jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Long | Hansong Ma | Yueguang Wei | Yanwei Liu
Source: International Journal of Mechanical Sciences (article, 2021-02-15)
Field: Materials Science — Materials Chemistry

A size-dependent model for predicting the mechanical behaviors of adhesively bonded layered structures based on strain gradient elasticity Materials science ; Adhesive ; Composite material ; Stiffening ; Elasticity (physics) ; Elastic modulus ; Displacement (psychology) ; Deformation (meteorology) ; Modulus ; Structural engineering ; Layer (electronics) ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Thermal properties of materials ; Mechanical and Optical Resonators
Authors: Alexander Kensert | Gilles Collaerts | Kyriakos Efthymiadis | Gert Desmet | Deirdre Cabooter
Source: Journal of Chromatography A (article, 2021-01-14)
Field: Chemistry — Spectroscopy

Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography Reinforcement learning ; Retention time ; Chemistry ; Selection (genetic algorithm) ; Chromatography ; Artificial intelligence ; Computer science Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chromatography in Natural Products
Authors: Chowdhury Ashraf | Nisarg Joshi | David A. C. Beck | Jim Pfaendtner
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2021-03-12)
Field: Materials Science — Materials Chemistry

Data Science in Chemical Engineering: Applications to Molecular Science Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations. Data science ; Computer science ; Molecular engineering ; Property (philosophy) ; Process (computing) ; Field (mathematics) ; Cheminformatics ; Science and engineering ; Nanotechnology ; Engineering ; Bioinformatics ; Engineering ethics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rubén Agregán | Noemí Echegaray | María López‐Pedrouso | Rana Muhammad Aadil | Christophe Hano | Daniel Franco | José M. Lorenzo
Source: Molecules (review, 2021-08-14)
Field: Chemistry — Spectroscopy

Proteomic Advances in Cereal and Vegetable Crops The importance of vegetables in human nutrition, such as cereals, which in many cases represent the main source of daily energy for humans, added to the impact that the incessant increase in demographic pressure has on the demand for these plant foods, entails the search for new technologies that can alleviate this pressure on markets while reducing the carbon footprint of related activities. Plant proteomics arises as a response to these problems, and through research and the application of new technologies, it attempts to enhance areas of food science that are fundamental for the optimization of processes. This review aims to present the different approaches and tools of proteomics in the investigation of new methods for the development of vegetable crops. In the last two decades, different studies in the control of the quality of crops have reported very interesting results that can help us to verify parameters as important as food safety, the authenticity of the products, or the increase in the yield by early detection of diseases. A strategic plan that encourages the incorporation of these new methods into the industry will be essential to promote the use of proteomics and all the advantages it offers in the optimization of processes and the solution of problems. Biotechnology ; Carbon footprint ; Emerging technologies ; Risk analysis (engineering) ; Quality (philosophy) ; Business ; Biochemical engineering ; Computer science ; Agricultural engineering ; Engineering ; Biology Advanced Proteomics Techniques and Applications ; Identification and Quantification in Food ; Metabolomics and Mass Spectrometry Studies
Authors: Moza Ghorab Al Marri | Mohammad A. Al‐Ghouti | Vasanth Chakravarthy Shunmugasamy | Nabil Zouari
Source: PLoS ONE (article, 2021-03-26)
Field: Materials Science — Polymers and Plastics

Date pits based nanomaterials for thermal insulation applications—Towards energy efficient buildings in Qatar Air-conditioning systems make the most significant part of energy consumption in the residential sector. There is no denying that it is essential to produce a comfortable indoor thermal environment for residents in a building. The actual goal is to achieve thermal comfort level without putting too much cost on the ecological system by trying to conserve the amount of energy consumed. An effective way to help achieve such a goal is by incorporating thermal insulation in buildings. Thermal insulations help reduce thermal energy gained during the implementation of a desired thermal comfort level. This study aims to use an environmentally friendly nanoparticle of date pits to create thermal insulations that can be used in buildings. Different ratios of the nanoparticle of the date pits and sand composite were investigated. Fourier transform infrared spectroscopy and scanning electron microscopy were used to characterize the new materials. The material with nanoparticles of date pits and 50% by-volume epoxy provided good thermal insulation with thermal conductivity of 0.26 W⁄mK that could be used in the existing buildings. This has the potential to reduce the overall energy consumption by 4,494 kWh and thereby reduce CO2 emissions of a 570 m2 house by 1.8 tons annually. In conclusion, the future of using nanoparticles of date pits in construction is bright and promising due to their promising results. Thermal insulation ; Nanomaterials ; Energy consumption ; Thermal comfort ; Thermal conductivity ; Thermal ; Environmental science ; Materials science ; Environmentally friendly ; Building insulation ; Efficient energy use ; Nanoparticle ; Thermal energy ; Composite material ; Architectural engineering ; Nanotechnology ; Engineering ; Meteorology ; Ecology Natural Fiber Reinforced Composites
Authors: Farooq Aslam | Zhen Li | Guanghao Qu | Yang Feng | Shijun Li | Shengtao Li | Hangyin Mao
Source: Materials (article, 2021-03-08)
Field: Materials Science — Materials Chemistry

Improvement of DC Breakdown Strength of the Epoxy/POSS Nanocomposite by Tailoring Interfacial Electron Trap Characteristics To date, breakdown voltage is an underlying risk to the epoxy-based electrical high voltage (HV) equipment. To improve the breakdown strength of epoxy resin and to explore the formation of charge traps, in this study, two types of polyhedral oligomeric silsesquioxane (POSS) fillers are doped into epoxy resin. The breakdown voltage test is performed to investigate the breakdown strength of neat epoxy and epoxy/POSS composites. Electron traps that play an important role in breakdown strength are characterized by thermally stimulated depolarized current (TSDC) measurement. A quantum chemical calculation tool identifies the source of traps. It is found that adding octa-glycidyl POSS (OG-POSS) to epoxy enhances the breakdown strength than that of neat epoxy and epoxycyclohexyl POSS (ECH-POSS) incorporated epoxy. Moreover, side groups of OG-POSS possess higher electron affinity (<i>E</i><sub>A</sub>) and large electronegativity that introduces deep-level traps into epoxy resin and restrain the electron transport. In this work, the origin of traps has been investigated by the simulation method. It is revealed that the functional properties of POSS side group can tailor an extensive network of deep traps in the interfacial region with epoxy and enhance the breakdown strength of the epoxy/POSS nanocomposite. Epoxy ; Materials science ; Silsesquioxane ; Composite material ; Nanocomposite ; Breakdown voltage ; Voltage ; Polymer ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Christian Gianoglio | Edoardo Ragusa | Paolo Gastaldo | F. Gallesi | F. Guastavino
Source: Energies (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors. Partial discharge ; Dependency (UML) ; Convolutional neural network ; Voltage ; Process (computing) ; Reliability engineering ; Computer science ; High voltage ; Artificial neural network ; Electrical engineering ; Engineering ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Farzaneh Shayeganfar | Rouzbeh Shahsavari
Source: Scientific Reports (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Deep Learning Method to Accelerate Discovery of Hybrid Polymer-Graphene Composites Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO<sub>2</sub>) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO<sub>2</sub> offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices. Graphene ; Composite material ; Polymer ; Materials science ; Computer science ; Nanotechnology Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Mingxing Tian | Hongchen Li | Huiying Zhang
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Model for Magnetization Characteristics of Ferromagnetic Materials The magnetic characteristics of silicon steel sheet 30Q120 under different AC frequencies were measured by an Epstein frame in order to analyze the effects of frequency variation on the hysteresis loop of ferromagnetic materials and compare the differences of such materials at different frequencies. First, the forecasting method of the magnetic properties of ferromagnetic materials under the influence of frequency using neural network was proposed based on the measured experimental data. Hysteresis loops at different frequencies were obtained. Then, the obtained results were compared with the measured results. Second, the dynamic Jiles-Atherton hysteresis model was established based on the Jiles-Atherton hysteresis theory, and hysteresis loops at different frequencies were obtained. The accuracies of the neural network model and Jiles-Atherton hysteresis model were verified by comparing the simulation results with the measured data. Upon comparing the dynamic Jiles-Atherton hysteresis and the neural network hysteresis models, results show that the latter has better accuracy. Furthermore, the correctness and effectiveness of the proposed method are verified. Magnetization ; Ferromagnetism ; Artificial neural network ; Condensed matter physics ; Magnetic domain ; Materials science ; Computer science ; Stoner–Wohlfarth model ; Physics ; Artificial intelligence ; Magnetic field Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Power Systems and Control
Authors: Chen Zhang | Yewei Yu | Yifan Wang | Zhiwu Han | Miaolei Zhou
Source: IEEE Transactions on Magnetics (article, 2021-03-12)
Field: Materials Science — Materials Chemistry

Chaotic Neural Network-Based Hysteresis Modeling With Dynamic Operator for Magnetic Shape Memory Alloy Actuator The magnetic shape memory alloy (MSMA) is a new family of smart materials, which exhibits great strain deformation and high energy density. Based on these properties, the MSMA has excellent potential to represent an available means for developing a novel generation of actuators in the micro-positioning application. However, the MSMA-based actuator suffers from the inherent hysteresis and it has become a bottleneck in the industrial application. A hybrid hysteresis model, which consists of a simple dynamic hysteresis operator (SDHO) and chaotic neural network (CNN), is proposed in this article. This developed model possesses a concise construction and distinguished generalization capability. By conducting comparative experiments, the proposed approach has a superior ability to predict the hysteresis behaviors under various input signals. Hysteresis ; Shape-memory alloy ; Actuator ; Chaotic ; Computer science ; Artificial neural network ; Magnetic hysteresis ; Bottleneck ; Control theory (sociology) ; Magnetic shape-memory alloy ; Operator (biology) ; Generalization ; Smart material ; Materials science ; Mechanical engineering ; Magnetic field ; Magnetization ; Artificial intelligence ; Physics ; Mathematical analysis ; Magnetic domain ; Mathematics ; Engineering ; Condensed matter physics ; Nanotechnology ; Control (management) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control
Authors: Monika Gešvandtnerová | Dario Rocca | Tomáš Bučko
Source: Journal of Catalysis (article, 2021-02-20)
Field: Chemistry — Inorganic Chemistry

Methanol carbonylation over acid mordenite: Insights from ab initio molecular dynamics and machine learning thermodynamic perturbation theory Chemistry ; Mordenite ; Carbonylation ; Ab initio ; Density functional theory ; Molecular dynamics ; Computational chemistry ; Perturbation theory (quantum mechanics) ; Methanol ; Catalysis ; Reaction dynamics ; Thermodynamics ; Zeolite ; Molecule ; Quantum mechanics ; Organic chemistry ; Physics ; Carbon monoxide Zeolite Catalysis and Synthesis ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Thomas Blum | Jeffery Graves | Michael J. Zachman | Felipe Polo‐Garzon | Zili Wu | Ramakrishnan Kannan | Xiaoqing Pan | Miaofang Chi
Source: Small Methods (article, 2021-02-25)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets Forming an ultra-thin, permeable encapsulation oxide-support layer on a metal catalyst surface is considered an effective strategy for achieving a balance between high stability and high activity in heterogenous catalysts. The success of such a design relies not only on the thickness, ideally one to two atomic layers thick, but also on the morphology and chemistry of the encapsulation layer. Reliably identifying the presence and chemical nature of such a trace layer has been challenging. Electron energy-loss spectroscopy (EELS) performed in a scanning transmission electron microscope (STEM), the primary technique utilized for such studies, is limited by a weak signal on overlayers when using conventional analysis methods, often leading to misinterpreted or missed information. Here, a robust, unsupervised machine learning data analysis method is developed to reveal trace encapsulation layers that are otherwise overlooked in STEM-EELS datasets. This method provides a reliable tool for analyzing encapsulation of catalysts and is generally applicable to any spectroscopic analysis of materials and devices where revealing a trace signal and its spatial distribution is challenging. Encapsulation (networking) ; Scanning transmission electron microscopy ; Materials science ; Transmission electron microscopy ; Nanotechnology ; Computer science ; Electron energy loss spectroscopy Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Mingjie Liu | Yilin Yang | John R. Kitchin
Source: The Journal of Chemical Physics (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Semi-grand canonical Monte Carlo simulation of the acrolein induced surface segregation and aggregation of AgPd with machine learning surrogate models The single atom alloy of AgPd has been found to be a promising catalyst for the selective hydrogenation of acrolein. It is also known that the formation of Pd islands on the surface will greatly reduce the selectivity of the reaction. As a result, the surface segregation and aggregation of Pd on the AgPd surface under reaction conditions of selective hydrogenation of acrolein are of great interest. In this work, we lay out a workflow that can predict the surface segregation and aggregation of Pd on a FCC(111) AgPd surface with and without the presence of acrolein. We use machine learning surrogate models to predict the AgPd bulk energy, AgPd slab energy, and acrolein adsorption energy on AgPd slabs. Then, we use the semi-grand canonical Monte Carlo simulation to predict the surface segregation and aggregation under different bulk Pd concentrations. Under vacuum conditions, our method predicts that only trace amount of Pd will exist on the surface at Pd bulk concentrations less than 20%. However, with the presence of acrolein, Pd will start to aggregate as dimers on the surface at Pd bulk concentrations as low as 6.5%. Acrolein ; Chemistry ; Monte Carlo method ; Adsorption ; Desorption ; Chemical physics ; Physical chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Xiaoting Zhong | Brian Gallagher | K. L. Eves | Emily Robertson | T. Nathan Mundhenk | T. Yong-Jin Han
Source: npj Computational Materials (article, 2021-10-05)
Field: Materials Science — Materials Chemistry

A study of real-world micrograph data quality and machine learning model robustness Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate. Micrograph ; Robustness (evolution) ; Artificial intelligence ; Normalization (sociology) ; Pixel ; Scanning electron microscope ; Electron micrographs ; Computer science ; Microscope ; Computer vision ; Pattern recognition (psychology) ; Biological system ; Image processing ; Materials science ; Electron microscope ; Optics ; Chemistry ; Composite material ; Image (mathematics) ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Peiyuan Gao | Xiu Yang | Yuhang Tang | Muqing Zheng | Amity Andersen | Vijayakumar Murugesan | Aaron Hollas | Wei Wang
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, p<i>K</i><sub>a</sub> and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using the Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model for electrostatic interaction, the nonpolar interaction contribution of the solvent and the conformational entropy of the solute in the solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of the conformational entropy of the solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal mol<sup>-1</sup> for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined. Solvation ; Chemistry ; Molecular descriptor ; Molecule ; Implicit solvation ; Gaussian ; Computational chemistry ; Thermodynamics ; Statistical physics ; Chemical physics ; Quantitative structure–activity relationship ; Organic chemistry ; Physics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Advanced Battery Technologies Research