jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kailiang Lu | Dongping Chang | Xiaobo Ji | Minjie Li | Wencong Lu
Source: International Journal of Electrochemical Science (article, 2021-10-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Aided Discovery of the Layered Double Hydroxides with the Largest Basal Spacing for Super-Capacitors Super capacitors with layered double hydroxides (LDHs) have excellent specific capacitance and cycling performance due to their unique layered structures and rich REDOX sites. The basal spacing (dspacing) of LDHs can be controlled by selecting optimal metal cations and interlayer anions. In general, the greater the dspacing of LDHs electrode materials, the greater the specific capacitance of the super-capacitors. In this work, the machine learning model was utilized to seek for novel LDHs materials with the larger dspacing. The genetic algorithm combined machine learning approaches were utilized to select the appropriate feature subset including atomic parameters and chemical compositions of LDHs. The Extreme Gradient Boosting model was established to predict the dspacing of LDHs. The correlation coefficient between predicted dspacing and experimental dspacing reached as high as 0.94 for the training set in leave-one-out cross-validation (LOOCV) and 0.89 for the independent testing set, respectively. The high-throughput screening of new LDHs with larger dspacing was carried out by using our online computation platform for materials data mining (OCPMDM). The dspacing of designed LDHs (Co0.67Fe0.33[Fe(CN)6]0.11•(OH)2) was predicted to be 12.40Å, increasing by 10.91% compared to the maximum dspacing (11.18Å) of Mg0.67Al0.33 [Fe(CN)6]0.08•(OH)2 reported. The online platform for predicting dspacing of unknown LDHs can be accessible for the public on the web server: http://materials-data-mining.com/online_model/LDHs_basal_spacing_model. Basal (medicine) ; Capacitor ; Materials science ; Process (computing) ; Computer science ; Medicine ; Engineering ; Electrical engineering ; Internal medicine ; Operating system Supercapacitor Materials and Fabrication ; Polyoxometalates: Synthesis and Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: Huan Chen | Zhe Chen | Geming Wang | Quanrong Deng | Lei Yao | Hongmei Wang
Source: ACS Applied Electronic Materials (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Inductive Effect in MXene/Poly(Ether Sulfone) Membrane during Ion Diffusion In this paper, a MXene/poly(ether sulfone) (PES) membrane was prepared via vacuum filtration method, and an inductive effect appears during the ion diffusion process in the electrolyte/membrane/water system under alternating current electric field. Both the larger ion size and higher concentration of the electrolyte lead to a stronger inductive effect. This inductive effect should contribute to the hindrance on the ions in the channel between the MXene sheets during the membrane. The backpropagation (BP) neural network was employed to investigate the contributions of the factors that influence inductance. The obtained MXene/PES membrane could be potentially applied to construct microsized inductors. Membrane ; Sulfone ; Electrolyte ; Ion ; Materials science ; Diffusion ; Ether ; Electric field ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Polymer chemistry ; Organic chemistry ; Physical chemistry ; Electrode MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: K.B. Brito | Edson Guedes da Costa | Bruno Albuquerque Dias | Marcus Tulius Barros. Florentino | George R. S. Lira
Source: International Journal of Electrical Power & Energy Systems (article, 2021-07-29)
Field: Materials Science — Materials Chemistry

Development of DIP-based algorithm for extraction of UV video attributes from corona discharges on polymeric insulators Insulator (electricity) ; Corona (planetary geology) ; Algorithm ; Ultraviolet ; Computer science ; Corona discharge ; Materials science ; Voltage ; Electrical engineering ; Engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Aerosol Filtration and Electrostatic Precipitation
Authors: Henry Chan | Badri Narayanan | Mathew J. Cherukara | Troy D. Loeffler | Michael Sternberg | Anthony Avarca | Subramanian K. R. S. Sankaranarayanan
Source: MRS Advances (article, 2021-03-08)
Field: Materials Science — Materials Chemistry

BLAST: bridging length/timescales via atomistic simulation toolkit Bridging (networking) ; Molecular dynamics ; Transferability ; Scalability ; Computer science ; Force field (fiction) ; High fidelity ; Fidelity ; Field (mathematics) ; Nanotechnology ; Materials science ; Artificial intelligence ; Physics ; Machine learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Chemical Physics Studies
Authors: Toshiyuki Takayanagi
Source: The Journal of Physical Chemistry A (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Application of Reaction Path Search Calculations to Potential Energy Surface Fits There has been significant progress in recent years in the use of machine learning techniques to model high-dimensional reactive potential energy surfaces using large-scale data obtained from <i>ab initio</i> electronic structure calculations. In these methods, the strategy used to gather data becomes a key issue as the molecular size increases. In this work, we examine the applicability of the reaction path search algorithm implemented in the Global Reaction Route Mapping (GRRM) code as a data-gathering approach. The electronic energies and gradients sampled by using the GRRM calculation are directly used in potential energy surface fitting to a permutationally invariant polynomial function. This simple approach was applied to the HNS and HCNO reaction systems, and we found that the fitted potential energy surfaces reasonably reproduce the features of the electronic structure calculations used in the GRRM calculations. This suggests that the GRRM sampling scheme can be used to construct an initial potential energy surface. Potential energy surface ; Surface (topology) ; Potential energy ; Computer science ; Ab initio ; Invariant (physics) ; Path (computing) ; Statistical physics ; Energy (signal processing) ; Electronic structure ; Polynomial ; Work (physics) ; Computational physics ; Physics ; Mathematics ; Atomic physics ; Geometry ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Minoru Kusaba | Chang Liu | Yukinori Koyama | Kiyoyuki Terakura | Ryo Yoshida
Source: Scientific Reports (article, 2021-02-26)
Field: Materials Science — Materials Chemistry

Recreation of the periodic table with an unsupervised machine learning algorithm In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for a tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping, which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev's periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces. Computer science ; Table (database) ; Periodic table ; Embedding ; Grid ; Algorithm ; Generative grammar ; Feature (linguistics) ; Artificial intelligence ; Element (criminal law) ; Point (geometry) ; Machine learning ; Data mining ; Mathematics ; Geometry ; Chemistry Machine Learning in Materials Science ; Image Retrieval and Classification Techniques ; Graph Theory and Algorithms
Authors: Karl Garbrecht | Miguel A. Aguiló | Allen R. Sanderson | Anthony D. Rollett | Robert M. Kirby | Jacob Hochhalter
Source: Integrating materials and manufacturing innovation (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Texture-Dependent Constitutive Models with Automatic Code Generation for Topological Optimization Symbolic regression ; Viscoplasticity ; Interpretability ; Computer science ; Topology optimization ; Machine learning ; Genetic programming ; Constitutive equation ; Artificial intelligence ; Topology (electrical circuits) ; Algorithm ; Mathematics ; Engineering ; Structural engineering ; Finite element method Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes
Authors: G. N. Kozhemyakin | Yurii Belov | A.N. Parashenko | В. В. Артемов | O. N. Soklakova
Source: Materials Science and Engineering B (article, 2021-06-02)
Field: Materials Science — Materials Chemistry

Morphology and nanostructured features in BiSbTe and BiSeTe solid solutions obtained by hot extrusion Extrusion ; Morphology (biology) ; Materials science ; Chemical engineering ; Composite material ; Geology ; Engineering ; Paleontology Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials ; Thermography and Photoacoustic Techniques
Authors: Sai Mani Prudhvi Valleti | Qiang Zou | Rui Xue | Lukáš Vlček | Maxim Ziatdinov | Rama K. Vasudevan | Mingming Fu | Jiaqiang Yan | David Mandrus | Zheng Gai | Sergei V. Kalinin
Source: ACS Nano (article, 2021-06-09)
Field: Materials Science — Materials Chemistry

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions. Statistical physics ; Bayes' theorem ; Workflow ; Bayesian inference ; Bayesian probability ; Generative model ; Bayesian optimization ; Ising model ; Computer science ; Parameter space ; Materials science ; Biological system ; Algorithm ; Artificial intelligence ; Physics ; Generative grammar ; Mathematics ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Theoretical and Computational Physics
Authors: Hang Min | Feifeng Wu | Jiaqiang Yang | Xianbao Duan | Yanwei Wen | Feng Xie | Bin Shan
Source: Computational Materials Science (article, 2021-05-03)
Field: Materials Science — Materials Chemistry

Development of an interatomic potential for Fe-He by neural network Helium ; Ab initio ; Atom (system on chip) ; Molecular dynamics ; Interatomic potential ; Ab initio quantum chemistry methods ; Binding energy ; Atomic physics ; Helium atom ; Materials science ; Atomic units ; Matrix (chemical analysis) ; Chemistry ; Molecular physics ; Chemical physics ; Computational chemistry ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Niklas Fehlemann | Yannik Sparrer | Felix Pütz | Markus Könemann | Sebastian Münstermann
Source: IOP Conference Series Materials Science and Engineering (article, 2021-06-01)
Field: Materials Science — Metals and Alloys

Influence of synthetically generated inclusions on the stress accumulation and concentration in X65 pipeline steel Abstract The use of a simulative approach with representative volume elements (RVE’s) is particularly well suited to investigate the influence of different microstructural parameters on the damage behavior of a material. In order to statistically analyze the individual components of the microstructure (e.g. geometric structure of grains and inclusions), well-known distribution functions such as logNormal/Gamma are normally used, but these do not take into account the interdependencies between the different parameters. However, newer approaches like machine learning techniques can only describe one phase of a single material at a time. Therefore, in this study, we extended an existing Wasserstein Generative Adversarial Network (WGAN) to a Conditional Wasserstein GAN with Gradient Penalty (CWGAN-GP), with which it is possible to process multiple materials/phases simultaneously. Training this algorithm on different steels and associated inclusions showed that a single trained network can generate synthetic microstructure for all different phases and materials with very high quality. A newly implemented evaluation method using the regularized Wasserstein-distance confirmed the excellent agreement of the real data with the synthetic data for all phases/materials. As a use case for our algorithm, the influence of different inclusions on the stress accumulation and concentration of X65 pipeline steel was investigated to find initiation sites for damage in the material. These investigations showed a pronounced correlation between stress concentration and inclusion parameters, thus confirming the usefulness of the CWGAN-GP as an input-generator for RVE’s. Pipeline (software) ; Materials science ; Generator (circuit theory) ; Microstructure ; Stress (linguistics) ; Process (computing) ; Log-normal distribution ; Biological system ; Computer science ; Inclusion (mineral) ; Composite material ; Mathematics ; Geology ; Mineralogy ; Thermodynamics ; Statistics Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Corrosion Behavior and Inhibition
Authors: Michela Pauletti | Vladimir V. Rybkin | Marcella Iannuzzi
Source: Journal of Chemical Theory and Computation (article, 2021-09-10)
Field: Materials Science — Materials Chemistry

Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn–Sham Accuracy Simulations based on electronic structure theory naturally include polarization and have no transferability problems. In particular, Kohn-Sham density functional theory (KS-DFT) has become the method of reference for ab initio molecular dynamics simulations of condensed matter systems. However, the high computational cost often poses strict limits on the affordable system size as well as on the extension of sampling (number of configurations). In this work, we propose an improvement to the subsystem density functional theory approach, known as the Kim-Gordon (KG) scheme, thus enabling the sampling of configurations for condensed molecular systems keeping the KS-DFT level accuracy at a fraction of computer time. Our scheme compensates the known KG shortcomings of the electronic kinetic energy term by adding a simple correction and can match KS-DFT accuracy in energies and forces. The computationally cheap correction is determined by means of a machine learning procedure. The proposed KG scheme is applied within a linear scaling self-consistent field formalism and is assessed by a series of molecular dynamics simulations of liquid water under different conditions. Although system-dependent, the correction is transferable between system sizes and temperatures. Density functional theory ; Kohn–Sham equations ; Computer science ; Molecular dynamics ; Linear scale ; Statistical physics ; Scaling ; Formalism (music) ; Orbital-free density functional theory ; Transferability ; Time-dependent density functional theory ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Machine learning Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Romana Petry | Bruno Focassio | Gabriel R. Schleder | Diego Stéfani T. Martinez | A. Fazzio
Source: The Journal of Chemical Physics (article, 2021-06-09)
Field: Chemistry — Organic Chemistry

Conformational analysis of tannic acid: Environment effects in electronic and reactivity properties Polyphenols are natural molecules of crucial importance in many applications, of which tannic acid (TA) is one of the most abundant and established. Most high-value applications require precise control of TA interactions with the system of interest. However, the molecular structure of TA is still not comprehended at the atomic level, of which all electronic and reactivity properties depend. Here, we combine an enhanced sampling global optimization method with density functional theory (DFT)-based calculations to explore the conformational space of TA assisted by unsupervised machine learning visualization and then investigate its lowest energy conformers. We study the external environment's effect on the TA structure and properties. We find that vacuum favors compact structures by stabilizing peripheral atoms' weak interactions, while in water, the molecule adopts more open conformations. The frontier molecular orbitals of the conformers with the lowest harmonic vibrational free energy have a HOMO-LUMO energy gap of 2.21 (3.27) eV, increasing to 2.82 (3.88) eV in water, at the DFT generalized gradient approximation (and hybrid) level of theory. Structural differences also change the distribution of potential reactive sites. We establish the fundamental importance of accurate structural consideration in determining TA and related polyphenol interactions in relevant technological applications. Conformational isomerism ; Tannic acid ; Density functional theory ; Chemical physics ; Chemistry ; Computational chemistry ; Molecule ; Natural bond orbital ; Reactivity (psychology) ; Electronic structure ; HOMO/LUMO ; Molecular dynamics ; Organic chemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Phytochemicals and Antioxidant Activities
Authors: Atharva Deshpande | Shreyash Deshpande | Shaunak Dhande
Source:  (article, 2021-12-16)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy Based Milk Classification and Purity Prediction Milk adulteration is increasing day by day and people are finding newer ways and chemicals to adulterate milk. Water is the most common milk adulterant. Adulterated milk can impair the functioning of various organs of the body, causing heart problems, cancer, and in extreme cases, even death. Monitoring of milk quality is necessary for sustaining human health. To overcome this problem, a non-destructive system is proposed that detects milk purity using NIR Spectroscopy and machine learning. Dataset is created from cows and buffalos milk samples that are adulterated with varying water quantities. This dataset is used to determine if the sample is buffalo's milk or cow's milk using KNN. The acquired data is used to train model which predicts the sample purity using linear regression. Classification is done according to level of adulteration. This paper proposes a non-destructive technique to predict purity of unknown milk sample. Adulterant ; Sample (material) ; Cow milk ; Food science ; Mathematics ; Statistics ; Chromatography ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Azam Safarnejad | M. Reza Hormozi‐Nezhad | Hamid Abdollahi
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-05-28)
Field: Chemistry — Analytical Chemistry

Radial basis function-artificial neural network (RBF-ANN) for simultaneous fluorescent determination of cysteine enantiomers in mixtures Thioglycolic acid ; Chemistry ; Analyte ; Enantiomer ; Radial basis function ; Partial least squares regression ; Artificial neural network ; Chromatography ; Organic chemistry ; Artificial intelligence ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: John A. Hodge | Kumar Vijay Mishra | Amir I. Zaghloul
Source: arXiv (Cornell University) (preprint, 2021-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Inverse Design of Reconfigurable Metasurfaces for Future Communications Reconfigurable intelligent surfaces (RIS) have recently received significant attention as building blocks for smart radio environments and adaptable wireless channels. By altering the space- and time-varying electromagnetic (EM) properties, the RIS transforms the inherently stochastic nature of the wireless environment into a programmable propagation channel. Conventionally, designing RIS to yield the desired EM response requires trial-and-error by iteratively investigating a large possibility of various geometries and materials through thousands of full-wave EM simulations. In this context, deep learning (DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design. Instead of explicitly solving Maxwell's equations, DL models learn physics-based relationships through supervised training data. Further, generative adversarial networks are shown to synthesize novel RIS designs not previously seen in the literature. This article provides a synopsis of DL techniques for inverse RIS design and optimization to yield targeted EM response necessary for future wireless networks. Context (archaeology) ; Wireless ; Computer science ; Inverse ; Channel (broadcasting) ; Wireless network ; Inverse problem ; Computer engineering ; Electronic engineering ; Telecommunications ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis