jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ri‐Na Dao | Limei Qi | Kai‐Xiang Hu | Junli Yang | Zi‐Yu Liu | Liqin Wu
Source: Microwave and Optical Technology Letters (article, 2022-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The reverse design of a tunable terahertz metasurface antenna based on a deep neural network Abstract The reverse design of a tunable terahertz metasurface antenna is proposed based on the deep neural network (DNN). To obtain the tunable properties of the terahertz antenna, the phase‐changed material vanadium dioxide (VO 2 ) is introduced by controlling the voltage without changing the structures of the metasurface antenna. To improve the efficiency and accuracy of the terahertz antenna, the DNN is used to establish the relationship of amplitude and phase for the antenna unit. The prediction errors are below 10%. The radiation direction of the antenna can be changed from 0° to 52° by the VO 2 with different states. The prediction error of angle is only 1° difference from the theoretical calculation. This reverse design method can be extended to other metasurface devices with improved efficiency and accuracy. Terahertz radiation ; Antenna (radio) ; Antenna efficiency ; Phase (matter) ; Radiation pattern ; Optoelectronics ; Optics ; Materials science ; Electronic engineering ; Computer science ; Physics ; Engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Sara Rejeb | Catherine Duveau | Tabea Rebafka
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-10-03)
Field: Chemistry — Analytical Chemistry

Self-organizing maps for exploration of partially observed data and imputation of missing values Self-organizing map ; Missing data ; Computer science ; Imputation (statistics) ; Cluster analysis ; Data mining ; Estimator ; Artificial neural network ; Visualization ; Field (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Alireza Sadri | Marjan Hadian-Jazi | Oleksandr Yefanov | M. Galchenkova | Henry Kirkwood | Grant Mills | Marcin Sikorski | Romain Letrun | Raphaël de Wijn | Mohammad Vakili | D. Oberthüer | Dana Komadina | W. Brehm | Adrian P. Mancuso⋈ | Jérôme Carnis | Luca Gelisio | Henry N. Chapman
Source: Journal of Applied Crystallography (article, 2022-10-25)
Field: Materials Science — Materials Chemistry

Automatic bad-pixel mask maker for X-ray pixel detectors with application to serial crystallography X-ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X-ray sources and enabled by employing high-frame-rate X-ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad-pixel masks for large-area X-ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X-ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets. Pixel ; Detector ; Computer science ; Software ; Brightness ; Frame (networking) ; Artificial intelligence ; Noise (video) ; Algorithm ; Optics ; Diffraction ; Computer vision ; Physics ; Image (mathematics) Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques
Authors: Ioannis Kalogeris | Stefanos Pyrialakos | Odysseas Kokkinos | Vissarion Papadopoulos
Source: Engineering With Computers (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Stochastic optimization of carbon nanotube reinforced concrete for enhanced structural performance Micromechanics ; Microscale chemistry ; Carbon nanotube ; Materials science ; Optimization problem ; Structural engineering ; Mortar ; Nonlinear system ; Composite number ; Composite material ; Computer science ; Mathematical optimization ; Mathematics ; Engineering ; Physics Carbon Nanotubes in Composites ; Anodic Oxide Films and Nanostructures ; Innovative concrete reinforcement materials
Authors: Thomas Dufils | Christoph Schran | Ji Chen | A. K. Geǐm | Laura Fumagalli | Angelos Michaelides
Source: arXiv (Cornell University) (preprint, 2022-11-25)
Field: Chemistry — Physical and Theoretical Chemistry

Understanding the anomalously low dielectric constant of confined water: an ab initio study Recent experiments have shown that the out-of-plane dielectric constant of water confined in nanoslits of graphite and hexagonal boron nitride (hBN) is vanishingly small. Despite extensive effort based mainly on classical force-field molecular dynamics (FFMD) approaches, the origin of this phenomenon is under debate. Here we used ab initio molecular dynamics simulations (AIMD) and AIMD-trained machine learning potentials to explore the structure and electronic properties of water confined inside graphene and hBN slits. We found that the reduced dielectric constant arises mainly from the anti-parallel alignment of the water dipoles in the perpendicular direction to the surface in the first two water layers near the solid interface. Although the water molecules retain liquid-like mobility, the interfacial layers exhibit a net ferroelectric ordering and constrained hydrogen-bonding orientations which lead to much reduced polarization fluctuations in the out-of-plane direction at room temperature. Importantly, we show that this effect is independent of the distance between the two confining surfaces of the slit, and it originates in the spontaneous polarization of interfacial water. Our calculations also show no significant variations in the structure and polarization of water near graphene and hBN, despite their different electronic structures. These results are important as they offer new insight into a property of water that plays a critical role in the long-range interactions between surfaces, the electric double-layer formation, ion solvation and transport, as well as biomolecular functioning. Dielectric ; Chemical physics ; Dipole ; Graphene ; Materials science ; Ab initio ; Polarization (electrochemistry) ; Molecular dynamics ; Condensed matter physics ; Water model ; Perpendicular ; Electric field ; Molecular physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Optoelectronics ; Physics ; Physical chemistry Electrostatics and Colloid Interactions ; Nanopore and Nanochannel Transport Studies ; Geophysical and Geoelectrical Methods
Authors: Qifang Xu | Roland L. Dunbrack
Source: Nucleic Acids Research (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

The protein common assembly database (ProtCAD)—a comprehensive structural resource of protein complexes Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad). Protein Data Bank (RCSB PDB) ; UniProt ; Protein Data Bank ; Biology ; Database ; Protein structure ; Computational biology ; Identifier ; Crystallography ; Computer science ; Bioinformatics ; Biochemistry ; Chemistry ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Mütesir Temel | Ömer Tayfuroğlu | Abdulkadır Kocak
Source: Journal of Computational Chemistry (article, 2022-11-02)
Field: Materials Science — Materials Chemistry

The performance of <scp>ANI‐ML</scp> potentials for ligand‐n(<scp>H<sub>2</sub>O</scp>) interaction energies and estimation of hydration free energies from end‐point <scp>MD</scp> simulations Here, we investigate the performance of "Accurate NeurAl networK engINe for Molecular Energies" (ANI), trained on small organic compounds, on bulk systems including non-covalent interactions and applicability to estimate solvation (hydration) free energies using the interaction between the ligand and explicit solvent (water) from single-step MD simulations. The method is adopted from ANI using the Atomic Simulation Environment (ASE) and predicts the non-covalent interaction energies at the accuracy of wb97x/6-31G(d) level by a simple linear scaling for the conformations sampled by molecular dynamics (MD) simulations of ligand-n(H<sub>2</sub> O) systems. For the first time, we test ANI potentials' abilities to reproduce solvation free energies using linear interaction energy (LIE) formulism by modifying the original LIE equation. Our results on ~250 different complexes show that the method can be accurate and have a correlation of R<sup>2</sup> = 0.88-0.89 (MAE <1.0 kcal/mol) to the experimental solvation free energies, outperforming current end-state methods. Moreover, it is competitive to other conventional free energy methods such as FEP and BAR with 15-20 × fold reduced computational cost. Solvation ; Chemistry ; Ligand (biochemistry) ; Molecular dynamics ; Covalent bond ; Interaction energy ; Computational chemistry ; Thermodynamics ; Solvent ; Molecule ; Physics ; Receptor Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Simin Tazikeh | Abdollah Davoudi | Ali Shafiei | Hossein Parsaei | Timur Sh. Atabaev | Oleksandr P. Ivakhnenko
Source: ACS Omega (article, 2022-08-16)
Field: Chemistry — Analytical Chemistry

A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas-oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (<i>R</i> <sup>2</sup>: 0.793, RMSE: 7.5) and the SVM models (<i>R</i> <sup>2</sup>: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements. Equation of state ; Support vector machine ; Bubble point ; Mean squared error ; Data point ; Volume (thermodynamics) ; Chemistry ; Bubble ; Mathematics ; Computer science ; Thermodynamics ; Algorithm ; Machine learning ; Statistics ; Physics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Lars Gerdes | Sebastian Berger | Jannis Saelzer | Pascal Franck | Ramon Helwing | Andreas Zabel | Frank Walther
Source: Applied Mechanics (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Application-Oriented Digital Image Correlation for the High-Speed Deformation and Fracture Analysis of AISI 1045 and Ti6Al4V Materials In order to achieve realistic simulations of the chip formation, high quality input data regarding the flow stress and damage behavior of the materials are required. The split Hopkinson pressure bar (SHPB) test setup for the characterization of highly dynamic material properties offers a suitable method for generating high strain rates, similar to those in the chip formation zone. However, the strain measurement in SHPB is usually performed by means of strain gauges. This leads to an unreliable evaluation of strain rate and flow stress/shear flow stress in the case of an inhomogeneous material deformation, since this method presents the total strain whilst excluding local deformations. Inhomogeneous deformations are induced deliberately in special shear specimens, as they are also observed in the investigated cylindrical specimens. The present work deals with this issue by providing two additional measurement techniques, which are applied in SHPB tests for cylindrical specimens made of AISI 1045 and Ti6Al4V. To enable a local strain resolution, digital image correlation (DIC) is applied to high-speed images of the deformation process. In order to allow for the detection of shear bands in the specimens, a deep-learning-based approach is presented. The two measurement methods (strain gauges and DIC) are compared and discussed. In particular, the findings regarding the inhomogeneous deformation of Ti6Al4V allow for future improvements in the result quality of SHPB tests. The presented algorithm shows promising predictions for shear band detection and creates the basis for an automated evaluation of shear sample results, as well as an AI-based pre-selection of frames for the DIC evaluation of SHPB tests. Digital image correlation ; Split-Hopkinson pressure bar ; Materials science ; Deformation (meteorology) ; Shear band ; Titanium alloy ; Shear (geology) ; Shear stress ; Strain rate ; Flow stress ; Direct shear test ; Strain gauge ; Digital image ; Composite material ; Computer science ; Image processing ; Artificial intelligence ; Image (mathematics) High-Velocity Impact and Material Behavior ; Advanced Surface Polishing Techniques ; Metal and Thin Film Mechanics
Authors: Sebastian Ehlert | Stefan Grimme | Andreas Hansen
Source: ChemRxiv (preprint, 2022-05-10)
Field: Materials Science — Materials Chemistry

Conformational energy benchmark for longer n-alkane chains We present the first benchmark set focusing on the conformational energies of highly flexible, long n-alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g, complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced r²SCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and r²SCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wavefunction methods, where MP2/CBS surprisingly performs worse compared to simple D4 dispersion corrected Hartree–Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parameterization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods. Benchmark (surveying) ; Statistical physics ; Computer science ; Hybrid functional ; Force field (fiction) ; Density functional theory ; Set (abstract data type) ; Dispersion (optics) ; Field (mathematics) ; Wave function ; Alkane ; Scale (ratio) ; Basis set ; Algorithm ; Physics ; Computational chemistry ; Artificial intelligence ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: Turar Yerkenov | Simin Tazikeh | Afshin Tatar | Ali Shafiei
Source: ACS Omega (article, 2022-09-09)
Field: Chemistry — Analytical Chemistry

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data pre-processing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C<sub>5</sub> asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery. Support vector machine ; Asphaltene ; Asphalt ; Computer science ; Artificial intelligence ; Population ; Machine learning ; Materials science ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Yujia Zhong | Haoyu Fang | Yutong Ran | Hongwei Zhu
Source: InfoMat (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Fast optical‐writing recognition based on two‐dimensional photothermoelectric effect assisted with deep learning Abstract The photothermoelectric (PTE) effect enables a simple structure to construct a noncontact and self‐powered writing device. PTE detectors based on active materials and one‐dimensional (1D) position‐sensitive PTE effect have attracted considerable attentions. However, there is no research on applying the 1D position‐sensitive PTE effect to a two‐dimensional (2D) surface. Besides, flexible writing devices based on the PTE effect have not been reported. Here, we demonstrate a self‐powered flexible optical writing device based on the 2D position‐sensitive PTE effect of SnSe thin film. Laser positioning, tracking, and distinguishing of optically written characters have been realized by only measuring the bi‐directional output voltages during the writing process. A deep convolutional neural network has been introduced to recognize optically written numbers and characters with high recognition accuracy (92%) and fast processing speed (8.6 ms). The flexible optical‐writing device with a simple structure could rapidly and accurately recognize characters in a noncontact fashion using a small amount of training data. image Computer science ; Convolutional neural network ; Construct (python library) ; Position (finance) ; Artificial intelligence ; Tracking (education) ; Process (computing) ; Simple (philosophy) ; Laser ; Computer hardware ; Computer vision ; Materials science ; Pattern recognition (psychology) ; Optics ; Physics ; Psychology Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Xianlan Li | Yue Luan | Yanhua Lu | Wei Li | Lihong Ma | Qingyou Zhang | Aimin Pang
Source: Chemical Research in Chinese Universities (article, 2022-10-10)
Field: Chemistry — Organic Chemistry

Estimation of Enthalpy of Formation Using Benson’s Group Addition and Functional Group Correction Group (periodic table) ; Molecule ; Chemistry ; Hydrogen bond ; Atom (system on chip) ; Group contribution method ; Partial least squares regression ; Enthalpy ; Set (abstract data type) ; Test set ; Function (biology) ; Mathematics ; Thermodynamics ; Computer science ; Statistics ; Organic chemistry ; Physics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods