jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yueyu Zhou | Jing Gao | Yiting Gui | Jun Wen | Yan Wang | Xiaoxiao Huang | Jun Cheng | Quanjin Liu | Qiang Wang | Chenlong Wei
Source: Optical Materials X (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Prediction of formation energies of UCr4C4-type compounds from Magpie feature descriptor-based machine learning approaches Lanthanide-doped UCr4C4-type phosphors have become one of the most promising materials for the narrow-band luminescence, due to their high photoluminescence quantum efficiency and good thermal stabilities. In this study, four machine learning regression models (i.e., the gradient boosted regression (GBR), support vector regression (SVR), random forest (RF) and K-nearest neighbor (KNN)) were established in combination with Magpie feature descriptors in order to calculate formation energies and then study thermal stabilities of phosphor hosts. Thereinto, the GBR model had the best performance (R2: 0.945, MAE: 0.106 eV/atom, MSE: 0.032 eV/atom) and then relatively accurately predicted formation energies of UCr4C4-type compounds. Besides, the Shapley additive explanation (SHAP) was implemented to interpret the prediction of the GBR and analyze the importance of feature descriptors. It is expected that the computing framework in the present work would provide a beneficial guidance for the study of the physical and chemical properties of inorganic phosphors. Phosphor ; Feature (linguistics) ; Support vector machine ; Random forest ; Photoluminescence ; Atom (system on chip) ; Feature vector ; Artificial intelligence ; k-nearest neighbors algorithm ; Computer science ; Machine learning ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Jinseok Ryu | Sangmin Lee | Sung‐Tae Kim | Young‐Chang Joo | Miyoung Kim
Source: Applied Physics Letters (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Correlative study between the local atomic and electronic structures of amorphous carbon materials via 4D-STEM and STEM-EELS Amorphous materials have been used in a range of electronic and photonic applications, and the need for quantitative analytical techniques on their local structural information is growing. We present a comprehensive analysis of the atomic and electronic structures of an amorphous material, amorphous carbon (a-C), with scanning transmission electron microscopy (STEM)-derived techniques, four-dimensional STEM (4D-STEM), and STEM-electron energy loss spectroscopy (STEM-EELS). Each diffraction pattern of an a-C layer stack acquired via 4D-STEM is transformed into a reduced density function (RDF) and a radial variance profile (RVP) to retrieve the information on the atomic structures. Importantly, a machine-learning approach (preferably cluster analysis) separates distinct features in the EELS and RDF datasets; it also describes the spatial distributions of these features in the scanned regions. Consequently, we showed that the differences in the sp2/sp3 ratio and the involvement of additional elements led to changes in the bond length. Furthermore, we identified the dominant types of medium-range ordering structures (diamond-like or graphite-like nano-crystals) by correlations among the EELS, RDF, and RVP data. The information obtained via STEM-EELS and 4D-STEM can be strongly correlated, leading to the comprehensive characterization of the a-C layer stack for a nanometer-scale area. This process can be used to investigate any amorphous material, thereby yielding comprehensive information regarding the origins of notable properties. Scanning transmission electron microscopy ; Amorphous solid ; Materials science ; Amorphous carbon ; Electron energy loss spectroscopy ; Chemical physics ; Nanotechnology ; Transmission electron microscopy ; Crystallography ; Chemistry Diamond and Carbon-based Materials Research ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Phanikumar Pentyala | Vibhuti Singhania | Vinay Kumar Duggineni | Parag A. Deshpande
Source: Molecular Catalysis (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

Machine learning-assisted DFT reveals key descriptors governing the vacancy formation energy in Pd-substituted multicomponent ceria Vacancy defect ; Catalysis ; Ionic bonding ; Chemical physics ; Charge (physics) ; Partial charge ; Metal ; Partial oxidation ; Oxygen ; Materials science ; Chemistry ; Ion ; Crystallography ; Physics ; Metallurgy ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Wei-Chih Chen | Yogesh K. Vohra | Cheng-Chien Chen
Source: ACS Omega (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

Discovering Superhard B–N–O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions We searched for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from an evolutionary algorithm. We first used cohesive energy to evaluate the thermodynamic stability of varying B <sub><i>x</i></sub> N <sub><i>y</i></sub> O <sub><i>z</i></sub> compositions and then gradually focused on compositional regions with high cohesive energy and high hardness. The results converged quickly after a few iterations. Our resulting ML models show that B <sub><i>x</i>+2</sub>N <sub><i>x</i></sub> O<sub>3</sub> compounds with <i>x</i> ≥ 3 (like B<sub>5</sub>N<sub>3</sub>O<sub>3</sub>, B<sub>6</sub>N<sub>4</sub>O<sub>3</sub>, etc.) are potentially superhard and thermodynamically favorable. Our meta-GGA density functional theory calculations indicate that these materials are also wide bandgap (≥4.4 eV) insulators, with the valence band maximum related to the p-orbitals of nitrogen atoms near vacant sites. This study demonstrates that an iterative method combining ML and <i>ab initio</i> simulations provides a powerful tool for discovering novel materials. Atomic orbital ; Ab initio ; Stability (learning theory) ; Materials science ; Density functional theory ; Band gap ; Total energy ; Crystal structure prediction ; Energy (signal processing) ; Valence (chemistry) ; Crystal structure ; Computer science ; Crystallography ; Machine learning ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Optoelectronics Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Jeffrey Ting | Corinne E. Lipscomb
Source: Pure and Applied Chemistry (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Launching a materials informatics initiative for industrial applications in materials science, chemistry, and engineering Abstract The advent of materials informatics (MI) with emerging global trends in digitalization, artificial intelligence, and automation has led to promising opportunities for transforming traditional scientific research workflows. However, new MI efforts rely critically on the establishment, management, and accessibility of high-quality thermophysical and chemical data, either by mining existing databases, labelling historical data in archives, or generating sufficient data sets as prerequisites to the creation of predictive machine learning models. For ambitious MI-driven projects, amassing systematic data can be a time-intensive and prohibitively costly endeavor in spaces where data is uncurated or scarce. Here, we describe a MI initiative that started in the 3M Corporate Research Laboratories (CRL), highlighting how we strategically applied MI tools and data-driven methodologies for industrial materials research and product development workflows. Robust web applications and cloud infrastructure were developed to structure, standardize, and aggregate materials data for specific CRL projects. This integrated approach leverages the diverse skills and deep technical expertise of subject-matter experts at 3M to build the foundations for MI through systematic data management in materials research and, ultimately, to advance core technology platforms with innovative, customer-driven product solutions. Key elements that have contributed to the ongoing implementation of this highly versatile MI program, as well as challenges encountered, are presented as lessons learned for the broader MI and cheminformatics communities. Workflow ; Cloud computing ; Data science ; Informatics ; Big data ; Computer science ; Data management ; Materials informatics ; Engineering management ; Knowledge management ; Engineering ; Engineering informatics ; Health informatics ; Database ; Data mining ; Political science Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Saikou Cesay | Paul Teng | Ruoli Wang | Haupeng Yue | Arbaaz Khan | David A. Lowther
Source: 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC) (article, 2022-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generalizable DNN based multi-material Hysteresis Modelling This The effective representation of material properties is fundamental to the simulation of electromagnetic devices such as electrical machines, actuators, sensors, transformers, etc. However, the actual operating point of a material is dependent both on position within the device and the excitation. Every point in an electrical machine can be operating on a different part of the magnetization curve. To determine performance parameters such as the efficiency of the machine, the hysteretic behavior of the material is crucial, and the representation used can impact the performance of a simulation code. In this paper, the use of deep learning methods is proposed to reduce the computational effort needed to implement hysteresis in a finite element based simulation system. Transformer ; Computer science ; Actuator ; Representation (politics) ; Finite element method ; Point (geometry) ; Hysteresis ; Operating point ; Magnetic hysteresis ; Electronic engineering ; Control engineering ; Mechanical engineering ; Magnetization ; Voltage ; Electrical engineering ; Engineering ; Artificial intelligence ; Structural engineering ; Physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Ying Zhao | Liang Li | Stéphane Lanteri | Jonathan Viquerat
Source: Mathematics and Computers in Simulation (article, 2022-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic metasurface control using Deep Reinforcement Learning Reinforcement learning ; Correctness ; Computer science ; Context (archaeology) ; Artificial neural network ; Finite-difference time-domain method ; Inverse ; Sequence (biology) ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xin Zhang | Chunzhi Li | Ming Xue | Wenbin Wang | Lihua Zhu
Source: Journal of Electrical Engineering and Technology (article, 2022-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of Deep Learning in Motor Vibration and Noise Suppression Based on Negative Magnetostrictive Effect Stator ; Magnetostriction ; Noise (video) ; Vibration ; Finite element method ; Acoustics ; Stress (linguistics) ; Electromagnetic coil ; Coupling (piping) ; Position (finance) ; Structural engineering ; Engineering ; Computer science ; Physics ; Mechanical engineering ; Magnetic field ; Electrical engineering ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Metallurgy and Material Forming
Authors: Jun Li | Qi An
Source: Journal of the American Ceramic Society (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Shear‐induced amorphization in boron subphosphide (B <sub>12</sub> P <sub>2</sub> ): Direct transition versus stacking fault mediation Abstract The shear‐induced amorphization has been observed in many strong ceramics and is responsible for their cracking and fragmentation. But its underlying mechanism remains elusive due to the complex structure and bonding environment in strong ceramics. To illustrate the deformation mechanism of local amorphization in strong ceramics, we employed molecular dynamics simulations with a deep‐learning force field to examine the shear‐induced amorphization in B 12 P 2 . Surprisingly, we identified a stacking‐fault‐mediated amorphization mechanism along the most plausible slip system (1 1 1)/[1 1 ]. This mechanism is even more favorable at a higher temperature than room temperature. In contrast, the direct crystal to amorphization transition, due to the icosahedral slip, is observed for the other most plausible slip system (0 1 1)/[2 ]. We report the activation volume and the activation free energy for the amorphization along the (1 1 1)/[1 1 ] slip system. The derived activation volume is only 41.47 A 3 , which is roughly 2–3 icosahedra, suggesting that the localized amorphization in B 12 P 2 is mediated by stacking fault formation. The previous results suggest complex amorphization mechanisms in strong ceramics. Materials science ; Slip (aerodynamics) ; Crystallography ; Stacking fault ; Molecular dynamics ; Icosahedral symmetry ; Boron ; Stacking-fault energy ; Chemical physics ; Activation energy ; Deformation mechanism ; Shear (geology) ; Stacking ; Crystal structure ; Condensed matter physics ; Thermodynamics ; Composite material ; Chemistry ; Dislocation ; Computational chemistry ; Physical chemistry ; Microstructure ; Physics Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; MXene and MAX Phase Materials
Authors: Benjamín Plá | Pau Bares | Enrique José Sanchís | André Nakaema Aronis
Source: Fuel (article, 2022-04-14)
Field: Materials Science — Materials Chemistry

Ammonia injection failure diagnostic and correction in engine after-treatment system by NOx and NH3 emissions observation NOx ; Ammonia ; Selective catalytic reduction ; Injector ; Fuel injection ; Environmental science ; Catalysis ; Process engineering ; Chemistry ; Automotive engineering ; Computer science ; Engineering ; Mechanical engineering ; Combustion Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Water Quality Monitoring and Analysis
Authors: R. Murrieta-Dueñas | Juan Pablo Serrano-Rubio | V. López-Ramírez | Ignacio Segovia-Domínguez | J. Cortez-González
Source: Process Safety and Environmental Protection (article, 2022-08-17)
Field: Chemistry — Physical and Theoretical Chemistry

Prediction of microbial growth via the hyperconic neural network approach Biological system ; Bacterial growth ; Multilayer perceptron ; Artificial neural network ; Exponential growth ; Computer science ; Biochemical engineering ; Machine learning ; Mathematics ; Artificial intelligence ; Engineering ; Biology thermodynamics and calorimetric analyses ; 3D Printing in Biomedical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Marianna Sledzinska | Peng Xiao | Eulàlia Puig Vilardell | E. Chávez Angel | M.J. Esplandiu | C. M. Sotomayor Torres
Source: Applied Physics Letters (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

Exciton tuning and strain imaging in WS2 supported on PDMS micropillars Since the raise of 2D materials, significant research has been dedicated to their strain-dependent electronic and mechanical properties. In this work, we studied exciton energies and low-frequency phonon modes in CVD-grown mono- and few-layer WS2 transferred on PDMS micropillars. The modification of the band structure under strain was investigated by photoluminescence (PL) spectroscopy at room temperature. Machine learning (ML) methods were used to analyze the PL spatial maps and facilitate the spectral deconvolution. For monolayer (1L) WS2, red shift in the exciton energy was detected as a function of the position, which was ascribed to the presence of residual strain. For three-layer (3L) strained WS2, a significant increase in the PL intensity corresponding to direct (K–K) band transition together with a change of exciton energy was observed. From the PL spectra, strain distribution maps were extracted for both studied samples, which strongly resembled the ML clustering results. Finally, the low-frequency Raman modes of WS2 were studied on both Si/SiO2 and PDMS substrates and no significant change of their frequency was observed for the 3L-WS2. Exciton ; Raman spectroscopy ; Photoluminescence ; Monolayer ; Materials science ; Strain (injury) ; Phonon ; Raman scattering ; Spectroscopy ; Band gap ; Optoelectronics ; Analytical Chemistry (journal) ; Chemical physics ; Molecular physics ; Condensed matter physics ; Nanotechnology ; Chemistry ; Optics 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; MXene and MAX Phase Materials
Authors: Minori Uchimiya
Source: Journal of Agriculture and Food Research (article, 2022-07-05)
Field: Chemistry — Analytical Chemistry

Aromaticity of secondary products as the marker for sweet sorghum [Sorghum bicolor (L.) Moench] genotype and environment effects Although data analytics and systems modeling are increasingly being utilized to interpret genotype × environment interactions in plant breeding, chemometrics is currently underutilized. Prior reports indicated correlations between redox-active polyphenols in stem juice of sweet sorghum [Sorghum bicolor (L.) Moench] and its resistance against sugarcane aphid [Melanaphis sacchari (Zehntner)]. However, such correlations are often confounded by the outperforming genotype (outlier in correlations) capable of accumulating several-fold higher secondary metabolites than other cultivars examined. To investigate the underlying chemical characteristics responsible for the pest resistance, this study first employed principal component analysis (PCA) as the exploratory analysis to visualize the primary factors affecting genotype and environmental (April, May, and June planting months) dependence of juice extracted from 24 sweet sorghum cultivars. Chemical parameters arising from redox reactivity were primarily responsible for distinguishing a resistant genotype. The distance of dendrogram based on the genotype-dependent electrochemistry (cyclic voltammetry) was then used as the perturbation parameter in 2D correlation analysis to understand the controlling chemical structures; both genotype and environment were controlled by the redox reactivity and aromaticity of sweet sorghum. Aromatic structures detectable by UV/visible spectrophotometry were then used to build calibrations based on machine learning. The workflow of data analytics in this study could be applied to expedite the biomarker-driven plant breeding without repeating chemical analysis of new field samples. Sweet sorghum ; Sorghum ; Cultivar ; Chemometrics ; Principal component analysis ; Biology ; Genotype ; Horticulture ; Biotechnology ; Chemistry ; Agronomy ; Mathematics ; Biochemistry ; Statistics ; Chromatography Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding
Authors: Marcin Mińkowski | David Kurunczi-Papp | Lasse Laurson
Source: Physical Review Materials (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity Predicting the behavior of complex systems is one of the main goals of science. An important example is plastic deformation of micron-scale crystals, a process mediated by collective dynamics of dislocations, manifested as broadly distributed strain bursts and significant sample-to-sample variations in the response to applied loading. Here, by combining large-scale discrete dislocation dynamics simulations and machine learning, we study the problem of predicting the fluctuating stress-strain curves of individual small single crystals subject to strain-controlled loading using features of the initial dislocation configurations as input. Our results reveal an intriguing rate dependence of deformation predictability: For small strains predictability improves with increasing strain rate, while for larger strains the predictability vs strain rate relation becomes nonmonotonic. We show that for small strains the rate dependence of deformation predictability can be captured by considering the fraction of dislocations moving against the direction imposed by the external stress, serving as a measure of strain-rate-dependent complexity of the dislocation dynamics. The nonmonotonic predictability vs strain rate relation for large strains is argued to be related to a transition from fluctuating to smooth plastic flow when strain rate is increased. Predictability ; Dislocation ; Strain rate ; Materials science ; Plasticity ; Deformation (meteorology) ; Strain (injury) ; Stress (linguistics) ; Flow stress ; Statistical physics ; Mechanics ; Mathematics ; Composite material ; Physics ; Statistics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Fernando Martins dos Santos | Gunar Vingre da Silva Mota | Lucas H. Martorano | Ana Carolina Ferreira de Albuquerque | Claudinei Alves da Silva | A.A. Silva | Antônio Maia de Jesus Chaves Neto | Alessandra L. Valverde | Evani Ferreira Cardoso | Fábio Luiz Paranhos Costa
Source: Magnetic Resonance in Chemistry (article, 2022-03-01)
Field: Chemistry — Spectroscopy

Combined application of DP4+ and ANN‐PRA to determine the relative configuration of natural products: The alpha‐bisabol case study The combination of computational methods and experimental data from Nuclear Magnetic Resonance (NMR) is a considerably valuable tool in the elucidation of new natural product structures and, also, in the structural revision of previously reported compounds. Until recently, only classical statistical parameters were used, for example, linear correlation coefficient (R<sup>2</sup> ), mean absolute error (MAE), or root mean square deviation (RMSD), as a way to statistically "validate" the structure pointed out by experimental NMR spectra. Regarding the resolution of the relative configuration of organic molecules, novel tools were available in the last few years to assist in the NMR elucidation process. The most relevant are DP4+, which is based on a Bayesian probability, and ANN-PRA, which is based on artificial neural networks. The combined application of these tools has become the most accurate and important alternative to solve structural and stereochemical problems in natural product chemistry. Therefore, herein, in this case study, we intended to promote these novel tools, exploring the strengths and limitations of each approach in resolving the relative configuration of the sesquiterpene alpha-bisabol. We also highlighted the advantages of the complementary use of H- and C-DP4+ to obtain optimal results in the differentiation of the stereoisomers, validating the proposal with ANN-PRA method. Chemistry ; Mean squared error ; Biological system ; Artificial neural network ; Natural product ; Correlation coefficient ; Root mean square ; Approximation error ; Bayesian probability ; Algorithm ; Artificial intelligence ; Stereochemistry ; Computer science ; Machine learning ; Statistics ; Mathematics ; Physics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography