jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Holdship | S. Viti | T. J. Haworth | J. D. Ilee
Source: Astronomy and Astrophysics (article, 2021-07-05)
Field: Materials Science — Materials Chemistry

Chemulator: Fast, accurate thermochemistry for dynamical models through emulation Context. Chemical modelling serves two purposes in dynamical models: accounting for the effect of microphysics on the dynamics and providing observable signatures. Ideally, the former must be done as part of the hydrodynamic simulation but this comes with a prohibitive computational cost that leads to many simplifications being used in practice. Aims. We aim to produce a statistical emulator that replicates a full chemical model capable of solving the temperature and abundances of a gas through time. This emulator should suffer only a minor loss of accuracy when compared to a full chemical solver and would have a fraction of the computational cost allowing it to be included in a dynamical model. Methods. The gas-grain chemical code UCLCHEM was updated to include heating and cooling processes, and a large dataset of model outputs from possible starting conditions was produced. A neural network was then trained to map directly from inputs to outputs. Results. Chemulator replicates the outputs of UCLCHEM with an overall mean squared error (MSE) of 1.7 × 10 −4 for a single time step of 1000 yr, and it is shown to be stable over 1000 iterations with an MSE of 3 × 10 −3 on the log-scaled temperature after one timzze step and 6 × 10 −3 after 1000 time steps. Chemulator was found to be approximately 50 000 times faster than the time-dependent model it emulates but can introduce a significant error to some models. Emulation ; Solver ; Fraction (chemistry) ; Thermochemistry ; Artificial neural network ; Observable ; Mean squared error Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Aron K. Beke | Martin Gyürkés | Zsombor Kristóf Nagy | György Marosi | Attila Farkas
Source: European Journal of Pharmaceutics and Biopharmaceutics (article, 2021-09-22)
Field: Chemistry — Analytical Chemistry

Digital twin of low dosage continuous powder blending – Artificial neural networks and residence time distribution models In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development. Process analytical technology ; Critical quality attributes ; Artificial neural network ; Quality by Design ; Computer science ; Process (computing) ; Residence time distribution ; Process engineering ; Calibration ; Artificial intelligence ; Machine learning ; Work in process ; Engineering ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Matthew Guziewski | David Montes de Oca Zapiain | Rémi Dingreville | Shawn P. Coleman
Source: ACS Applied Materials & Interfaces (article, 2021-01-07)
Field: Materials Science — Materials Chemistry

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. A diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales. Grain boundary ; Materials science ; Misorientation ; Grain boundary strengthening ; Silicon carbide ; Boundary (topology) ; Characterization (materials science) ; Statistical physics ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Composite material ; Microstructure ; Physics ; Mathematics ; Mathematical analysis Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Shaik Kareem Ahmmad | Nameera Jabeen | Syed Taqi Uddin Ahmed | Syed Ferhatullah Hussainy | Basheer Ahmed
Source: Ceramics International (article, 2021-07-21)
Field: Materials Science — Ceramics and Composites

Density of fluoride glasses through artificial intelligence techniques Fluoride ; Materials science ; Artificial neural network ; Linear regression ; Boron ; Mineralogy ; Biological system ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Chemistry ; Inorganic chemistry Glass properties and applications ; Digital Radiography and Breast Imaging ; Advanced X-ray and CT Imaging
Authors: Weixiao Zhan | Xiaodong Yang | Guoquan Lu | Yun Deng | Linnan Yang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-07-16)
Field: Chemistry — Analytical Chemistry

A rapid quality grade discrimination method for Gastrodia elata powderusing ATR-FTIR and chemometrics Gastrodia elata ; Chemometrics ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Classifier (UML) ; Fourier transform infrared spectroscopy ; Computer science ; Mathematics ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Metabolomics and Mass Spectrometry Studies
Authors: Litao Chen | Wentao Zhang | Zhiwei Nie | Shunning Li | Feng Pan
Source: Journal of Materials Informatics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Generative models for inverse design of inorganic solid materials Journal of Materials Informatics (JMI) is an international peer-reviewed, open access journal. The journal provides a platform for presentation, publication, and exchange of materials informatics research, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge. Informatics ; Materials informatics ; Presentation (obstetrics) ; Data science ; Computer science ; Science and engineering ; Generative grammar ; Engineering informatics ; Health informatics ; Engineering ethics ; Engineering ; Artificial intelligence ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Nir Goldman | Kyoung E. Kweon | Babak Sadigh | Tae Wook Heo | Rebecca Lindsey | C. Huy Pham | Laurence E. Fried | Bálint Aradi | Kiel Holliday | Jason R. Jeffries | Brandon C. Wood
Source: Journal of Chemical Theory and Computation (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH<sub>2</sub> as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results. Computer science ; Chebyshev polynomials ; Tight binding ; Workflow ; Chebyshev filter ; Density functional theory ; Quantum ; Set (abstract data type) ; Statistical physics ; Range (aeronautics) ; Force field (fiction) ; Field (mathematics) ; Biological system ; Computational science ; Electronic structure ; Computational chemistry ; Materials science ; Physics ; Mathematics ; Chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Alexander Stangl | Adeel Riaz | Laëtitia Rapenne | José Manuel Caicedo | Juan de Dios Sirvent | Federico Baiutti | Carmen Jiménez | Albert Tarancón | Michel Mermoux | Mónica Burriel
Source: Journal of Materials Chemistry A (article, 2021-12-07)
Field: Materials Science — Materials Chemistry

Tailored nano-columnar La<sub>2</sub>NiO<sub>4</sub> cathodes for improved electrode performance We report a new strategy to grow nano-columnar La 2 NiO 4 thin films with an enlarged specific surface area. In combination with modifications of the surface chemistry, we strongly improved the oxygen exchange activity of this promising electrode material. Non-blocking I/O ; Electrode ; Nano- ; Materials science ; Cathode ; Nanotechnology ; Thin film ; Chemical engineering ; Chemistry ; Composite material ; Catalysis ; Physical chemistry Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells ; Semiconductor materials and devices
Authors: Siarhei Zhuk | Andrey A. Kistanov | Simon C. Boehme | Noémie Ott | Fabio La Mattina | Michael Stiefel | Maksym V. Kovalenko | Sebastian Siol
Source: Chemistry of Materials (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of the Ternary Nitride Semiconductor Zn<sub>2</sub>VN<sub>3</sub>: Theoretical Prediction, Combinatorial Screening, and Epitaxial Stabilization Computationally guided high-throughput synthesis is used to explore the Zn–V–N phase space, resulting in the synthesis of a novel ternary nitride Zn2VN3. Following a combinatorial PVD screening, we isolate the phase and synthesize polycrystalline Zn2VN3 thin films with wurtzite structure on conventional borosilicate glass substrates. In addition, we demonstrate that cation-disordered, but phase-pure (002)-textured, Zn2VN3 thin films can be grown using epitaxial stabilization on α-Al2O3 (0001) substrates at remarkably low growth temperatures well below 200 °C. The structural properties and phase composition of the Zn2VN3 films are studied in detail using XRD and (S)TEM techniques. The composition as well as chemical state of the constituent elements are studied using RBS/ERDA and XPS/HAXPES methods. These analyses reveal a stoichiometric material with no oxygen contamination, besides a thin surface oxide. We find that Zn2VN3 is a weakly doped p-type semiconductor demonstrating broad-band room-temperature photoluminescence spanning the range between 2 and 3 eV. In addition, the electronic properties can be tuned over a wide range via isostructural alloying on the cation site, making this a promising material for optoelectronic applications. Ternary operation ; Characterization (materials science) ; Epitaxy ; Materials science ; Nitride ; Semiconductor ; Optoelectronics ; Nanotechnology ; Computer science Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials
Authors: Jinzhao Li | Junyu Li | Shudao Zhou | Fei Yi
Source: Micromachines (review, 2021-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface Photodetectors Photodetectors are the essential building blocks of a wide range of optical systems. Typical photodetectors only convert the intensity of light electrical output signals, leaving other electromagnetic parameters, such as the frequencies, phases, and polarization states unresolved. Metasurfaces are arrays of subwavelength structures that can manipulate the amplitude, phase, frequency, and polarization state of light. When combined with photodetectors, metasurfaces can enhance the light-matter interaction at the pixel level and also enable the detector pixels to resolve more electromagnetic parameters. In this paper, we review recent research efforts in merging metasurfaces with photodetectors towards improved detection performances and advanced detection schemes. The impacts of merging metasurfaces with photodetectors, on the architecture of optical systems, and potential applications are also discussed. Photodetector ; Detector ; Optoelectronics ; Polarization (electrochemistry) ; Optics ; Physics ; Amplitude Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Wireless Communication Technologies
Authors: Laura Rolinger | Matthias Rüdt | Jürgen Hubbuch
Source: Biotechnology and Bioengineering (article, 2021-07-23)
Field: Chemistry — Analytical Chemistry

Comparison of UV‐ and Raman‐based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L<sup>-1</sup> was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L<sup>-1</sup> . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase. Partial least squares regression ; Mean squared error ; Fusion ; Sensor fusion ; Raman spectroscopy ; Biological system ; Convolutional neural network ; Linear regression ; Artificial intelligence ; Computer science ; Process analytical technology ; Artificial neural network ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Statistics ; Engineering ; Physics ; Work in process Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Analytical Chemistry and Chromatography
Authors: Urszula Kotowska | Joanna Struk-Sokołowska | Janina Piekutin
Source: Scientific Reports (article, 2021-05-12)
Field: Chemistry — Analytical Chemistry

Simultaneous determination of low molecule benzotriazoles and benzotriazole UV stabilizers in wastewater by ultrasound-assisted emulsification microextraction followed by GC–MS detection A rapid, sensitive, economically and ecologically friendly method based on one-step ultrasound-assisted emulsification microextraction and in situ derivatization followed by gas chromatography-mass spectrometry for simultaneous determination of low molecular benzotriazoles and benzotriazole-based ultraviolet filters was developed. The optimized method allows quantification of benzotriazole, 4-methylbenzotriazole, 5-methylbenzotriazole; 5-chlorobenzotriazole, 2-(2'-hydroxy-3'-tert-butyl-5'-methylphenyl)-5-chlorobenzortriazole and 2-(2'-hydroxy-5'-(1,1,3,3-tetramethylbutyl)phenyl)benzotriazole in municipal and industrial (dairy) wastewater. The method was validated using real influent and effluent wastewater and samples at various stages of the purification process. Relative recoveries obtained using wastewater as sample matrix were between 77 and 137%, method limits of detection from 0.001 to 0.035 µg/L, method limits of quantification from 0.003 to 0.116 µg/L, the repeatability expressed by the coefficient of variation did not exceed 12%. The use of the method for the determination of tested compounds in municipal and industrial wastewater showed their presence in most of the tested samples, in concentrations from LoD to 6.110 µg/L. The conducted studies of samples from municipal wastewater treatment plant located in north-east Poland showed that the effectiveness of benzotriazole removal by this plant wasfrom 29 to 84%. The load of tested compounds released into the environment by this facility ranges from 2 to 269 mg/day/1000 inhabitants. Benzotriazole ; Derivatization ; Wastewater ; Chromatography ; Repeatability ; Detection limit ; Gas chromatography–mass spectrometry ; Chemistry ; Gas chromatography ; Effluent ; Mass spectrometry ; Sewage treatment ; Environmental science ; Organic chemistry ; Environmental engineering Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Water Quality Monitoring and Analysis
Authors: Mingxuan Gao | Wenxian Yang | Chenxin Li | Yuqing Chang | Yachen Liu | Qingzu He | Chuan‐Qi Zhong | Jianwei Shuai | Rongshan Yu | Jiahuai Han
Source: Communications Biology (article, 2021-10-14)
Field: Chemistry — Spectroscopy

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics We developed DreamDIA<sup>XMBD</sup> (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis. Computer science ; Discriminative model ; Representation (politics) ; Artificial intelligence ; Pattern recognition (psychology) ; Decoy ; Software ; Deep learning ; External Data Representation ; Data mining ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications