jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: İlker Demiroğlu | Yenal Karaaslan | Tuğbey Kocabaş | Murat Keçeli | Álvaro Vázquez‐Mayagoitia | Cem Sevik
Source: The Journal of Physical Chemistry C (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics. Gaussian ; Density functional theory ; Statistical physics ; Molecular dynamics ; Graphene ; Ab initio ; Thermal expansion ; Phonon ; Computation ; Interatomic potential ; Ab initio quantum chemistry methods ; Physics ; Materials science ; Computer science ; Algorithm ; Quantum mechanics ; Thermodynamics ; Molecule Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Junde Li | Rasit Onur Topaloglu | Swaroop Ghosh
Source: arXiv (Cornell University) (preprint, 2021-01-09)
Field: Materials Science — Materials Chemistry

Quantum Generative Models for Small Molecule Drug Discovery Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on the order of 1060 . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from curse-of-dimensionality. A full quantum GAN may require more than 90 qubits even to generate QM9-like small molecules. We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN. The QGANHG model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and, a classical discriminator. QGAN-HG with only 14.93% retained parameters can learn molecular distribution as efficiently as classical counterpart. The QGAN-HG variation with patched circuits considerably accelerates our standard QGANHG training process and avoids potential gradient vanishing issue of deep neural networks. Code is available on GitHub https://github.com/jundeli/quantum-gan. Computer science ; Qubit ; Chemical space ; Theoretical computer science ; Quantum ; Generative model ; Representation (politics) ; Drug discovery ; Biological system ; Topology (electrical circuits) ; Artificial intelligence ; Generative grammar ; Quantum mechanics ; Physics ; Mathematics ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jianxun Yang | Qian Du | Rongfu Ma | Afrasyab Khan
Source: Journal of Molecular Liquids (article, 2021-08-19)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite Adsorption ; Nanocomposite ; Artificial neural network ; Materials science ; Hydroxide ; Chemical engineering ; Process engineering ; Computer science ; Biological system ; Chromatography ; Chemistry ; Nanotechnology ; Organic chemistry ; Artificial intelligence ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Magnesium Oxide Properties and Applications
Authors: Tennant A | Samarakoon A
Source: ePubs (Science and Technology Facilities Council, Research Councils UK) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Magnetic Phase Diagrams and Inverse Scattering Problems Abstract Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo methods. These approaches are shown to be effective at simulating magnetic structures and dynamics in a wide range of materials. Using large numbers of simulations the effectiveness of machine learning approaches are assessed. Principal component analysis and nonlinear autoencoders are considered with the latter found to provide a high degree of compression and to be highly suited to neutron scattering problems. Agglomerative heirarchical clustering in the latent space is shown to be effective at extracting phase diagrams of behavior and features in an automated way that aid understanding and interpretation. The autoencoders are also well suited to optimizing model parameters and were found to be highly advantageous over conventional fitting approaches including being tolerant of artifacts in untreated data. The potential of machine learning to automate complex data analysis tasks including the inversion of neutron scattering data into models and the processing of large volumes of multidimensional data is assessed. Directions for future developments are considered and machine learning argued to have high potential for impact on neutron science generally. Computer science ; Neutron scattering ; Machine learning ; Artificial intelligence ; Artificial neural network ; Monte Carlo method ; Scattering ; Cluster analysis ; Physics ; Mathematics ; Optics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Andrew Falkowski | Steven K. Kauwe | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Optimizing Fractional Compositions to Achieve Extraordinary Properties Property (philosophy) ; Computer science ; Element (criminal law) ; Inverse ; Focus (optics) ; Function (biology) ; Resolution (logic) ; Artificial neural network ; Mathematical optimization ; Algorithm ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Edgar Miguel García-Carrillo | Adriana B. Espinoza‐Martínez | L.F. Ramos de Valle | S. Sánchez‐Valdés
Source: Computational Materials Science (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Simultaneous optimization of thermal and electrical conductivity of high density polyethylene-carbon particle composites by artificial neural networks and multi-objective genetic algorithm Materials science ; Thermal conductivity ; Composite material ; High-density polyethylene ; Particle swarm optimization ; Genetic algorithm ; Artificial neural network ; Electrical resistivity and conductivity ; Algorithm ; Polyethylene ; Computer science ; Machine learning Thermal properties of materials ; Heat Transfer and Optimization ; Smart Materials for Construction
Authors: Keita Kobayashi | Yuki Nagai | Mitsuhiro Itakura | Motoyuki Shiga
Source: The Journal of Chemical Physics (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Self-learning hybrid Monte Carlo method for isothermal–isobaric ensemble: Application to liquid silica Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report [Nagai et al., Phys. Rev. B 102, 041124(R) (2020)], the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy x-ray data. Isobaric process ; Monte Carlo method ; Statistical physics ; Isothermal process ; Canonical ensemble ; Statistical ensemble ; Grand canonical ensemble ; Molecular dynamics ; Sampling (signal processing) ; Microcanonical ensemble ; Physics ; Thermodynamics ; Mathematics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; High-pressure geophysics and materials
Authors: José Marcial | Yanwen Zhang | Xiaodong Zhao | Hongwu Xu | Adel Mesbah | Emily T. Nienhuis | Stéphanie Szenknect | Jöerg C. Neuefeind | Jian Lin | Liang Qi | Artas Migdisov | Rodney C. Ewing | Nicolas Dacheux | John S. McCloy | Xiaofeng Guo
Source: npj Materials Degradation (article, 2021-06-18)
Field: Chemistry — Inorganic Chemistry

Thermodynamic non-ideality and disorder heterogeneity in actinide silicate solid solutions Abstract Non-ideal thermodynamics of solid solutions can greatly impact materials degradation behavior. We have investigated an actinide silicate solid solution system (USiO 4 –ThSiO 4 ), demonstrating that thermodynamic non-ideality follows a distinctive, atomic-scale disordering process, which is usually considered as a random distribution. Neutron total scattering implemented by pair distribution function analysis confirmed a random distribution model for U and Th in first three coordination shells; however, a machine-learning algorithm suggested heterogeneous U and Th clusters at nanoscale (~2 nm). The local disorder and nanosized heterogeneous is an example of the non-ideality of mixing that has an electronic origin. Partial covalency from the U/Th 5 f –O 2 p hybridization promotes electron transfer during mixing and leads to local polyhedral distortions. The electronic origin accounts for the strong non-ideality in thermodynamic parameters that extends the stability field of the actinide silicates in nature and under typical nuclear waste repository conditions. Actinide ; Solid solution ; Mixing (physics) ; Silicate ; Thermodynamics ; Chemical stability ; Pair distribution function ; Nanoscopic scale ; Chemistry ; Materials science ; Chemical physics ; Physics ; Nanotechnology ; Inorganic chemistry Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Eric Morelle | Alexander Rudolph | Christopher McHardy | Cornelia Rauh
Source: Food and Bioproducts Processing (article, 2021-04-03)
Field: Materials Science — Materials Chemistry

Detection and prediction of foam evolution during the bottling of noncarbonated beverages using artificial neural networks Bottling line ; Convolutional neural network ; Computer science ; Process (computing) ; Artificial neural network ; Artificial intelligence ; Process engineering ; Viscosity ; Materials science ; Composite material ; Bottle ; Engineering Pickering emulsions and particle stabilization ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Sodabeh Ebrahimpoor | Vahid Kiarostami | Morteza Khosravi | Mehran Davallo | A.M. Ghaedi
Source: Fibers and Polymers (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Optimization of Tartrazine Adsorption onto Polypyrrole/SrFe12O19/Graphene Oxide Nanocomposite Using Central Composite Design and Bat Inspired Algorithm with the Aid of Artificial Neural Networks Tartrazine ; Adsorption ; Materials science ; Polypyrrole ; Nanocomposite ; Central composite design ; Langmuir adsorption model ; Graphene ; Aqueous solution ; Oxide ; Chemical engineering ; Composite number ; Langmuir ; Endothermic process ; Polymerization ; Composite material ; Response surface methodology ; Nanotechnology ; Polymer ; Chromatography ; Organic chemistry ; Chemistry Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Hiroto Yamashita | Rei Sonobe | Yuhei Hirono | Akio Morita | Takashi Ikka
Source: Scientific Reports (article, 2021-02-18)
Field: Chemistry — Analytical Chemistry

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy. Hyperspectral imaging ; Reflectivity ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Regression ; Biological system ; Mathematics ; Chemistry ; Statistics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Johanna C. Clauser | Judith Maas | Jutta Arens | Thomas Schmitz‐Rode | Ulrich Steinseifer | Benjamin Berkels
Source: ACS Biomaterials Science & Engineering (article, 2021-01-22)
Field: Materials Science — Surfaces, Coatings and Films

Hemocompatibility Evaluation of Biomaterials—The Crucial Impact of Analyzed Area The hemocompatibility of blood-contacting medical devices remains one of the major challenges in medical device development. A common tool for the analysis of adherent and activated platelets on materials following in vitro tests is microscopy. Currently, most researchers develop their own routines, resulting in numerous different methods that are applied. The majority of those (semi-)manual methods analyze only a very small fraction of the material surface (<1%), which neglects the inhomogeneity of platelet distribution and makes results hardly comparable. Within this study, we examined the relation between the fraction of analyzed sample area and the platelet adhesion result. By means of image segmentation and machine learning algorithms, 103 100 microscopy images were analyzed automatically. We discovered a crucial impact of the analyzed surface fraction and thus a misrepresentation of a surface's platelet adhesion unless up to 40% of the sample surface is analyzed. These findings underline the necessity of standardization in the field of in vitro hemocompatibility tests and analyses in particular and provide a first basis to make future tests more reliable and comparable. Biomedical engineering ; Platelet adhesion ; Standardization ; Sample (material) ; Materials science ; Adhesion ; Computer science ; Platelet ; Misrepresentation ; Fraction (chemistry) ; Nanotechnology ; Composite material ; Chemistry ; Medicine ; Chromatography ; Immunology Polymer Surface Interaction Studies ; 3D Printing in Biomedical Research ; Blood properties and coagulation
Authors: B.S. Reddy | A.K. Maurya | P.L. Narayana | S. K. Khadheer Pasha | M.R. Reddy | Mohammad Rafe Hatshan | Noura Darwish | S.A. Kori | Kwon‐Koo Cho | N.S. Reddy
Source: Environmental Research (article, 2021-11-11)
Field: Materials Science — Materials Chemistry

Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks Extraction (chemistry) ; Artificial neural network ; Chemistry ; Chromatography ; Artificial intelligence ; Computer science Ultrasound and Cavitation Phenomena ; Water Quality Monitoring and Analysis ; Water Quality Monitoring Technologies
Authors: Yangyang Ma | Yongjian Li | Shuaichao Yue | He Sun | Ming Yang
Source: Journal of Magnetism and Magnetic Materials (article, 2021-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid intelligent hysteresis model based on DBN-DNN algorithm and fusion Preisach operator Hysteresis ; Operator (biology) ; Computer science ; Nonlinear system ; Artificial neural network ; Algorithm ; Sensor fusion ; Artificial intelligence ; Physics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Piezoelectric Actuators and Control
Authors: Johann Lüder
Source: Physical review. B./Physical review. B (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Determining electronic properties from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>L</mml:mi></mml:math>-edge x-ray absorption spectra of transition metal compounds with artificial neural networks X-ray absorption spectroscopy at the $L$-edge probes transitions of $2p$ electrons into unoccupied $d$ states. Applied to transition metal atoms, this experimental technique can provide valuable information about the electronic structure of $d$ states. However, multiplet effects, spin-orbit coupling, and a large number of possible transitions can cause a rather involved nature of $2p$ XAS spectra, which can often complicate extracting of information directly from them. Here, artificial neural networks trained on simulated spectra of a $2p$ XAS model Hamiltonian are presented that can directly determine information about atomic properties and the electronic configuration of $d$ states from $L$-edge x-ray absorption spectra. Moreover, the adaptable nature of artificial neural networks (ANNs) allows extending their capability to obtain information about the electronic ground state and core hole lifetimes from $2p$ XAS spectra as well as to incorporate external factors, such as temperature and experimental convolution that can affect details in spectral features. The effects of noise and background contributions in spectra on the accuracy of ANNs are discussed and the method is validated on experimental spectra of transition metal compounds, including metal-organic molecules and metal oxides. Multiplet ; X-ray absorption spectroscopy ; Spectral line ; Electronic structure ; Absorption spectroscopy ; Metal K-edge ; Transition metal ; Metal L-edge ; Electron configuration ; Physics ; Atomic physics ; Materials science ; Electron ; Chemistry ; Condensed matter physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Navid Kardani | T. Pradeep | Pijush Samui | Dookie Kim | Annan Zhou
Source: Petroleum Science and Technology (article, 2021-09-16)
Field: Chemistry — Analytical Chemistry

Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR Asphaltene precipitation is the reason behind some of the most destructive issues in the oil industry such as wettability alteration, formation plugging, and reduction of the relative permeability. There are different experimental and modeling methods to study the asphaltene phase behavior. However, those approaches are either time-consuming or costly. In addition, the prediction of asphaltene precipitation is challenging due to the nonlinear dependence. Therefore, in this study, three advanced computational algorithms including group method of data handling (GMDH), emotional neural network (ENN), and minimax probability machine regression (MPMR) are developed and applied to the comprehensive dataset to estimate the amount of asphaltene precipitation as the function of temperature, dilution ratio, and the molecular weight of the n-alkanes. Many different performance metrics are used to evaluate the predictive performance of intelligent algorithms. Obtained results of the modeling reveal that intelligent computational algorithms have a great ability to mimic the nonlinear relationships between the target variable and its influential variables. The results indicate MPMR as the best predictive model with the highest R-squared value on both training and testing datasets with values of R2 = 0.992 and R2 = 0.991, respectively. In addition, MPMR is compared and outperformed empirical correlations. Asphaltene ; Computer science ; Nonlinear system ; Artificial neural network ; Artificial intelligence ; Machine learning ; Data mining ; Chemical engineering ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Heat transfer and supercritical fluids