jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hakan Demir | İlknur Eruçar
Source: Industrial & Engineering Chemistry Research (article, 2024-07-03)
Field: Chemistry — Inorganic Chemistry

Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF<sub>4</sub> from CH<sub>4</sub> and N<sub>2</sub> One of the most crucial problems of the modern world is to combat the emission of greenhouse gases as these lead to extreme weather conditions, droughts, etc., and endanger the global ecosystem. Among the various greenhouse gases, the removal of carbon tetrafluoride, CF4, from the atmosphere is of particular interest as its global warming potential is several orders of magnitude higher than that of many other greenhouse gases, including carbon dioxide, CO2. The separation of CF4 from methane, CH4, and nitrogen, N2, which are present in the atmosphere alongside CF4, can be efficiently accomplished by adsorption- or membrane-based processes. Metal–organic frameworks (MOFs) offer a variety of possibilities for gas separation due to their large surface areas and tailored chemical and physical structures. In this work, the separation of CF4/CH4, CF4/N2, and CF4/CH4/N2 in a subset of MOFs, double-linker MOFs (DL-MOFs), is investigated by using molecular simulations and machine learning (ML) methods. Among ∼500 MOFs, the best MOFs for adsorption-based CF4/CH4, CF4/N2, and CF4/CH4/N2 separation are determined by considering the adsorption selectivity, working capacity, and regenerability. The ML models developed for adsorption-based CF4/CH4, CF4/N2, and CF4/CH4/N2 separation have revealed that only one or two features (Henry’s constant and/or surface area) are sufficient to establish models that can make accurate predictions of gas uptake. The membrane-based CF4/CH4 and CF4/N2 separation performances of DL-MOFs have also been studied, and it was found that inverse selective MOFs preferring CH4 or N2 over CF4 can exist. Overall, this study will open avenues for the design and development of novel MOF adsorbents and membranes for the separation of CF4 from CH4 and N2 by providing atomistic insights into the adsorption and diffusion properties of DL-MOFs. Adsorption ; Gas separation ; Greenhouse gas ; Methane ; Atmosphere (unit) ; Air separation ; Carbon fibers ; Carbon dioxide ; Metal-organic framework ; Chemistry ; Membrane ; Chemical engineering ; Materials science ; Meteorology ; Organic chemistry ; Physics ; Oxygen ; Geology Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon Dioxide Capture Technologies
Authors: Xiaonan Du | Youwen Chen | Qing Wang | Huiying Yang | Yong Lu | Xianfu Wu
Source: Phytochemistry Reviews (article, 2024-10-23)
Field: Chemistry — Analytical Chemistry

Exploring AI-enhanced NMR dereplication analysis for complex mixtures and its potential use in adulterant detection Adulterant ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Metabolomics and Mass Spectrometry Studies
Authors: Arpit B. Mendhe | Bharati Panigrahy | Himanshu Sekhar Panda
Source: Electrochimica Acta (article, 2024-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ensemble machine learning for predictive accuracy and experimental corroboration on transition metal-based electrodes for supercapacitor Supercapacitor ; Electrode ; Machine learning ; Transition metal ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Electrochemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrocatalysts for Energy Conversion
Authors: Matthew S. Dargusch | Yuan Wang | Chuhan Sha | Nan Yang | Xingrui Chen | Jeffrey Venezuela | Joseph Otte | Sean F. Johnston | Cora Lau | Rachel Allavena | Karine Mardon | Ingrid McCaroll | Julie M. Cairney
Source: Acta Biomaterialia (article, 2024-11-07)
Field: Materials Science — Biomaterials

Insights into heat treatments of biodegradable Mg-Y-Nd-Zr alloys in clinical settings: Unveiling roles of β' and β1 nanophases and latent in vivo hydrogen evolution Heat treatment serves as a viable strategy to effectively mitigate the intense corrosion of biodegradable WE43 alloys. However, limited comprehension of the passivation mechanisms underlying heat treatment and the dilemma to quantitatively examine the evolution of hydrogen gas in vivo introduce uncertainties in designing heat treatments for developing clinically applicable WE43. This work aims to advance this knowledge by applying cutting-edge atom probe tomography to provide atomic-scale insights into the passivation roles of rare earth (RE)-rich β<sub>1</sub> (Mg<sub>3</sub>(Y, Nd)) and β' (Mg<sub>12</sub>NdY) nanophases induced by T6 heat treatment at 250 °C, and employing machine learning-based image analysis techniques to quantitatively unveil WE43's in vivo gas evolution during a 12-week implantation. It was found that nanosized β<sub>1</sub> and β' phases can effectively improve WE43's corrosion resistance by inducing an accelerated passivation effect on the surface and confining the distribution of hydrogen ions in the matrix. Female rats presented slightly higher corrosion rates than male rats in weeks 1 and 4 but lower hydrogen gas volumes in vivo, while male rats possessed a superior ability to metabolise hydrogen gas in vivo. Notably, latent gas evolution against the corrosion rates was found which peaked at week 4 and subsided at week 12 despite the gradually decreased corrosion rates from week 1 to 12. This study offers insights for engineering heat treatments to develop clinically applicable WE43 with acceptable corrosion rates and in vivo gas generation at various implantation stages. STATEMENT OF SIGNIFICANCE: The study aimed to reveal the role of β<sub>1</sub> and β' nanophases on the good corrosion resistance of WE43. The influence of these nanophases on WE43's corrosion performance has not been totally understood. Similarly, the understanding of hydrogen gas evolution as it relates to the magnesium implant's corrosion rate lacks clarity. Atom probe tomography (APT) indicates β<sub>1</sub> and β' nanophases trap hydrogen, removing H<sub>2</sub> from the lattice and disabling its catalytic role in Mg oxidation. Machine learning-aided analyses of computed tomography (CT) scan images indicate latent gas evolution, contradicting the monotonic in vivo H<sub>2</sub> evolution that is widely accepted. Materials science ; Latent heat ; In vivo ; Chemical engineering ; Nanotechnology ; Thermodynamics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Corrosion Behavior and Inhibition
Authors: Ying Li | Haokai Xu | Xiaozhen Lan | Jixuan Wang | Xiaoming Su | Xiaoping Bai | Brian K. Via | Zhiyong Pei
Source: Renewable Energy (article, 2024-06-13)
Field: Chemistry — Analytical Chemistry

Predicting calorific value and ash content of sand shrub using Vis-NIR spectra and various chemometrics Calorific value (CV) reflects the ability of material flow and energy conversion of plants, which is the key indices of combustion properties for utilization and development of energy plants. However, the commonly used method for CV determination of solid fuels is bomb calorimetry in the laboratory using powder samples, which hinders the capability of rapid and non-destructive prediction for a large-scale samples in a natural environment. Visible and near infrared spectroscopy (Vis-NIR) has been widely proposed as a replace for laboratory determination in properties prediction. However, chemometrics are essential for spectral analysis. Various chemometrics including competitive adaptive reweighted sample (CARS), lifting wavelet transform (LWT), successive projections algorithm (SPA), and convolutional neural networks (CNNs) optimized by whale optimization algorithm (WOA) were employed to optimize models. Additionally, canopy spectra were measured in the field instead of powder samples’ spectra collecting from laboratory. The results demonstrated that CARS-WOA-CNN was the best to predict CV and ash content (AC) with R2 of 0.858 and 0.751, respectively. Compared to raw full spectra, spectral dimension was reduced from 2048 to 93 and 22 for CV and AC, respectively. Overall, this study provided a meaningful strategy for harvest planning and assessing value of biomass in the field. Chemometrics ; Shrub ; Heat of combustion ; Environmental science ; Water content ; Mineralogy ; Chemistry ; Geology ; Chromatography ; Geotechnical engineering ; Ecology Spectroscopy and Chemometric Analyses ; Heavy Metals in Plants ; Soil and Land Suitability Analysis
Authors: Jianping Guo | Leilei Gu | X.-C. Yuan | Ruihuan Wu | Qiongxiong Ma
Source: Optical Materials Express (article, 2024-06-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On demand design of multi-freedom metamaterial absorbers based on recurrent prediction networks Metamaterial absorbers (MMAs) have received a lot of attention due to their wide range of promising applications. In general, metamaterials involve a large number of geometric parameters, so the traditional simulation requires researchers to have rich experience and sufficient computational resources. In this paper, we demonstrate the forward and on demand design of multi-freedom MMAs based on the recurrent prediction network (RPN). We used the variable contribution analysis (VCA) algorithm to eliminate 55 absorption points whose contribution score was less than 1.0, the maximum fitting coefficient (R 2 ) was increased by 7.0%, and the average calculation time was reduced by 20.326s. multi-freedom design is achieved by quantifying the design material into binary features as geometric parameter inputs into the RPN model. We solve the prediction peak error problem by many-to-one RPN, compared with the many-to-many prediction of classical deep neural network (DNN) model, the RPN error range is 0-0.12 compared to the 0-0.25 error range of the DNN. The ultra-wideband MMA design with the absorption of 0.2-4.2µm broadband is more than 90%, and the highest absorption rate is 99.2%. This method can be used in zoom imaging, metamaterials filters and other fields. Metamaterial ; Computer science ; Broadband ; Range (aeronautics) ; Degrees of freedom (physics and chemistry) ; Artificial neural network ; Absorption (acoustics) ; Wideband ; Algorithm ; Materials science ; Mathematical optimization ; Computer engineering ; Optics ; Artificial intelligence ; Mathematics ; Telecommunications ; Physics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Microwave Imaging and Scattering Analysis
Authors: Gustina Alfa Trisnapradika | Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono | Hideaki Kasai
Source: Theoretical Chemistry Accounts (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

A machine learning approach for forecasting the efficacy of pyridazine corrosion inhibitors Random forest ; Quantitative structure–activity relationship ; Computer science ; Mean squared error ; Virtual screening ; Artificial intelligence ; Machine learning ; k-nearest neighbors algorithm ; Feature selection ; Support vector machine ; Correlation coefficient ; Gradient boosting ; Corrosion ; Chemistry ; Mathematics ; Statistics ; Molecular dynamics ; Computational chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Material Properties and Failure Mechanisms
Authors: Yang Zhao | Wei Li | Chenyang Zhao | Lan Fang | Huaiyu Yang | Yiming Ma
Source: Industrial & Engineering Chemistry Research (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures Solubility data of active pharmaceutical ingredients (APIs) in binary solvent mixtures are crucial for optimizing solid–liquid separation processes, conducting early solvent screening, and ensuring safety. This study presents data-driven models integrating Monte Carlo optimization algorithms, the Jouyban–Acree (JA) model, and artificial neural networks (ANN) to comprehensively predict API solubility in binary solvent mixtures. A comprehensive database comprising 71,888 data points was constructed, encompassing quantitative descriptors of the three-dimensional structures of solutes and binary solvent molecules, as well as the molecular interaction energies between these solvents. A hybrid model, Jouyban–Acree–ANN (JAANN), was developed to predict solubility across various temperatures and solvent compositions. This model demonstrated robust predictive performance, with prediction errors generally below 10%. Additionally, we introduced a Point Adjusted JAANN (PA-JAANN) model that integrates Monte Carlo simulations to refine solubility predictions by calibrating a single experimental data point. This calibration significantly enhances the model’s accuracy, achieving an average error reduction of over 20% compared to the standard JAANN model. A comparative Direct Prediction-ANN (DP-ANN) model was also constructed, providing rapid solubility predictions without experimental data, though it had limitations in robustness. The predictive abilities of these three models were thoroughly validated based on experiments involving the mefenamic acid–2-butanol–heptane system. These models can be used for different predictive needs, offering flexible and reliable solubility predictions essential for optimizing crystallization processes in pharmaceutical manufacturing. Binary number ; Artificial neural network ; Solubility ; Solvent ; Chemistry ; Computer science ; Organic chemistry ; Artificial intelligence ; Mathematics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Awais Qadir | Shoaib Ali | Ján Dusza | David Rafaja
Source: Open Ceramics (article, 2024-07-05)
Field: Materials Science — Ceramics and Composites

Predicting hardness of graphene-added Si3N4 using machine learning: A data-driven approach This study presents a data-driven framework based on machine learning (ML) using extreme gradient boosting (XGBoost) for predicting the hardness of silicon nitride (Si3N4) ceramics reinforced with graphene. The XGBoost model takes into account various factors such as graphene type and content, characteristics of the raw Si3N4 powder, the parameters of the sintering process (sintering technique, temperature, pressure, holding time), and the characteristics of the sintered samples, i.e., the density, αβ content and Vickers hardness. The parameters that influence the Si3N4 hardness most strongly are identified, with sintering pressure, sintering time and density being the most influential. The addition of graphene content up to a certain threshold (1 wt%) has a positive impact on hardness. However, beyond that it leads to a lower density and a lower mechanical performance. Sintering parameters, particularly the sintering pressure, temperature, holding time and technique, strongly affect the density, final grain size, αβ Si3N4 composition and subsequently the hardness. The study highlights the importance of density and the densification process in achieving high hardness in Si3N4 ceramics. The developed ML model provides a valuable tool for predicting the hardness of Si3N4+graphene ceramics composites and offers insights into selecting suitable graphene type, content, and processing parameters. While the study primarily focuses on Si3N4+graphene composites, this novel approach holds promise for the in-silico design and analysis of diverse ceramic materials. Materials science ; Sintering ; Graphene ; Vickers hardness test ; Silicon nitride ; Ceramic ; Composite material ; Grain size ; Indentation hardness ; Silicon ; Metallurgy ; Nanotechnology ; Microstructure Advanced ceramic materials synthesis ; Thermal properties of materials ; Advanced materials and composites
Authors: Eneko López | Jaione Etxebarria‐Elezgarai | Maite García‐Sebastián | Miren Altuna | Mirian Ecay‐Torres | Ainara Estanga | Mikel Tainta | Carolina López | Pablo Martínez‐Lage | José Manuel Amigo | Andreas Seifert
Source: International Journal of Molecular Sciences (article, 2024-04-26)
Field: Chemistry — Analytical Chemistry

Unlocking Preclinical Alzheimer’s: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods. Chemometrics ; Raman spectroscopy ; In vitro ; Chemistry ; Medicine ; Computational biology ; Analytical Chemistry (journal) ; Biology ; Chromatography ; Biochemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Ning Yang | Jing Guo
Source: Solid-State Electronics (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Performance potential of transistors based on tellurium nanowire arrays: A quantum transport study Materials science ; Nanowire ; Optoelectronics ; Quantum tunnelling ; Transistor ; Leakage (economics) ; Schottky barrier ; Ambipolar diffusion ; Nanotechnology ; Band gap ; Subthreshold slope ; Nanoelectronics ; Field-effect transistor ; Electrical engineering ; Diode ; Physics ; Engineering Graphene research and applications ; 2D Materials and Applications ; Molecular Junctions and Nanostructures
Authors: Y. Chen | Xiaogang Wu | K. Zhou | Haoran Hu
Source: Applied Energy (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Thermal - electric cooperative control of solid oxide electrolytic cell stack considering system efficiency optimization Stack (abstract data type) ; Electrolytic cell ; Electrolyte ; Solid oxide fuel cell ; Thermal ; Oxide ; Process engineering ; Materials science ; Engineering ; Computer science ; Chemical engineering ; Chemistry ; Nuclear engineering ; Electrolysis ; Thermodynamics ; Electrode ; Physics ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Qiaoyan Jiang | Yuan Zhang | Yang Sun | Lixiang Wang | Zhengsheng Mao | Cong Pian | Ping Huang | Feng Chen | Yue Cao
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-09-06)
Field: Chemistry — Spectroscopy

On-site SERS analysis and intelligent multi-identification of fentanyl class substances by deep machine learning Chemistry ; Fentanyl ; Nanomaterials ; Nanotechnology ; Identification (biology) ; Combinatorial chemistry ; Materials science Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tao Yu | Shengcai Zhu | Yanglong Hou
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A novel sp<sup>3</sup> carbon allotrope with 4+5+6+7+8 odd–even ring In this study, we report a novel monoclinic phase of carbon that contains 4+5+6+7+8 member rings in <i>P</i>2<sub>1</sub>/<i>m</i> symmetry, identified by applying the stochastic surface walking method combined with high dimensional neural network potentials. We demonstrate that this phase possesses lower energy than graphite above 21.5 GPa. The phonon spectra show that this structure is stable under ambient pressure. This phase is a super hard material with a shear hardness as high as 81.9 GPa while it possesses an indirect band gap of 3.16 eV. The energy barrier of graphite to the Y phase is 0.27 eV, slightly higher than that of the hexagonal diamond (0.21 eV) in a similar phase transition mechanism. Two types of thermodynamically stable interfaces can be formed with the hexagonal diamond (HD), namely (001)<sub>Y</sub>//(100)<sub>HD</sub>, [100]<sub>Y</sub>//[010]<sub>HD</sub> and (001)<sub>Y</sub>//(001)<sub>HD</sub>, [010]<sub>Y</sub>//[001]<sub>HD</sub>. Although the discrete bulk Y phase is hard to synthesize, a faulted structure between HD is possible because of the well-matched interface between Y and HD. Our work shows that the Y phase may be formed in some special conditions and enhances our understanding of the formation of novel carbon allotropes. Monoclinic crystal system ; Ring (chemistry) ; Carbon fibers ; Crystallography ; Phase (matter) ; Chemistry ; Materials science ; Organic chemistry ; Crystal structure Porphyrin and Phthalocyanine Chemistry ; Synthesis and Properties of Aromatic Compounds ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Eslam Ibrahim | Yury Lysogorskiy | Ralf Drautz
Source: Journal of Chemical Theory and Computation (article, 2024-10-21)
Field: Chemistry — Inorganic Chemistry

Efficient Parametrization of Transferable Atomic Cluster Expansion for Water We present a highly accurate and transferable parametrization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of the ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab initio molecular dynamics simulations. Our results demonstrate that the ACE descriptors enable a potential initially fitted solely on ice structures, which is later upfitted with few configurations of liquid, identified with AL to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient, and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations while also presenting a transferable interatomic potential for water that reveals the accuracy of first-principles reference. This advancement not only enhances our understanding of the relationship between ice and liquid water at the atomic level but also opens up new avenues for studying complex aqueous systems. Parametrization (atmospheric modeling) ; Cluster (spacecraft) ; Computer science ; Water cluster ; Physics ; Statistical physics ; Chemistry ; Chemical physics ; Quantum mechanics ; Molecule Radioactive element chemistry and processing ; Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis