jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fernando Lejarza | Michael Bâldea
Source: AIChE Journal (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Discovering governing equations via moving horizon learning: The case of reacting systems Abstract Governing equations in the form of differential equations are fundamental modeling elements for understanding, controlling, and optimizing chemical processes. While significant advances in machine learning allow for high‐performance surrogate modeling, the resulting models typically fail to extrapolate to regimes beyond the training data and provide little physical insight regarding the underlying phenomena. In this work, we propose a moving horizon dynamic nonlinear optimization strategy that recovers parsimonious governing equations from large‐scale, noisy data sets. Differently from prior works, our approach does not rely on significant structural assumptions (mainly concerning linearity with respect to estimated model coefficients), which provides greater modeling flexibility and permits distilling governing equations of systems involving chemical reactions occurring under nonisothermal conditions. The main advantages and contributions of our proposed approach are demonstrated through two numerical case studies consisting of a continuously stirred tank reactor operated under isothermal and nonisothermal conditions. Nonlinear system ; Flexibility (engineering) ; Computer science ; Work (physics) ; Isothermal process ; Applied mathematics ; Mathematical optimization ; Scale (ratio) ; Mathematics ; Engineering ; Physics ; Thermodynamics ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yi Chen | Jingyu Wang | Benjamin B. Hoar | Shengtao Lu | Chong Liu
Source: Proceedings of the National Academy of Sciences (article, 2022-08-01)
Field: Chemistry — Electrochemistry

Machine learning–based inverse design for electrochemically controlled microscopic gradients of O <sub>2</sub> and H <sub>2</sub> O <sub>2</sub> A fundamental understanding of extracellular microenvironments of O<sub>2</sub> and reactive oxygen species (ROS) such as H<sub>2</sub>O<sub>2</sub>, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> at microscopic scale with high spatiotemporal precision. However, there is a paucity of high-throughput strategies of microenvironment design, and it remains challenging to achieve O<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> profiles with spatial resolution of ∼10<sup>1</sup> μm and temporal resolution of ∼10° s. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> microenvironments while being two orders of magnitude faster than experimental explorations. Interfacing ML-based inverse design with electrochemically controlled concentration heterogeneity creates a viable fast-response platform toward better understanding the extracellular space with desirable spatiotemporal control. Interfacing ; Inverse ; Nanotechnology ; Computer science ; Throughput ; Biological system ; Materials science ; Chemistry ; Biology ; Computer hardware ; Wireless ; Mathematics Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Photoacoustic and Ultrasonic Imaging
Authors: Yadnyesh Y. Karkare | Wadilal R. Rathod | Vivek Sathe | Abhijit R. Chavan
Source: Chemical Engineering and Processing - Process Intensification (article, 2022-12-09)
Field: Chemistry — Organic Chemistry

Combined ultrasound-microwave assisted synthesis of aripiprazole: Process optimization using RSM-ANN Response surface methodology ; Central composite design ; Yield (engineering) ; Process optimization ; Microwave ; Impurity ; Chemistry ; Moiety ; Composite number ; Materials science ; Analytical Chemistry (journal) ; Process engineering ; Chromatography ; Chemical engineering ; Computer science ; Organic chemistry ; Composite material Microwave-Assisted Synthesis and Applications ; Multicomponent Synthesis of Heterocycles ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yuqi Wang | Siyuan Wu | Wei Shao | Xiaorui Sun | Qiang Wang | Ruijuan Xiao | Hong Li
Source: Journal of Materiomics (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Accelerated strategy for fast ion conductor materials screening and optimal doping scheme exploration Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks. The process usually includes structural optimization, energy calculation, charge analysis and ionic migration performance estimation. The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures, and the computational cost is always high because of the task-intensive features. The last one relates to the kinetic problems, for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods. In this work, two predictive models, ionic migration activation energy model and structural optimization model, are developed based on machine learning (ML) techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures, respectively. By training 3136 energy barrier data calculated by bond valence (BV) method, an ionic migration activation energy model (Ea model) with mean absolute error (MAE) of 0.26 eV on testing data set is obtained. We apply this model and filter LiBiOS as a promising fast Li+ conductor from 49 Li-containing hetero-anionic compounds. Although the model-predicted result shows relatively low energy barrier, further analysis indicates that the high carrier formation energy restricts the ionic transportability. Therefore, we substitute fractional Li+ with Mg2+ in LiBiOS to relieve the large difficulty of forming carriers in the structure. In order to fast explore the optimal doping scheme, we develop the structural optimization model (E-f model) containing the ML-based energy and force prediction to accelerate the structural optimization under various LiMg ratio and doping configurations. Decent doping scheme Li1-2xMgxBiOS (x = 0.1875) shows much better Li+ migration performance compared with LiBiOS without substitution. This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations. Materials science ; Ionic bonding ; Conductor ; Computer science ; Ion ; Kinetic energy ; Molecular dynamics ; Chemical physics ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Composite material Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advancements in Battery Materials
Authors: Zhao Zhizheng | Mohamed E. El‐Khouly | Qiang Che | Fangcheng Sun | Bin Zhang | Haidong He | Yu Chen
Source: Angewandte Chemie (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Redox‐Active Azulene‐based 2D Conjugated Covalent Organic Framework for Organic Memristors Abstract As a conjugated and unsymmetric building block composed of an electron‐poor seven‐membered sp 2 carbon ring and an electron‐rich five‐membered carbon ring, azulene and its derivatives have been recognized as one of the most promising building blocks for novel electronic devices due to its intrinsic redox activity. By using 1,3,5‐tris(4‐aminophenyl)‐benzene and azulene‐1,3‐dicarbaldehyde as the starting materials, an azulene(Azu)‐based 2D conjugated covalent organic framework, COF‐Azu, is prepared through liquid‐liquid interface polymerization strategy for the first time. The as‐fabricated Al/COF‐Azu/indium tin oxide (ITO) memristor shows typical non‐volatile resistive switching performance due to the electric filed induced intramolecular charge transfer effect. Associated with the unique memristive performance, a simple convolutional neural network is built for image recognition. After 8 epochs of training, image recognition accuracy of 80 % for a neutral network trained on a larger data set is achieved. Azulene ; Conjugated system ; Covalent organic framework ; Materials science ; Memristor ; Indium tin oxide ; Covalent bond ; Porphyrin ; Chemistry ; Photochemistry ; Nanotechnology ; Organic chemistry ; Layer (electronics) Covalent Organic Framework Applications ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: Lifeng Lian | Zhi Li
Source: Waves in Random and Complex Media (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Dynamic and frequency responses of the FG nanopipe using deep neural network and nonlocal strain/stress gradient theory This research introduces physics-informed supervised neural networks to predict the vibration behavior of nano-structures. In this regard, data-driven solution and data-driven discovery are presented to solve problems involving the determination of natural frequencies. Equations of motion are derived employing Hamilton’s principle for bi-directional functionally graded (FG) concrete nanopipe and solved with the aid of the discrete singular convolution-integrated method (DSC-IM). For modeling the current nanostructure, an exact size-dependent theory called nonlocal strain/stress gradient theory is presented. In this work, the structure is made of concrete materials, which vary exponentially in both thickness and axial directions. Further, a parametric study is conducted to investigate the effects of the FG power index in each direction, length to width ratio, and size-dependent parameters on the frequency of the bi-directional FG concrete nanopipe. By comparing the current results with the outcomes of the deep neural network, and open sources in the literature, the outcomes are verified. For future research, the outputs of this article can be used in the handbook of the nanostructures made of concrete materials. Artificial neural network ; Stress (linguistics) ; Strain (injury) ; Mathematical analysis ; Mathematics ; Physics ; Computer science ; Artificial intelligence ; Philosophy Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Markus J. Buehler
Source: MRS Communications (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures Universality (dynamical systems) ; Generative grammar ; Transformer ; Artificial neural network ; Materials science ; Generative adversarial network ; Artificial intelligence ; Computer science ; Nanotechnology ; Deep learning ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Pickering emulsions and particle stabilization ; Additive Manufacturing and 3D Printing Technologies
Authors: Emma M. Glass | Sahil Kulkarni | Christina Eng | shurui feng | Avishi Malaviya | Ravi Radhakrishnan
Source: Frontiers in Medical Technology (article, 2022-07-15)
Field: Materials Science — Biomaterials

Multiphysics pharmacokinetic model for targeted nanoparticles Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide <i>in vivo</i> NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff <i>via</i> quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs. Physiologically based pharmacokinetic modelling ; Multiphysics ; Ordinary differential equation ; Ode ; Computer science ; Solver ; MATLAB ; Biodistribution ; Biological system ; Multiscale modeling ; Pharmacokinetics ; Applied mathematics ; Chemistry ; Differential equation ; Mathematics ; Bioinformatics ; Physics ; Computational chemistry ; Finite element method ; Biology ; Thermodynamics Nanoparticle-Based Drug Delivery ; Field-Flow Fractionation Techniques ; Mathematical Biology Tumor Growth
Authors: Pınar Demir
Source: Polymer Bulletin (article, 2022-10-21)
Field: Materials Science — Polymers and Plastics

Thermal degradation kinetics, mechanism, thermodynamics, shape memory properties and artificial neural network application study of polycaprolactone (PCL)/polyvinyl chloride (PVC) blends Polycaprolactone ; Materials science ; Polyvinyl chloride ; Activation energy ; Glass transition ; Kinetics ; Amorphous solid ; Melting point ; Thermal decomposition ; Thermal analysis ; Differential scanning calorimetry ; Polymer blend ; Composite material ; Chemical engineering ; Polymer ; Polymer chemistry ; Thermal ; Thermodynamics ; Physical chemistry ; Copolymer ; Chemistry ; Organic chemistry Polymer Science and PVC ; Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties
Authors: Simona Varvara | Camelia Berghian-Groșan | Gianina Elena Damian | Maria Popa | Florin Popa
Source: Materials (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution The inhibiting properties of 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol (PyODT) on the corrosion of carbon steel in 1.0 M HCl solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy, Raman spectroscopy, and SEM-EDX analysis. An approach based on machine learning algorithms and Raman data was also applied to follow the carbon steel degradation in different experimental conditions. The electrochemical measurements revealed that PyODT behaves as a mixed-type corrosion inhibitor, reaching an efficiency of about 93.1% at a concentration of 5 mM, after 1 h exposure to 1.0 M HCl solution. Due to the molecular adsorption and structural organization of PyODT molecules on the C-steel surface, higher inhibitive effectiveness of about 97% was obtained at 24 h immersion. The surface analysis showed a significantly reduced degradation state of the carbon steel surface in the presence of PyODT due to the inhibitor adsorption revealed by Raman spectroscopy and the presence of N and S atoms in the EDX spectra. The combination of Raman spectroscopy and machine learning algorithms was proved to be a facile and reliable tool for an incipient identification of the corrosion sites on a metallic surface exposed to corrosive environments. Raman spectroscopy ; Carbon steel ; Dielectric spectroscopy ; Materials science ; Corrosion ; Adsorption ; Electrochemistry ; Metal ; Molecule ; Inorganic chemistry ; Analytical Chemistry (journal) ; Nuclear chemistry ; Chemistry ; Metallurgy ; Physical chemistry ; Organic chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bruno X. Ferreira | Carlos Roberto Hall Barbosa | João Cajaiba | Vinícius Kartnaller | Brunno Ferreira dos Santos
Source: Energy & Fuels (article, 2022-01-31)
Field: Materials Science — Biomaterials

Development of Artificial Neural Network Models for the Simulation of a CaCO<sub>3</sub> Scale Formation Process in the Presence of Monoethylene Glycol (MEG) in Dynamic Tube Blocking Test Equipment The precipitation of gas hydrate and inorganic salts (scale) during oil and gas production represents a significant flow assurance hindrance for the industry. Chemical inhibitors can prevent the fouling process, but specific inhibitors to address a problem could result in synergistic or adverse effects. Simulations in tubes and pipelines are necessary to understand these behaviors by assessing the scaling tendency of the water. The primary objective of this study was to create models using an artificial neural network (ANN) of the multilayer perceptron (MLP) type for the simulation of the calcium carbonate scaling formation process in the presence of monoethylene glycol (MEG), a typical gas hydrate inhibitor. A database was obtained from 38 tube blocking test (TBT) experiments with different conditions. The models were developed using MATLAB R2020a, splitting the database into two groups on the ratio of 70:30, respectively, train and test ones, preserving the time dependency of the differential pressure (ΔP) data. The ANNs were created using six inputs (temperature, pressure, calcium and bicarbonate concentrations, MEG concentration, and ΔP measured at a selected time) and one output (ΔP measured at a later time). The goal was to explore how monitoring the conditions in a pipeline can predict the evolution of the scaling process. We investigated two scenarios for the ΔP prediction: a near future (one step ahead) and a far future (five steps ahead). The MLP models demonstrated high performance, with an R2 higher than 92.9% for both training and test groups for both prediction horizons. Then, these models were tested with a second data group to evaluate their applicability to control the systems. The best models showed good scaling prediction, with R2 ranging from 80.0% to 99.9%. These results represent a promising step toward applying machine learning techniques to simulate and predict scaling tendencies in controlled pipelines. Artificial neural network ; Flow assurance ; Scaling ; Test data ; Process (computing) ; Computer science ; Biological system ; Simulation ; Chemistry ; Hydrate ; Artificial intelligence ; Mathematics Calcium Carbonate Crystallization and Inhibition ; Drilling and Well Engineering ; Hydraulic Fracturing and Reservoir Analysis
Authors: Mohammad Sabeghi | Mohammad Moghiman | Danial Gandomzadeh
Source: International Journal of Hydrogen Energy (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Experimental study of the effect of HHO gas injection on pollutants produced by a diesel engine at idle speed Diesel engine ; Automotive engineering ; Exhaust gas ; Diesel fuel ; Idle ; Environmental science ; Gas engine ; Combustion ; Engineering ; Waste management ; Chemistry ; Computer science Catalytic Processes in Materials Science ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: S. Quondam Antonio | Vincenzo Bonaiuto | F. Sargeni | Alessandro Salvini
Source: Magnetochemistry (article, 2022-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel A computationally efficient hysteresis model, based on a standalone deep neural network, with the capability of reproducing the evolution of the magnetization under arbitrary excitations, is here presented and applied in the simulation of a commercial grain-oriented electrical steel sheet. The main novelty of the proposed approach is to embed the past history dependence, typical of hysteretic materials, in the neural net, and to illustrate an optimized training procedure. Firstly, an experimental investigation was carried out on a sample of commercial GO steel by means of an Epstein equipment, in agreement with the international standard. Then, the traditional Preisach model, identified only using three measured symmetric hysteresis loops, was exploited to generate the training set. Once the network was trained, it was validated with the reproduction of the other measured hysteresis loops and further hysteresis processes obtained by the Preisach simulations. The model implementation at a low level of abstraction shows a very high computational speed and minimal memory allocation, allowing a possible coupling with finite-element analysis (FEA). Hysteresis ; Preisach model of hysteresis ; Artificial neural network ; Magnetic hysteresis ; Abstraction ; Finite element method ; Coupling (piping) ; Computer science ; Electrical steel ; Magnetization ; Ferromagnetism ; Shape-memory alloy ; Novelty ; Statistical physics ; Materials science ; Condensed matter physics ; Artificial intelligence ; Physics ; Engineering ; Structural engineering ; Magnetic field ; Metallurgy Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Electric Motor Design and Analysis