jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mihaela-Ligia Ungureșan | Vlad Mureşan
Source: International Journal of Nonlinear Sciences and Numerical Simulation (article, 2021-03-20)
Field: Chemistry — Organic Chemistry

Fast interaction of Cu<sup>2+</sup> with S<sub>2</sub>O<sub>3</sub> <sup>2−</sup> in aqueous solution Abstract In this paper, a comparison between the experimental values for the kinetics of the fast redox reaction between Cu 2+ and S 2 O 3 2− and some possible variants of analogical modeling and numerical simulation for this pre-equilibrium reaction have been presented. One of them is based on a function with a periodical, strongly under-damped component. For a non-periodical fast damped evolution of reaction between Cu 2+ and S 2 O 3 2− , this paper proposes a variant of numerical modeling and simulation based on two exponential functions. For this complex reaction kinetics, the proposed approach based on application of neural networks is an efficient and accurate tool to solve modeling problems. The method ensures a good approximation of the experimental data, with a remarkable flexibility of analyses and synthesis as elaborated in the paper. The associated numerical simulation operates with an easy and flexible program, which allows the change in large limits of some structure parameters, for the adaptation of the numerical results with the experimental measurements. Aqueous solution ; Kinetics ; Chemical kinetics ; Computer simulation ; Flexibility (engineering) ; Function (biology) ; Numerical analysis ; Exponential function ; Redox ; Statistical physics ; Computer science ; Physics ; Thermodynamics ; Applied mathematics ; Chemistry ; Physical chemistry ; Mathematics ; Simulation ; Mathematical analysis ; Classical mechanics ; Inorganic chemistry Free Radicals and Antioxidants ; Metal complexes synthesis and properties ; Chemical and Physical Properties in Aqueous Solutions
Authors: Nicodemo Di Pasquale | Joshua D. Elliott | Panagiotis Hadjidoukas | Paola Carbone
Source: arXiv (Cornell University) (preprint, 2021-03-30)
Field: Materials Science — Materials Chemistry

Dynamically polarisable force-fields for surface simulations via\n multi-output classification Neural Networks We present a general procedure to introduce electronic polarization into\nclassical Molecular Dynamics (MD) force-fields using a Neural Network (NN)\nmodel. We apply this framework to the simulation of a solid-liquid interface\nwhere the polarization of the surface is essential to correctly capture the\nmain features of the system. By introducing a multi-input, multi-output NN and\ntreating the surface polarization as a discrete classification problem, for\nwhich NNs are known to excel, we are able to obtain very good accuracy in terms\nof quality of predictions. Through the definition of a custom loss function we\nare able to impose a physically motivated constraint within the NN itself\nmaking this model extremely versatile, especially in the modelling of different\nsurface charge states. The NN is validated considering the redistribution of\nelectronic charge density within a graphene based electrode in contact with\naqueous electrolyte solution, a system highly relevant to the development of\nnext generation low-cost supercapacitors. We compare the performances of our\nNN/MD model against Quantum Mechanics/Molecular dynamics simulations where we\nobtain a most satisfactorily agreement.\n Artificial neural network ; Molecular dynamics ; Computer science ; Polarization (electrochemistry) ; Statistical physics ; Charge density ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Neural Networks and Applications
Authors: Itsuki Miyazato | Keisuke Takahashi
Source: MEMBRANE (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Materials Informatics : Summary and Examples The introduction of materials informatics has changed the way of understanding and designing the materials. The idea is to design materials from materials data utilizing the data science techniques such as data visualization and machine learning. Here, the concept and methods taken in materials informatics are explored along its applications, thus, it helps to understand the how materials informatics are used in materials science. Materials informatics ; Informatics ; Computer science ; Data science ; Engineering informatics ; Visualization ; Health informatics ; Engineering ; Artificial intelligence Machine Learning in Materials Science
Authors: Chao Shu | Zhuoran Xin | Cheng Xie
Source: arXiv (Cornell University) (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials. However, tissue analysis methods of materials science, such as metallographic analysis, XRD analysis, and EBSD analysis, cannot directly establish a complete quantitative relationship between tissue structure and performance. Therefore, this paper proposes a novel data-knowledge-driven organization representation and performance prediction method to obtain a quantitative structure-performance relationship. First, a knowledge graph based on EBSD is constructed to describe the material's mesoscopic microstructure. Then a graph representation learning network based on graph attention is constructed, and the EBSD organizational knowledge graph is input into the network to obtain graph-level feature embedding. Finally, the graph-level feature embedding is input to a graph feature mapping network to obtain the material's mechanical properties. The experimental results show that our method is superior to traditional machine learning and machine vision methods. Computer science ; Graph ; Embedding ; Feature (linguistics) ; Electron backscatter diffraction ; Theoretical computer science ; Artificial intelligence ; Representation (politics) ; Microstructure ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Eugen Hruška | Ariel Gale | Fang Liu
Source:  (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations. Redox ; Solvation ; Solvent ; Chemistry ; Solvent models ; Density functional theory ; Solvent effects ; Implicit solvation ; Computational chemistry ; Embedding ; Chemical physics ; Computer science ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Source: Pump Industry Analyst (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

The Gorman-Rupp Co, USA The microcrystalline structure of (Bi1-xSbx)2Te3 (p-type) and (Bi1-xSex)2Te3 (n-type) solid solutions was studied using optical (OM) and scanning electronic microscopes (SEM), and was characterized by X-ray diffraction (XRD) and X-ray energy dispersive spectroscopy (EDS) in SEM. These semiconductor thermoelectric materials were obtained by the hot extrusion method, which is used in practical production of thermoelements. The extruded pieces were polycrystals with crystallite sizes 1–50 µm and 1–30 μm for p-type and n-type, respectively. SEM images showed that the crystallites of these extruded pieces had a nanocrystalline structure formed by nanocrystals and voids. The nanostructure morphology was studied using an artificial neural network (ANN). The p-type extrusions consisted of spherical nanocrystals with 10–100 nm dimensions, which formed the crystallites with dimensions up to 300 nm. The n-type extrusions consisted out of interwoven fibers of different orientation with up to 50 μm length and 20–200 nm diameters. Crystallite ; Nanocrystalline material ; Materials science ; Microcrystalline ; Thermoelectric effect ; Scanning electron microscope ; Nanostructure ; Crystallography ; Extrusion ; Nanocrystal ; Nanotechnology ; Composite material ; Metallurgy ; Chemistry Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys
Authors: Chaoyi Chang | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2021-05-26)
Field: Materials Science — Materials Chemistry

Application of DFTB and machine learning to evaluate the stability of biomass intermediates on the Rh(111) surface Biomass compounds adsorbed on surfaces are challenging to study due to the large number of possible species and adsorption geometries. In this work, possible intermediates of erythrose, glyceraldehyde, glycerol and propionic acid are studied on the Rh(111) surface. The intermediates and elementary reactions are generated from first 2 recursions of a recursive bond-breaking algorithm. These structures are used as the input of an unsupervised Mol2Vec algorithm to generate vector descriptors. A data-driven scheme to classify the reactions is developed and adsorption energies are predicted. The lowest mean absolute error (MAE) of our prediction on adsorption energies is 0.39 eV, and the relative ordering of different surface adsorption geometries is relatively accurate. We show that combining geometries from density functional tight-binding (DFTB) calculations with energies from machine-learning predictions provides a novel workflow for rapidly assessing the stability of various molecular geometries on the Rh(111) surface. Adsorption ; Stability (learning theory) ; Chemistry ; Surface (topology) ; Work (physics) ; Tight binding ; Computational chemistry ; Chemical physics ; Physics ; Computer science ; Mathematics ; Physical chemistry ; Thermodynamics ; Machine learning ; Electronic structure ; Geometry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Sterling G. Baird | Taylor D. Sparks
Source:  (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

High-throughput Calculation of Atomic Planar Density for Compounds A large collection of element-wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods. We demonstrate that the element-wise max lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open-source Mathematica package hosted at https://github.com/sgbaird/LatticePlane. Planar ; Computer science ; Computational science ; Brute force ; Lattice (music) ; Element (criminal law) ; Algorithm ; Computational physics ; Physics ; Computer graphics (images) ; Acoustics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Satvik Lolla | Haotong Liang | A. Gilad Kusne | Ichiro Takeuchi | William Ratcliff
Source: arXiv (Cornell University) (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Semi-Supervised Approach for Automatic Crystal Structure Classification The structural solution problem can be a daunting and time consuming task. Especially in the presence of impurity phases, current methods such as indexing become more unstable. In this work, we apply the novel approach of semi-supervised learning towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. Our semi-supervised generative deep learning model can train on both labeled data -- diffraction patterns with the associated crystal structure -- and unlabeled data, diffraction patterns that lack this information. This approach allows our models to take advantage of the troves of unlabeled data that current supervised learning approaches cannot, which should result in models that can more accurately generalize to real data. In this work, we classify powder diffraction patterns into all 14 Bravais lattices and 144 space groups (we limit the number due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies. Our models also drastically outperform current deep learning approaches for both space group and Bravais Lattice classification using less training data. Bravais lattice ; Artificial intelligence ; Computer science ; Diffraction ; Machine learning ; Supervised learning ; Crystal system ; Lattice (music) ; Pattern recognition (psychology) ; Crystal structure ; Artificial neural network ; Chemistry ; Physics ; Crystallography ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Christopher Yeung | Ryan Tsai | Benjamin Pham | Brian King | Yusaku Kawagoe | David Ho | Julia Liang | Mark W. Knight | Aaswath P. Raman
Source: Conference on Lasers and Electro-Optics (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Conditional Machine Learning-Based Inverse Design Across Multiple Classes of Nanophotonic Structures We present a machine learning-based photonics design strategy centered on encoding image colors with material and structural data. Given input target spectra, our model can accurately determine the optimal metasurface class, materials, and structure. Nanophotonics ; Computer science ; Inverse ; Photonics ; Encoding (memory) ; Class (philosophy) ; Artificial intelligence ; Inverse problem ; Algorithm ; Theoretical computer science ; Optoelectronics ; Mathematics ; Materials science ; Geometry Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Photonic Crystals and Applications
Authors: Shengli Jiang | Zhuo Xu | Medhavi Kamran | Stas Zinchik | Sidike Paheding | Armando G. McDonald | Ezra Bar‐Ziv | Ví­ctor M. Zavala
Source: ChemRxiv (preprint, 2021-04-29)
Field: Chemistry — Analytical Chemistry

Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste &lt;p&gt;We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.&lt;/p&gt; Attenuated total reflection ; Fourier transform infrared spectroscopy ; Convolutional neural network ; Plastic waste ; Fourier transform ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Materials science ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Biological system ; Chemistry ; Mathematics ; Physics ; Optics ; Environmental chemistry ; Engineering ; Waste management ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses
Authors: Gaétan Laurens | Jacek Goniakowski | Julien Lam
Source: arXiv (Cornell University) (preprint, 2021-08-24)
Field: Materials Science — Materials Chemistry

Non-classical nucleation of zinc oxide from a physically-motivated machine-learning approach Observing non-classical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fields that can capture the whole complexity of their underlying interatomic interactions and an advanced structural analysis. Here, we first report the construction of a machine-learning force field for zinc oxide interactions using the Physical LassoLars Interaction Potentials approach which allows us to be predictive even for untrained structures. Then, we carried out freezing simulations from a liquid and observed the crystal formation with atomistic precision. Our results, which are analyzed using a data-driven approach based on bond order parameters, demonstrate the presence of both prenucleation clusters and two-step nucleation scenarios thus retrieving seminal predictions of non-classical nucleation pathways made on much simpler models. Nucleation ; Computer science ; Statistical physics ; Zinc ; Field (mathematics) ; Chemical physics ; Materials science ; Physics ; Thermodynamics ; Mathematics ; Metallurgy Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallization and Solubility Studies
Authors: Alexis M. Fenton | Fikile R. Brushett
Source: ChemRxiv (preprint, 2021-04-26)
Field: Chemistry — Electrochemistry

Using Voltammetry Augmented with Physics-Based Modeling and Bayesian Hypothesis Testing to Estimate Electrolyte Composition Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in electrolytes and as such has been used in numerous fields of study. Recently, automation has been introduced into voltammetric analyses to extend their capabilities (e.g., Bayesian parameter estimation, compound identification via machine learning); however, opportunities exist to enable more versatile methods across a wider range of electrolyte and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of electroactive species in multicomponent electrolytes across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled an electrolyte containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating its ability to identify electroactive constituents of a multicomponent solution. Finally, we identify areas of further improvement (e.g., achieving greater detection accuracy) and future applications to potentially enhance in situ or operando diagnostic workflows. Electrolyte ; Voltammetry ; Cyclic voltammetry ; Phenothiazine ; Bayesian probability ; Protocol (science) ; Computer science ; Bayesian inference ; Biological system ; Electrochemistry ; Materials science ; Chemistry ; Artificial intelligence ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source: ChemRxiv (preprint, 2021-07-18)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields forOrganic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Force field (fiction) ; Transferability ; Flexibility (engineering) ; Dihedral angle ; Cluster (spacecraft) ; Molecular dynamics ; Statistical physics ; Mean squared error ; Raw data ; Molecule ; Computer science ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Artificial intelligence ; Quantum mechanics ; Machine learning ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications