jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiang Li | Romain Berraud‐Pache | Christelle Souprayen | Maguy Jaber
Source: Applied Clay Science (article, 2023-08-08)
Field: Materials Science — Biomaterials

Intercalation of lecithin into bentonite: pH dependence and intercalation mechanism Organoclay ; Intercalation (chemistry) ; Lecithin ; Chemistry ; Bilayer ; Pulmonary surfactant ; Bentonite ; Saponite ; Inorganic chemistry ; Ion exchange ; Chemical engineering ; Crystallography ; Ion ; Organic chemistry ; Chromatography ; Montmorillonite ; Membrane Clay minerals and soil interactions ; Polymer Nanocomposites and Properties ; Surfactants and Colloidal Systems
Authors: Ming Li | Kaitang Hu | Suying Shao
Source: Cogent Engineering (article, 2023-02-07)
Field: Materials Science — Biomaterials

Tensile strength estimation of paper sheets made from recycled wood and non-wood fibers using machine learning The deterioration of fiber properties during recycling processes, especially the loss of tensile strength, raises concerns that paper products made from recycled fibers might not satisfy quality requirements.The purpose of this paper is to estimate the deterioration of tensile strength and the damage in paper sheets made of recycled fibers using the theory of damage mechanics and machine learning methods.Experiments were carried out to recycle wood fibers and nonwood fibers four times, and the physicochemical properties of the handsheets made from these fibers were measured after each recycling.Water retention value and relative bonded area were selected as the features to estimate and predict tensile strength during recycling because they had strong correlations with tensile strength.This paper proposed a damage index to quantitatively express the severity of the damage in paper sheets based on the experimental investigation and the theory of damage mechanics.Thus, the deterioration of tensile strength could be estimated and predicted.To determine the damage index, a curve fitting model based on the hyperbolic theory of pulp properties was developed.The proposed quantitative expression of the damage index is:, where the coefficients were determined through the curve fitting model.This paper also developed a long short-term memory recurrent neural network model to determine the damage index according to the sequence of recycling.Both models were trained with the experimental data of water retention value and relative bonded area.The estimation and prediction by the curve fitting model were more accurate than those of the neural network model.The root mean square errors by the curve fitting model were 0.0278 for estimation, 0.1667 for prediction; and by the neural network model were 0.2445 for estimation, 0.2206 for prediction, respectively.After the damage index was determined, the deterioration of tensile strength then could be calculated as T = T 0 (1-D). Ultimate tensile strength ; Materials science ; Composite material ; Pulp (tooth) ; Fiber ; Compressive strength ; Curve fitting ; Artificial neural network ; Structural engineering ; Computer science ; Machine learning ; Engineering Advanced Cellulose Research Studies ; Material Properties and Processing ; Natural Fiber Reinforced Composites
Authors: Ivette Morales‐Salazar | Carlos E. Garduño-Albino | Flora P. Montes-Enríquez | Dania A. Nava-Tapia | Napoleón Navarro‐Tito | Leonardo David Herrera-Zúñiga | Eduardo González‐Zamora | Alejandro Islas‐Jácome
Source: Pharmaceuticals (article, 2023-11-06)
Field: Chemistry — Organic Chemistry

Synthesis of Pyrrolo[3,4-b]pyridin-5-ones via Ugi–Zhu Reaction and In Vitro–In Silico Studies against Breast Carcinoma An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (<i>N</i>-acylation/<i>aza</i> Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-<i>b</i>]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound <b>1f</b>, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT<sub>1</sub>) and Orexetine type 2 receptor (Ox<sub>2</sub>R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands. In vitro ; Chemistry ; Docking (animal) ; Acylation ; In silico ; Trifluoromethanesulfonate ; Stereochemistry ; Kinase ; Combinatorial chemistry ; Catalysis ; Biochemistry Multicomponent Synthesis of Heterocycles ; Phytochemical compounds biological activities ; Synthesis and biological activity
Authors: Liqin Su | Yifei Yu | Linyou Cao | Yong Zhang
Source: Science China Materials (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Correlative spectroscopic investigations of the mechanisms of inhomogeneity in CVD-grown monolayer WS2 Materials science ; Raman spectroscopy ; Mesoscopic physics ; Monolayer ; Tungsten disulfide ; Chemical vapor deposition ; Doping ; Photoluminescence ; Sapphire ; Condensed matter physics ; Nanotechnology ; Optoelectronics ; Optics ; Laser ; Composite material ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications
Authors: Mathias Schreiner | Ole Winther | Simon Olsson
Source: arXiv (Cornell University) (preprint, 2023-05-29)
Field: Materials Science — Materials Chemistry

Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereas convergence of some moments, e.g. binding free energy or rates, might rely on sampling processes on time-scales as long as $10^{-1}\, \mathrm{s}$, and these simulations must be repeated for every molecular system independently. Here, we present Implict Transfer Operator (ITO) Learning, a framework to learn surrogates of the simulation process with multiple time-resolutions. We implement ITO with denoising diffusion probabilistic models with a new SE(3) equivariant architecture and show the resulting models can generate self-consistent stochastic dynamics across multiple time-scales, even when the system is only partially observed. Finally, we present a coarse-grained CG-SE3-ITO model which can quantitatively model all-atom molecular dynamics using only coarse molecular representations. As such, ITO provides an important step towards multiple time- and space-resolution acceleration of MD. Code is available at \href{https://github.com/olsson-group/ito}{https://github.com/olsson-group/ito}. Computer science ; Molecular dynamics ; Convergence (economics) ; Operator (biology) ; Code (set theory) ; Resolution (logic) ; Statistical physics ; Algorithm ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Zijie Geng | Shufang Xie | Yingce Xia | Lijun Wu | Tao Qin | Jie Wang | Yongdong Zhang | Feng Wu | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

De Novo Molecular Generation via Connection-aware Motif Mining De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibility in generating novel molecules based on existing molecule fragments. However, the motif vocabulary, i.e., the collection of frequent fragments, is usually built upon heuristic rules, which brings difficulties to capturing common substructures from large amounts of molecules. In this work, we propose a new method, MiCaM, to generate molecules based on mined connection-aware motifs. Specifically, it leverages a data-driven algorithm to automatically discover motifs from a molecule library by iteratively merging subgraphs based on their frequency. The obtained motif vocabulary consists of not only molecular motifs (i.e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other. Based on the mined connection-aware motifs, MiCaM builds a connection-aware generator, which simultaneously picks up motifs and determines how they are connected. We test our method on distribution-learning benchmarks (i.e., generating novel molecules to resemble the distribution of a given training set) and goal-directed benchmarks (i.e., generating molecules with target properties), and achieve significant improvements over previous fragment-based baselines. Furthermore, we demonstrate that our method can effectively mine domain-specific motifs for different tasks. Motif (music) ; Computer science ; Generative model ; Vocabulary ; Generative grammar ; Structural motif ; Artificial intelligence ; Heuristic ; Theoretical computer science ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maciej Przybyłek | Tomasz Jeliński | Magdalena Mianowana | Kinga Misiak | Piotr Cysewski
Source: Molecules (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of edaravone solubility data. Hence, new measurements were performed to collect edaravone solubility values in polar non-protic and diprotic media. Such an extended set of data was used in the machine learning process for tuning the parameters of regressor models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring high predictive power and accurate solubility computations for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria, were included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are better suited for edaravone dissolution than those described in this manuscript. The theoretical framework presented in this study provides a precise guideline for conducting experiments, as well as saving time and resources in the pursuit of new findings. Solubility ; Dissolution ; Solvent ; Binary number ; Edaravone ; Computer science ; Process (computing) ; Protocol (science) ; Chemistry ; Process engineering ; Thermodynamics ; Mathematics ; Organic chemistry ; Physics ; Engineering Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Zige Liu | Michael L. Kalin | Bojun Liu | Siqin Cao | Xuhui Huang
Source: Aggregate (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems Abstract Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences. Aggregation-induced emission ; Kinetics ; Aggregate (composite) ; Luminescence ; Amorphous solid ; Molecular dynamics ; Chemistry ; Computer science ; Nanotechnology ; Biological system ; Chemical physics ; Materials science ; Fluorescence ; Physics ; Computational chemistry Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: Fatemeh Akbari
Source: Medical Physics (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

A comprehensive open‐access database of electron backscattering coefficients for energies ranging from 0.1 keV to 15 MeV The database provides the most up-to-date source of experimentally obtained electron backscattering coefficients that can be used in theoretical and MC calculations and modeling validations. The data availability is still very limited for many solids and almost non-existent for compounds. Novel machine learning methods should be well adapted to predict these unknown values for various targets, thicknesses, energies, and incident angles utilizing the presented cleaned dataset. Monte Carlo method ; Electron ; Database ; Electron scattering ; Scattering ; Physics ; Characterization (materials science) ; Atomic number ; Computational physics ; Materials science ; Computer science ; Atomic physics ; Optics ; Nuclear physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Materials and Properties
Authors: Mikhail Ivanov | Maksim Posysoev | Alexander P. Lyubartsev
Source: Journal of Chemical Theory and Computation (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set. Artificial neural network ; Computer science ; Granularity ; Monte Carlo method ; Set (abstract data type) ; Range (aeronautics) ; Nonlinear system ; Algorithm ; Inverse ; Statistical physics ; Biological system ; Artificial intelligence ; Mathematics ; Materials science ; Physics ; Statistics Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Protein Structure and Dynamics
Authors: Thanh Hai Pham | Ong Kim Le | Viorel Chihaia | Phung K. Le | Do Ngoc Son
Source: Journal of The Electrochemical Society (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Computational Prediction of Electrochemical Corrosion Rates of Copper in the Presence of Corrosion Inhibitors Computational prediction of corrosion rates is still a challenging issue in the field of metal corrosion. In this study, we proposed a computational model to predict the corrosion rates of copper in the presence of adsorption-type corrosion inhibitors using density functional theory calculations, microkinetic simulation, and machine learning. The model-calculated corrosion current and potential of clean copper are close to values obtained in available experiments. The copper corrosion rates in the presence of inhibitors were further predicted using the adsorption free energy of adsorbed inhibitors and the inhibitor concentration in solution to describe the effects of inhibitors. The proposed model was applied to predict corrosion inhibition efficiency by combining it with a machine learning model. The combining model exhibited that it was more interpretative and accurate than a machine-learning-only model in predicting corrosion inhibition efficiencies of organic compounds on copper. Corrosion ; Copper ; Adsorption ; Electrochemistry ; Corrosion inhibitor ; Erosion corrosion of copper water tubes ; Metal ; Materials science ; Chemistry ; Metallurgy ; Inorganic chemistry ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaopeng Li | Hongping Zhang | Haifei Zhan | Youhong Tang
Source: Nanomaterials (article, 2023-08-08)
Field: Materials Science — Biomaterials

Structural and Mechanical Properties of Doped Tobermorite As calcium silicate hydrate (C-S-H) is the main binding phase in concrete, understanding the doping behavior of impurity elements in it is important for optimizing the structure of cementitious materials. However, most of the current studies focus on cement clinker, and the doping mechanism of impurity elements in hydrated calcium silicate is not yet fully understood. The hydrated calcium silicate component is complex, and its structure is very similar to that of the tobermorite mineral family. In this study, the effects of three different dopants (Mg, Sr and Ba) on a representing structure of C-S-H-tobermorite-was systematically explored using densify functional theory (DFT) calculations. The calculations show that Mg doping leads to a decrease in lattice volume and causes obvious structure and coordination changes of magnesium-oxygen polyhedra. This may be the reason why high formation energy is required for the Mg-doped tobermorite. Meanwhile, doping only increases the volume of the Sr- and Ba-centered oxygen polyhedra. Specifically, the Mg-doped structure exhibits higher chemical stability and shorter interatomic bonding. In addition, although Mg doping distorts the structure, the stronger chemical bonding between Mg-O atoms also improves the compressive (~1.99% on average) and shear resistance (~2.74% on average) of tobermorillonite according to the elastic modulus and has less effect on the anisotropy of the Young's modulus. Our results suggest that Mg doping is a promising strategy for the optimized structural design of C-S-H. Tobermorite ; Materials science ; Doping ; Composite material ; Nanotechnology ; Optoelectronics Clay minerals and soil interactions ; Recycling and utilization of industrial and municipal waste in materials production ; Concrete and Cement Materials Research
Authors: Xueling Wei | Jie Nong | Yiyi Zhang | Hansi Ma | Rixing Huang | Zhenkun Yuan | Zhenfu Zhang | Zhenrong Zhang | Junbo Yang
Source: Nanomaterials (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Sb2S3-Based Dynamically Tuned Color Filter Array via Genetic Algorithm Color displays have become increasingly attractive, with dielectric optical nanoantennas demonstrating especially promising applications due to the high refractive index of the material, enabling devices to support geometry-dependent Mie resonance in the visible band. Although many structural color designs based on dielectric nanoantennas employ the method of artificial positive adjustment, the design cycle is too lengthy and the approach is non-intelligent. The commonly used phase change material Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST) is characterized by high absorption and a small contrast to the real part of the refractive index in the visible light band, thereby restricting its application in this range. The Sb<sub>2</sub>S<sub>3</sub> phase change material is endowed with a wide band gap of 1.7 to 2 eV, demonstrating two orders of magnitude lower propagation loss compared to GST, when integrated onto a silicon waveguide, and exhibiting a maximum refractive index contrast close to 1 at 614 nm. Thus, Sb<sub>2</sub>S<sub>3</sub> is a more suitable phase change material than GST for tuning visible light. In this paper, genetic algorithms and finite-difference time-domain (FDTD) solutions are combined and introduced as Sb<sub>2</sub>S<sub>3</sub> phase change material to design nanoantennas. Structural color is generated in the reflection mode through the Mie resonance inside the structure, and the properties of Sb<sub>2</sub>S<sub>3</sub> in different phase states are utilized to achieve tunability. Compared to traditional methods, genetic algorithms are superior-optimization algorithms that require low computational effort and a high population performance. Furthermore, Sb<sub>2</sub>S<sub>3</sub> material can be laser-induced to switch the transitions of the crystallized and amorphous states, achieving reversible color. The large chromatic aberration ∆E modulation of 64.8, 28.1, and 44.1 was, respectively, achieved by the Sb<sub>2</sub>S<sub>3</sub> phase transition in this paper. Moreover, based on the sensitivity of the structure to the incident angle, it can also be used in fields such as angle-sensitive detectors. Refractive index ; Finite-difference time-domain method ; Materials science ; Dielectric ; Optics ; Optoelectronics ; Mie scattering ; Structural coloration ; Band gap ; Phase (matter) ; Computer science ; Physics ; Light scattering ; Photonic crystal ; Scattering Phase-change materials and chalcogenides ; Optical Polarization and Ellipsometry ; Advanced optical system design