jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sait Senceroglu | Mohamed Arselene Ayari | Tahereh Rezaei | Fardad Faress | Amith Khandakar | Muhammad E. H. Chowdhury | Zanko Hassan Jawhar
Source: Pharmaceuticals (article, 2022-11-14)
Field: Chemistry — Analytical Chemistry

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer-drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer-drug systems' stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future. Solubility ; Ibuprofen ; Support vector machine ; Materials science ; Polymer ; Chromatography ; Chemistry ; Computer science ; Pharmacology ; Machine learning ; Organic chemistry ; Medicine ; Composite material Analytical Methods in Pharmaceuticals ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Uttam Kumar Kar | J. Srinivas
Source: Journal of the Brazilian Society of Mechanical Sciences and Engineering (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Frequency analysis and shock response studies in bidirectional functionally graded microbeam with thermal effects Microbeam ; Materials science ; Mechanics ; Material properties ; Finite element method ; Vibration ; Natural frequency ; Boundary value problem ; Structural engineering ; Acoustics ; Mathematics ; Mathematical analysis ; Engineering ; Physics ; Optics ; Composite material Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Ryo Tamura | Momo Matsuda | Jianbo Lin | Yasunori Futamura | Tetsuya Sakurai | Tsuyoshi Miyazaki
Source: Physical review. B./Physical review. B (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system. Molecular dynamics ; Component (thermodynamics) ; Binary number ; Atom (system on chip) ; Statistical physics ; Space (punctuation) ; Locality ; Computer science ; Materials science ; Silicon ; Process (computing) ; Biological system ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Electron Microscopy Techniques and Applications
Authors: Tongan Yan | Zhiyuan Bi | Dahuan Liu | Xiaonan Zhang | Gang Lu | Qingyuan Yang
Source: The Journal of Physical Chemistry A (article, 2022-11-07)
Field: Chemistry — Inorganic Chemistry

A Self-Evolutionary Methodology for Reverse Design of Novel MOFs A combination of high-throughput molecular simulation and machine learning (ML) algorithms has been widely adopted to seek promising metal-organic frameworks (MOFs) as energy gas carriers. However, the currently reported studies are mainly limited to extracting top performers from existing databases, not fully unleashing the ML capabilities for intelligently predicting novel structures with better performance. Herein, an efficient self-evolutionary methodology was proposed for searching high-performance MOFs that are unstructured in the origin database, in which a Tangent Adaptive Genetic Algorithm (TAGA) was newly put forward for structural evolution and the high-precision ML model of eXtreme Gradient Boosting (XGBoost) was employed as the fitness function. By taking CH<sub>4</sub> storage in MOFs at room temperature as a showcase and using the database of 51,163 hMOFs, the TAGA-XGBoost coupling strategy rapidly suggested a certain number of possible combinations of the building blocks to form new structures with gravimetric storage capacity (35 bar) and volumetric working capacity (65-5.8 bar) higher than the best materials in the original database. The structures of some promising MOFs successfully used the finally optimized material genes for the two application conditions, and their performances were also confirmed by subsequent molecular simulations. The best materials can respectively reach a storage amount of 580 cm<sup>3</sup>(STP)/g at 35 bar and a working capacity of 218 cm<sup>3</sup>(STP)/cm<sup>3</sup> between 65 and 5.8 bar. An analysis of the top 100 materials predicted from our method revealed that the choice of organic linkers has a systematic effect on the storage performance of MOFs. It might be believed that the proposed methodology offers an opportunity to expedite the discovery of unprecedented materials for other practical applications. Computer science ; Genetic algorithm ; Bar (unit) ; Database ; Data mining ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Scott A. Southern | David L. Bryce
Source: Solid State Nuclear Magnetic Resonance (article, 2022-04-27)
Field: Chemistry — Physical and Theoretical Chemistry

To what extent do bond length and angle govern the 13C and 1H NMR response to weak CH⋯O hydrogen bonds? A case study of caffeine and theophylline cocrystals Chemical shift ; Chemistry ; Hydrogen bond ; Carbon-13 NMR ; Density functional theory ; Crystallography ; Nuclear magnetic resonance spectroscopy ; Heteronuclear molecule ; Computational chemistry ; Physical chemistry ; Stereochemistry ; Molecule ; Organic chemistry Crystallography and molecular interactions ; Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography
Authors: Carlos Perez | Laleh Avazpour | M. K. Eryilmaz | T. Earles | Steven Ruder | Venkatraman Gopalan | D. Botez | I. Knežević | Bladimir Ramos-Alvarado | Brian M. Foley | L. J. Mawst
Source: Applied Physics Letters (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

Incoherent-to-coherent crossover in thermal transport through III–V alloy superlattices We report on time-domain thermoreflectance measurements of cross-plane thermal conductivity of In0.63Ga0.37As/In0.37Al0.63As superlattices with interface densities ranging from 0.0374 to 2.19 nm−1 in the temperature range 80–295 K. The measurements are complemented by a three-dimensional finite-difference time-domain solution to the elastic wave equation, in which the rms roughness and correlation length at heterointerfaces are varied, and the parameters yielding best agreement with experiment are determined using machine learning. Both experimental measurements and simulations demonstrate the existence of a minimum in the cross-plane thermal conductivity as a function of interface density, which is evidence of a crossover from incoherent to coherent phonon transport as the interface density increases. This minimum persists with increasing temperature, indicating the continued dominance of the temperature-independent interface and alloy-disorder scattering over the temperature-dependent three-phonon scattering in thermal transport through III–V alloy superlattices. Superlattice ; Condensed matter physics ; Thermal conductivity ; Scattering ; Phonon ; Phonon scattering ; Materials science ; Atmospheric temperature range ; Crossover ; Surface finish ; Optics ; Physics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Hülya Aykaç Özen | Tuğba Mutuk | Merve Yiğiter
Source: Environmental Science and Pollution Research (article, 2022-08-04)
Field: Materials Science — Biomaterials

Smoke filtration performances of membranes produced from commercial PVA and recycled PET by electrospinning method and ANN modeling Membrane ; Filtration (mathematics) ; Materials science ; Smoke ; Waste management ; Electrospinning ; Pulp and paper industry ; Chemical engineering ; Composite material ; Chemistry ; Polymer ; Mathematics ; Engineering Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Microplastics and Plastic Pollution
Authors: Zixuan Wang | Yaxin Zhang | Runtao Tian | Zhigang Luo | Ruiping Zhang | Xin Li | Zeper Abliz
Source: Analytical Chemistry (article, 2022-09-29)
Field: Chemistry — Spectroscopy

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>-SNE of Mass Spectrometry Imaging Data Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue. Penumbra ; Mass spectrometry imaging ; Segmentation ; Artificial intelligence ; Robustness (evolution) ; Brain tissue ; Metabolomics ; Chemistry ; Pattern recognition (psychology) ; Computational biology ; Computer science ; Mass spectrometry ; Neuroscience ; Ischemia ; Biology ; Internal medicine ; Medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Poornima Hulipalled | Veerabhadrappa Algur | V. Lokesha
Source: Journal of Bio- and Tribo-Corrosion (article, 2022-05-08)
Field: Materials Science — Materials Chemistry

An Approach of Data Science for the Prediction of Wear Behaviour of Hypereutectoid Steel Support vector machine ; Random forest ; Kriging ; Hot work ; Mean squared error ; Relevance vector machine ; Gaussian process ; Gaussian ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Engineering ; Mathematics ; Metallurgy ; Statistics ; Tool steel Metal Alloys Wear and Properties ; Tunneling and Rock Mechanics ; Mechanical stress and fatigue analysis
Authors: Chayaphol Lortaraprasert | Junichiro Shiomi
Source: npj Computational Materials (article, 2022-10-25)
Field: Materials Science — Materials Chemistry

Robust combined modeling of crystalline and amorphous silicon grain boundary conductance by machine learning Abstract Knowledge in thermal and electric transport through grain boundary (GB) is crucial for designing nanostructured thermoelectric materials, where the transport greatly depends on GB atomistic structure. In this work, we employ machine learning (ML) techniques to study the relationship between silicon GB structure and its thermal and electric boundary conductance (TBC and EBC) calculated by Green’s function methods. We present a robust ML prediction model of TBC covering crystalline–crystalline and crystalline–amorphous interfaces, using disorder descriptors and atomic density. We also construct high-accuracy ML models for predicting both TBC and EBC and their ratio, using only small data of crystalline GBs. We found that the variations of interatomic angles and distance at GB are the most predictive descriptors for TBC and EBC, respectively. These results demonstrate the robustness of the black-box model and open the way to decouple thermal and electrical conductance, which is a key physical problem with engineering needs. Materials science ; Grain boundary ; Amorphous solid ; Crystalline silicon ; Conductance ; Amorphous silicon ; Silicon ; Robustness (evolution) ; Thermal conductivity ; Thermal ; Thermal conduction ; Condensed matter physics ; Optoelectronics ; Composite material ; Thermodynamics ; Crystallography ; Physics ; Chemistry ; Microstructure Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Haili Jia | Canhui Wang | Chao Wang | Paulette Clancy
Source: ACS Nano (article, 2022-12-20)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Approach to Enable Spectral Imaging Analysis for Particularly Complex Nanomaterial Systems Scanning transmission electron microscopy-based electron energy loss spectroscopy spectral imaging (STEM-EELS-SI) has been widely used in material research to capture a wealth of information, including elemental, electron density, and bonding state distributions. However, its exploitation still faces many challenges due to the difficulty of extracting information from noisy and overlapping edges in the convoluted spatial and spectroscopic data set. A traditional EELS spectral imaging analysis lacks the capability to isolate noise and deconvolute such overlapping edges, which either limits the resolution or the signal-to-noise ratio of the maps generated by EELS-SI. Existing machine learning (ML) algorithms can achieve denoising and deconvolution to a certain extent, but the extracted spectra lack physical meaning. To address these challenges, we have developed a ML method tailored to a spectral imaging analysis system and based on a non-negative robust principal component analysis. This approach offers an effective way to analyze EELS spectral images with improved space-time resolution, signal-to-noise ratio, and the capability to separate subtle differences in the spectrum. We apply this algorithm to 13 nanomaterial systems to show that ML can greatly improve image quality compared to a traditional approach, especially for more challenging systems. This will expand the type of nanomaterial systems that can be characterized by EELS-SI, and aid the analysis of structural, chemical, and electronic properties that are otherwise difficult to obtain. Deconvolution ; Computer science ; Noise (video) ; Spectral imaging ; Principal component analysis ; Image resolution ; Spectral resolution ; Artificial intelligence ; Resolution (logic) ; Electron energy loss spectroscopy ; Signal-to-noise ratio (imaging) ; Noise reduction ; Materials science ; Pattern recognition (psychology) ; Biological system ; Nanotechnology ; Optics ; Spectral line ; Algorithm ; Physics ; Image (mathematics) ; Transmission electron microscopy ; Telecommunications Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Iaroslav Gaponenko | S. Cherifi | Ulises Acevedo-Salas | Nazanin Bassiri‐Gharb | Patrycja Paruch
Source: Scientific Reports (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Correlative imaging of ferroelectric domain walls The wealth of properties in functional materials at the nanoscale has attracted tremendous interest over the last decades, spurring the development of ever more precise and ingenious characterization techniques. In ferroelectrics, for instance, scanning probe microscopy based techniques have been used in conjunction with advanced optical methods to probe the structure and properties of nanoscale domain walls, revealing complex behaviours such as chirality, electronic conduction or localised modulation of mechanical response. However, due to the different nature of the characterization methods, only limited and indirect correlation has been achieved between them, even when the same spatial areas were probed. Here, we propose a fast and unbiased analysis method for heterogeneous spatial data sets, enabling quantitative correlative multi-technique studies of functional materials. The method, based on a combination of data stacking, distortion correction, and machine learning, enables a precise mesoscale analysis. When applied to a data set containing scanning probe microscopy piezoresponse and second harmonic generation polarimetry measurements, our workflow reveals behaviours that could not be seen by usual manual analysis, and the origin of which is only explainable by using the quantitative correlation between the two data sets. Correlative ; Characterization (materials science) ; Computer science ; Nanoscopic scale ; Piezoresponse force microscopy ; Ferroelectricity ; Microscopy ; Workflow ; Scanning probe microscopy ; Materials science ; Pattern recognition (psychology) ; Nanotechnology ; Artificial intelligence ; Optics ; Physics ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications ; Photorefractive and Nonlinear Optics
Authors: Soumyashree S. Panda | Sumit Choudhary | Siddharth Joshi | Satinder K. Sharma | Ravi S. Hegde
Source: Optics Letters (article, 2022-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning approach for inverse design of metasurfaces with a wider shape gamut While the large design degrees of freedom (DOFs) give metasurfaces a tremendous versatility, they make the inverse design challenging. Metasurface designers mostly rely on simple shapes and ordered placements, which restricts the achievable performance. We report a deep learning based inverse design flow that enables a fuller exploitation of the meta-atom shape. Using a polygonal shape encoding that covers a broad gamut of lithographically realizable resonators, we demonstrate the inverse design of color filters in an amorphous silicon material platform. The inverse-designed transmission-mode color filter metasurfaces are experimentally realized and exhibit enhancement in the color gamut. Gamut ; Inverse ; Computer science ; Optics ; Color gel ; Inverse filter ; Color filter array ; Filter (signal processing) ; Computer vision ; Materials science ; Physics ; Mathematics ; Geometry ; Nanotechnology Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Mitsumasa Osada | Tamura Kotaro | Iori Shimada
Source: The Journal of Supercritical Fluids (article, 2022-09-02)
Field: Chemistry — Spectroscopy

Prediction of the solubility of organic compounds in high-temperature water using machine learning Solubility ; Lasso (programming language) ; Support vector machine ; Partial least squares regression ; Mean squared error ; Regression ; Least squares support vector machine ; Chemistry ; Artificial intelligence ; Machine learning ; Biological system ; Mathematics ; Computer science ; Organic chemistry ; Statistics Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Crystallization and Solubility Studies
Authors: Sidhartha Roy | Yen T. Nguyen | Christopher A. Neal | Stephen Baek | H. S. Udaykumar
Source: Journal of Applied Physics (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Meso-scale simulation of energetic materials. I. A method for generating synthetic microstructures using deep feature representations The response of a wide class of heterogeneous energetic materials (HEs) to loads is determined by dynamics at the meso-scale, i.e., by physicochemical processes in their underlying microstructure. Structure–property–performance (S–P–P) linkages for such materials can be developed in a multi-scale framework, connecting the physics and thermophysical properties at the meso-scale to response at the macro-scale. Due to the inherent stochasticity of the microstructure, ensembles of microstructures are required to conduct meso-scale simulations to establish S–P–P linkages. Here, a deep neural network-based method called deep feature representation is applied to generate a range of material microstructures from heterogeneous energetic materials to metal foams and metallic mixtures. The method allows for the generation of stochastic microstructures using a single real microstructure as the input and is not limited to low packing density or topological complexity of solids. In its application to pressed energetic materials, we show that qualitative and quantitative features of real (i.e., imaged) microstructures are captured in the synthetic microstructures. Therefore, a stochastic ensemble of synthetic microstructures can be created for use in reactive meso-scale simulations to relate the microstructures of HEs to their performance. While the focus is on pressed HE microstructures, we also show that the method is general and useful for generating microstructures for in silico experiments for a wide range of composite/multiphase materials, which can be used to establish S–P–P linkages. Microstructure ; Materials science ; Scale (ratio) ; Macroscopic scale ; Representation (politics) ; Length scale ; Feature (linguistics) ; Statistical physics ; Characterization (materials science) ; Biological system ; Nanotechnology ; Computer science ; Physics ; Composite material ; Mechanics Machine Learning in Materials Science ; Energetic Materials and Combustion ; Crystallography and molecular interactions