Microstructure ; Characterization (materials science) ; Materials science ; Micrograph ; Artificial intelligence ; Metallurgy ; Computer science ; Composite material ; Nanotechnology ; Scanning electron microscope ; Mathematics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Chi-Huan Tung | Meng-Zhe Chen | Hsin‐Lung Chen | Guan-Rong Huang | Lionel Porcar | Ming‐Ching Chang | Jan‐Michael Y. Carrillo | Yangyang Wang | Bobby G. Sumpter | Yuya Shinohara | Changwoo Do | Wei-Ren Chen
Source: Journal of Applied Crystallography (article, 2024-06-18)
Field: Chemistry — Physical and Theoretical Chemistry

Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning An innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond. Deep learning ; Scattering ; Inversion (geology) ; Computer science ; Electrostatics ; Electrostatic interaction ; Soft matter ; Colloid ; Artificial intelligence ; Work (physics) ; Algorithm ; Chemistry ; Physics ; Chemical physics ; Optics ; Quantum mechanics ; Geology Electrostatics and Colloid Interactions ; Electrowetting and Microfluidic Technologies ; Material Dynamics and Properties
Authors: Héctor Lobato | Carlos Cernuda | Kepa Zulueta | Aitor Arriaga | Jon M. Matxain | Aizeti Burgoa
Source: International Journal of Solids and Structures (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Prediction of long-term creep modulus of thermoplastics using brief tests and interpretable machine learning Creep ; Machine learning ; Linear regression ; Term (time) ; Computer science ; Predictive modelling ; Property (philosophy) ; Work (physics) ; Modulus ; Regression analysis ; Artificial intelligence ; Materials science ; Mechanical engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Environmental Impact and Sustainability
Authors: Zhiyong Zhang | Mingtao Ye | Yisu Ge | Mohamed Gamal Elsehrawy | Xiaotian Pan | Nermeen Abdullah | Samia Elattar | Ehab El Sayed Massoud | Suxian Lin
Source: Environmental Research (article, 2024-08-23)
Field: Materials Science — Biomaterials

Eco-friendly nanotechnology in rheumatoid arthritis: ANFIS-XGBoost enhanced layered nanomaterials Nanomaterials ; Rheumatoid arthritis ; Nanotechnology ; Environmentally friendly ; Materials science ; Medicine ; Biology ; Immunology ; Ecology Nanoparticle-Based Drug Delivery ; Rheumatoid Arthritis Research and Therapies ; Nanoparticles: synthesis and applications
Authors: Ludovico Musenich | Alessandro Stagni | Lucio Derin | Flavia Libonati
Source: ACS Materials Letters (article, 2024-05-07)
Field: Materials Science — Biomaterials

Tunable Energy Absorption in 3D-Printed Data-Driven Diatom-Inspired Architected Materials Boosted by additive manufacturing, architected materials have opened new opportunities to extend the performance of engineering materials. Yet, their development is held back by the intense efforts required to understand their complex property–structure–process–performance relationship. Therefore, data-driven biomimetic approaches are becoming increasingly popular to unveil such relationships. Here, we mimic the functionally graded structures found in Coscinodiscus sp. diatom to understand the role of their shapes and define new guidelines for the design of novel architected honeycombs with tunable mechanical properties. Finite element simulations, validated on the outcome of a testing campaign performed on three-dimensional (3D)-printed elastomeric samples, are used to build a dataset for machine learning algorithm training. Different machine learning techniques are used to link the geometric features of the designed biomimetic structures to their energy absorption properties and, in particular, to the specific absorbed energy divided by the peak force, here used as the performance index. The proposed approach leads to a novel design, which features a performance increase of 250%, with respect to conventional honeycombs. Absorption (acoustics) ; Process (computing) ; Computer science ; Materials science ; Finite element method ; Property (philosophy) ; Engineering design process ; Mechanical engineering ; Diatom ; Energy (signal processing) ; Elastomer ; 3d printed ; Artificial intelligence ; Nanotechnology ; Composite material ; Engineering ; Structural engineering ; Manufacturing engineering ; Geology ; Mathematics Diatoms and Algae Research ; Cephalopods and Marine Biology ; Calcium Carbonate Crystallization and Inhibition
Authors: Tingting Wang | Takahisa Yamato | Wataru Sugiura
Source: The Journal of Physical Chemistry B (article, 2024-08-28)
Field: Chemistry — Physical and Theoretical Chemistry

Thermal Energy Transport through Nonbonded Native Contacts in Protein Within the protein interior, where we observe various types of interactions, nonuniform flow of thermal energy occurs along the polypeptide chain and through nonbonded native contacts, leading to inhomogeneous transport efficiencies from one site to another. The folded native protein serves not merely as thermal transfer medium but, more importantly, as sophisticated molecular nanomachines in cells. Therefore, we are particularly interested in what sort of "communication" is mediated through native contacts in the folded proteins and how such features are quantitatively depicted in terms of local transport coefficients of heat currents. To address the issue, we introduced a concept of inter-residue thermal conductivity and characterized the nonuniform thermal transport properties of a small globular protein, HP36, using equilibrium molecular dynamics simulation and the Green-Kubo formula. We observed that the thermal transport of the protein was dominated by that along the polypeptide chain, while the local thermal conductivity of nonbonded native contacts decreased in the order of H-bonding > π-stacking > electrostatic > hydrophobic contacts. Furthermore, we applied machine learning techniques to analyze the molecular mechanism of protein thermal transport. As a result, the contact distance, variance in contact distance, and H-bonding occurrence probability during MD simulations are found to be the top three important determinants for predicting local thermal transport coefficients. Chemical physics ; Globular protein ; Molecular dynamics ; Thermal ; Stacking ; Thermal conductivity ; Energy transport ; Thermal energy ; Nanopore ; Chemistry ; Statistical physics ; Materials science ; Nanotechnology ; Thermodynamics ; Crystallography ; Physics ; Computational chemistry ; Engineering physics thermodynamics and calorimetric analyses ; Protein Structure and Dynamics ; Molecular Junctions and Nanostructures
Authors: Tagir Akhmetshin | Dmitry Zankov | Philippe Gantzer | Dmitry Babadeev | Anna Pinigina | Timur Madzhidov | Alexandre Varnek
Source: Journal of Chemical Information and Modeling (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

SynPlanner: An End-to-End Tool for Synthesis Planning SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner. Retrosynthetic analysis ; Pipeline (software) ; Computer science ; Standardization ; Interface (matter) ; Artificial intelligence ; Data mining ; Operating system ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Matthew P. Rivera | Gianmarco Terrones | Tae Hoon Lee | Zachary P. Smith | Heather J. Kulik
Source: ACS Applied Materials & Interfaces (article, 2024-11-19)
Field: Chemistry — Inorganic Chemistry

Data-Driven Screening and Discovery of Metal–Organic Frameworks as C <sub>2</sub> Adsorbents from over 900 Experimental Isotherms The separation of ethylene from ethane accounts for almost 100 million tons of CO<sub>2</sub> emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains. Metal-organic frameworks (MOFs) are well-suited for adsorption separation due to their high surface areas and tunable chemical properties. Exploring all possible MOFs is a daunting experimental challenge, motivating in silico screening with machine learning models. We present a database of 948 experimentally measured pure-component C<sub>2</sub> isotherms from 192 MOFs gathered from the literature and use it to train machine learning models to predict MOF ethane and ethylene uptake across a range of temperature and pressure conditions. The models have high accuracy in interpolative tasks (mean absolute error ∼0.05 mmol/g) when trained on only 20% of available data. Performance on unseen structures was also reasonably accurate with a mean absolute error (MAE) ∼0.7 mmol/g. We apply the models to screen the CoRE MOF2019 ASR database and identify the most promising candidates. Several MOFs containing lanthanide metals were predicted to have high ethane selectivity, suggesting that this class of MOFs may merit further investigation. Feature importance analysis suggests that both optimizing MOF secondary building unit chemistry and the process conditions at which the sorbent will operate are critical for enabling ethane-selective separation. We synthesize a MOF predicted to exhibit high ethane selectivity and experimentally validate qualitative agreement with model predictions, highlighting the utility of both the data set and model in discovering unexplored C<sub>2</sub> adsorbents. Materials science ; Metal-organic framework ; Adsorption ; Nanotechnology ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Qiuyi Wen | Wenlong Wei | Yun Li | Dan Chen | Jianqing Zhang | Zhenwei Li | De‐an Guo
Source: Sensors (article, 2024-12-25)
Field: Chemistry — Analytical Chemistry

Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the <i>Curcuma</i> species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of <i>Curcuma</i> species and thus provides a technique for rapid quality evaluation of affinis species. Curcuma ; Rhizome ; Support vector machine ; Smoothing ; Medicinal plants ; Identification (biology) ; Artificial intelligence ; Traditional medicine ; Computer science ; Biological system ; Chemistry ; Algorithm ; Computational biology ; Mathematics ; Biology ; Botany ; Medicine ; Statistics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Trenton J. Wolter | Evangelos Smith | Roberto Schimmenti | Jakob Schiøtz | Karsten W. Jacobsen | Manos Mavrikakis
Source: The Journal of Physical Chemistry C (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Moiré Patterns in Pt Overlayers on Gold: A Graph Neural Network Interatomic Potential Study Overlayer structures in bimetallic catalysts are relevant to a variety of catalytic reactions, particularly in electrocatalysis for fuel cell applications. Previous computational studies largely consider these overlayer structures to be those of a pseudomorphic overlayer, where there is a 1:1 atomic ratio between the overlayer and the support metal. Our previous work based on density functional theory (DFT) has shown that there exist nonstoichiometric overlayer structures that are more stable than the stoichiometric ones. In this work, we developed a graph neural network interatomic potential (GNN-IP) to analyze structures formed in Pt overlayers on Au(111). The GNN-IP was used to explore properties of nonstoichiometric overlayers at length scales that are prohibitively expensive to pursue with planewave DFT calculations. In particular, we examined large Pt islands on top of a 48 × 48 Au(111) unit cell to explore the influence of the rotational angle (α) between the Pt overlayer and support Au(111) on both the stability of and the preferred atomic density in the overlayer. Island structures with smaller rotational angles between the Pt overlayer and Au(111) tend to be more stable. Further, smaller rotational angles tend to result in lower atomic density in the Pt overlayer. Moiré pattern ; Interatomic potential ; Materials science ; Artificial neural network ; Graph ; Computer science ; Physics ; Molecular dynamics ; Optics ; Chemistry ; Artificial intelligence ; Computational chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Applications