jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel J. Fowles | David Scott Palmer
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Solvation entropy, enthalpy and free energy prediction using a multi-task deep learning functional in 1D-RISM Simultaneous calculation of entropies, enthalpies and free energies has been a long-standing challenge in computational chemistry, partly because of the difficulty in obtaining estimates of all three properties from a single consistent simulation methodology. This has been particularly true for methods from the Integral Equation Theory of Molecular Liquids such as the Reference Interaction Site Model which have traditionally given large errors in solvation thermodynamics. Recently, we presented pyRISM-CNN, a combination of the 1 Dimensional Reference Interaction Site Model (1D-RISM) solver, pyRISM, with a deep learning based free energy functional, as a method of predicting solvation free energy (SFE). With this approach, a 40-fold improvement in prediction accuracy was delivered for a multi-solvent, multi-temperature dataset when compared to the standard 1D-RISM theory [Fowles <i>et al., Digital Discovery</i>, 2023, <b>2</b>, 177-188]. Here, we report three further developments to the pyRISM-CNN methodology. Firstly, solvation free energies have been introduced for organic molecular ions in methanol or water solvent systems at 298 K, with errors below 4 kcal mol<sup>-1</sup> obtained without the need for corrections or additional descriptors. Secondly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. For neutral solutes, prediction errors nearing or below 1 kcal mol<sup>-1</sup> are obtained for each organic solvent system at 298 K and water solvent systems at 273-373 K. Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal mol<sup>-1</sup>, respectively, for water solvent systems at 298 K. Solvation ; Enthalpy ; Thermodynamics ; Entropy (arrow of time) ; Chemistry ; Statistical physics ; Computational chemistry ; Physics ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yipin Si | Yin Zhang | Dengke Chen | J.L. Wormald | Benjamin Anglin | David L. McDowell | Ting Zhu
Source: Journal of the Mechanics and Physics of Solids (article, 2023-06-11)
Field: Materials Science — Materials Chemistry

Atomistic determination of Peierls barriers of dislocation glide in nickel Peierls stress ; Materials science ; Dislocation ; Condensed matter physics ; Glide plane ; Lattice (music) ; Shear (geology) ; Shear stress ; Dislocation creep ; Physics ; Composite material Nuclear Materials and Properties ; Microstructure and mechanical properties ; High Temperature Alloys and Creep
Authors: Egor S. Rodionov | Victor V. Pogorelko | Victor G. Lupanov | Polina N. Mayer | Alexander E. Mayer
Source: Materials (article, 2023-08-12)
Field: Materials Science — Materials Chemistry

Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model Current progress in numerical simulations and machine learning allows one to apply complex loading conditions for the identification of parameters in plasticity models. This possibility expands the spectrum of examined deformed states and makes the identified model more consistent with engineering practice. A combined experimental-numerical approach to identify the model parameters and study the dynamic plasticity of metals is developed and applied to the case of cold-rolled OFHC copper. In the experimental part, profiled projectiles (reduced cylinders or cones in the head part) are proposed for the Taylor impact problem for the first time for material characterization. These projectiles allow us to reach large plastic deformations with true strains up to 1.3 at strain rates up to 10<sup>5</sup> s<sup>-1</sup> at impact velocities below 130 m/s. The experimental results are used for the optimization of parameters of the dislocation plasticity model implemented in 3D with the numerical scheme of smoothed particle hydrodynamics (SPH). A Bayesian statistical method in combination with a trained artificial neural network as an SPH emulator is applied to optimize the parameters of the dislocation plasticity model. It is shown that classical Taylor cylinders are not enough for a univocal selection of the model parameters, while the profiled cylinders provide better optimization even if used separately. The combination of different shapes and an increase in the number of experiments increase the quality of optimization. The optimized numerical model is successfully validated by the experimental data about the shock wave profiles in flyer plate experiments from the literature. In total, a cheap, simple, but efficient route for optimizing a dynamic plasticity model is proposed. The dislocation plasticity model is extended to estimate grain refinement and volume fractions of weakened areas in comparison with experimental observations. Plasticity ; Projectile ; Dislocation ; Mechanics ; Structural engineering ; Shock (circulatory) ; Materials science ; Experimental data ; Artificial neural network ; Computer simulation ; Computer science ; Engineering ; Mathematics ; Physics ; Composite material ; Artificial intelligence High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Microstructure and mechanical properties
Authors: Emanuele Telari | Antonio Tinti | Manoj Settem | Luca Maragliano | Riccardo Ferrando | Alberto Giacomello
Source: ACS Nano (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Charting Nanocluster Structures via Convolutional Neural Networks A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes. Nanoclusters ; Computer science ; Cluster analysis ; Maxima and minima ; Structural motif ; Convolutional neural network ; Representation (politics) ; Differentiable function ; Set (abstract data type) ; Biological system ; Materials science ; Statistical physics ; Artificial intelligence ; Nanotechnology ; Physics ; Mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Computational Drug Discovery Methods
Authors: Pier Paolo Poier | Olivier Adjoua | Louis Lagardère | Jean‐Philip Piquemal
Source: The Journal of Physical Chemistry Letters (article, 2023-02-07)
Field: Chemistry — Spectroscopy

Generalized Many-Body Dispersion Correction through Random-Phase Approximation for Chemically Accurate Density Functional Theory We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (<i>Q</i>) terms using a generalized Random Phase Approximation (RPA) formalism, thus enabling the inclusion of van der Waals contributions beyond dipole. The resulting DNN-MBDQ model only relies on <i>ab initio</i>-derived quantities as the introduced quadrupole polarizabilities are recursively retrieved from dipole ones, in turn modeled via the Tkatchenko-Scheffler method. A transferable and efficient deep-neuronal network (DNN) provides atom-in-molecule volumes, while a single range-separation parameter is used to couple the model to Density Functional Theory (DFT). Since it can be computed at a negligible cost, the DNN-MBDQ approach can be coupled with DFT functionals, such as PBE, PBE0, and B86bPBE (dispersionless). The DNN-MBQ-corrected functionals reach chemical accuracy while exhibiting lower errors compared to their dipole-only counterparts. Random phase approximation ; Polarizability ; Density functional theory ; Dipole ; Quadrupole ; van der Waals force ; Discrete dipole approximation ; Ab initio ; Statistical physics ; Physics ; Quantum mechanics ; Molecular physics ; Molecule Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Taoyuze Lv | Zhicheng Zhong | Yuhang Liang | Feng Li | Jun Huang | Rongkun Zheng
Source: Physical review. B./Physical review. B (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Deep Charge: Deep learning model of electron density from a one-shot density functional theory calculation Electron charge density is a fundamental physical quantity, determining various properties of matter. In this study, we have proposed a deep learning model for accurate charge-density prediction. Our model naturally preserves physical symmetries and can be effectively trained from one-shot density functional theory calculation toward high accuracy. It captures detailed atomic environment information, ensuring accurate predictions of charge density across bulk, surface, molecules, and amorphous structures. This implementation exhibits excellent scalability and provides efficient analyses of material properties in large-scale condensed matter systems. Density functional theory ; Charge (physics) ; Charge density ; Warm dense matter ; Electron ; Physics ; Scalability ; Statistical physics ; Electron density ; Computational physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Zhidan Lei | Yi-Duo Xu | Cheng Lei | Yan Zhao | Du Wang
Source: Photonics Research (article, 2023-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic multifunctional metasurfaces: an inverse design deep learning approach Optical metasurfaces (OMs) offer unprecedented control over electromagnetic waves, enabling advanced optical multiplexing. The emergence of deep learning has opened new avenues for designing OMs. However, existing deep learning methods for OMs primarily focus on forward design, which limits their design capabilities, lacks global optimization, and relies on prior knowledge. Additionally, most OMs are static, with fixed functionalities once processed. To overcome these limitations, we propose an inverse design deep learning method for dynamic OMs. Our approach comprises a forward prediction network and an inverse retrieval network. The forward prediction network establishes a mapping between meta-unit structure parameters and reflectance spectra. The inverse retrieval network generates a library of meta-unit structure parameters based on target requirements, enabling end-to-end design of OMs. By incorporating the dynamic tunability of the phase change material Sb 2 Te 3 with inverse design deep learning, we achieve the design and verification of dynamic multifunctional OMs. Our results demonstrate OMs with multiple information channels and encryption capabilities that can realize multiple physical field optical modulation functions. When Sb 2 Te 3 is in the amorphous state, near-field nano-printing based on meta-unit amplitude modulation is achieved for X -polarized incident light, while holographic imaging based on meta-unit phase modulation is realized for circularly polarized light. In the crystalline state, the encrypted information remains secure even with the correct polarization input, achieving double encryption. This research points towards ultra-compact, high-capacity, and highly secure information storage approaches. Computer science ; Deep learning ; Encryption ; Multiplexing ; Electronic engineering ; Artificial intelligence ; Telecommunications ; Engineering ; Computer network Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Random lasers and scattering media
Authors: Xinyuan Xu | Zefeng Yu | Wei‐Ying Chen | Aiping Chen | Arthur T. Motta | Xing Wang
Source: Journal of Nuclear Materials (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm Nanocrystalline material ; Convolutional neural network ; Crystallite ; Computer science ; Artificial intelligence ; Transmission electron microscopy ; Algorithm ; Machine learning ; Grain size ; Throughput ; Materials science ; Nanotechnology ; Composite material ; Metallurgy Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Dane M. Sterbentz | Charles F. Jekel | D. White | Robert N. Rieben | Jonathan L. Belof
Source: Journal of Applied Physics (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Linear shaped-charge jet optimization using machine learning methods Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. We are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE. Shaped charge ; Explosive material ; Mechanics ; Detonation ; Jet (fluid) ; Instability ; Detonator ; Work (physics) ; Materials science ; Mechanical engineering ; Physics ; Chemistry ; Engineering High-Velocity Impact and Material Behavior ; Laser-Plasma Interactions and Diagnostics ; Energetic Materials and Combustion
Authors: Yifei Wang | Shiyang Chen | Guobin Chen | Ethan Shurberg | Hang Liu | Pengyu Hong
Source: Informatics (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Motif-Based Graph Representation Learning with Application to Chemical Molecules This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks. Computer science ; ENCODE ; Motif (music) ; Graph ; Theoretical computer science ; Feature learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Katarina Nedić Grujin | Tanja Lužaić | Lato Pezo | Branislava Nikolovski | Zoran Maksimović | Ranko Romanić
Source: Foods (article, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid—Investigation of Oil Quality during Industrial Filtration Probe Waxes, phospholipids, free fatty acids, peroxides, aldehydes, soap, trace metals and moisture present in crude sunflower oil have a negative effect on the oil quality and are, therefore, removed during the refining process. Waxes crystallizing at low temperatures are removed during winterization by cooling and filtration. Waxes have poor filtration characteristics and an industrial filtration process must be enhanced by the use of filtration aids, which improve filter cake structure and properties, and consequently prolong the filtration cycle. Today, traditional filtration aids (diatomite, perlite, etc.) being used in the industry are frequently replaced by cellulose-based aids. The aim of this study is to examine the effect of oil filtration assisted by two cellulose-based filtration aids on the chemical parameters (wax, moisture, phospholipids, soaps, and fatty acids), oil transparency, carotenoids, and Fe and Cu content of sunflower oil obtained in an industrial horizontal pressure leaf filter. In order to investigate the mentioned parameters, the following techniques were used: gravimetric (waxes and moisture content), spectrophotometric (phospholipids and carotenoid content and oil transparency), volumetric (soaps and free fatty acids content) as well as inductively coupled plasma mass spectrometry (ICP-MS) for Fe and Cu content. An artificial neural network model (ANN) was employed for the prediction of removal efficiency based on the chemical quality, oil transparency, Fe and Cu content in oils before filtration, as well as filtration aid quantity and filtration time. Cellulose-based filtration aids had multiple beneficial effects; on average, 99.20% of waxes, 74.88% of phospholipids, 100% of soap, 7.99% of carotenoids, 16.39% of Fe and 18.33% of Cu were removed. Filtration (mathematics) ; Wax ; Sunflower oil ; Chromatography ; Chemistry ; Filter cake ; Cellulose ; Pulp and paper industry ; Food science ; Organic chemistry ; Mathematics Spectroscopy and Chemometric Analyses ; Electrohydrodynamics and Fluid Dynamics ; Electrospun Nanofibers in Biomedical Applications
Authors: Peiguo Liu | Hanqing Liu
Source: The Innovation (review, 2023-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic protection strategy using adaptive energy selective mechanism Electromagnetic spaces face growing threats from both naturally occurring and artificial electromagnetic pulses; however, the current protection methodologies are still far from practical needs. To address this issue, we propose an electromagnetic protection strategy that makes use of an adaptive energy selective mechanism. This strategy, carried out using electromagnetic metamaterials, provides in-band protection to electronic equipment with a high tolerance threshold and fast response. We propose several approaches to further enhance the protective performance of electromagnetic metamaterials. These include reconfigurable designs based on digital circuits and deep learning algorithms, as well as the adoption of nanoscale field-controlled devices made of two-dimensional (2D) phase transition materials and nanoelectromechanical systems. Our study can not only lead to a comprehensive protection system with superior compatibility, but also offer reliable support for maintaining electromagnetic spatial security. Metamaterial ; Computer science ; Electromagnetic field ; Electromagnetic compatibility ; Electromagnetic environment ; Electronics ; Electromagnetic radiation ; Electronic engineering ; Electrical engineering ; Telecommunications ; Engineering ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Energy Harvesting in Wireless Networks
Authors: Chuanrui Qin | Mengtao Dang | Yifei Meng | Dongfeng Zhao
Source: Process Safety and Environmental Protection (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Fusion calorimetric experiments and QSPR modeling to inversely explore the accuracy of thermodynamic methods: A case study of the apparent activation energies of 18 aromatic nitro compounds Quantitative structure–activity relationship ; Chemistry ; Nitro ; Thermodynamics ; Adiabatic process ; Biological system ; Computational chemistry ; Organic chemistry ; Stereochemistry Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Yuhao Liu | Xiaoxiao Huang | Jiahao Sun | Guansheng Ma | Yuefeng Yan | Boshi Gao | Xu Yan | Yanan Liu
Source: Composites Science and Technology (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

Multi-scale optimization of selectively magnetized isotropic carbon fiber fabrics for microwave absorption using machine learning Finite element method ; Microwave ; Isotropy ; Materials science ; Computer science ; Mechanical engineering ; Macro ; Absorption (acoustics) ; Bandwidth (computing) ; Computational science ; Composite material ; Structural engineering ; Optics ; Physics ; Engineering Graphene research and applications ; MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties
Authors: Murphy M. Peksen | H. Spliethoff
Source: International Journal of Hydrogen Energy (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Optimising pre-reforming for quality r-SOC syngas preparation using artificial intelligence (AI) based machine learning (ML) Syngas ; Process engineering ; Process (computing) ; Computer science ; Hydrogen ; Engineering ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions