jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changsu Kim | Jiyong Kim
Source: International Journal of Energy Research (article, 2022-08-13)
Field: Materials Science — Materials Chemistry

Machine learning‐based high‐throughput screening, strategical design and knowledge extraction of Pt/ <scp> Ce <sub>x</sub> Zr <sub>1</sub> </scp> <sub>−</sub> <scp> <sub>x</sub> O <sub>2</sub> </scp> catalysts for water gas shift reaction Hydrogen production through water-gas shift reaction (WGSR) is a paramount option for a sustainable hydrogen economy. ZrO2-incorporated CeO2 has been widely applied to WGSR using Pt-based catalysts because of its high oxygen storage capacity, reducibility, and CO oxidation. However, there are challenges in identifying the optimal reaction conditions and structure of Pt/CexZr1−xO2 catalysts due to a lack of understanding of the complex catalysis in WGSR over Pt/CexZr1−xO2. In this study, we developed a machine learning-based approach to predict the performance of Pt/CexZr1−xO2 catalysts using experimental data collected from the literature. Artificial neural networks (ANNs) were exploited to predict the CO conversion of the catalysts in a wide range of WGSR conditions. The ANN model analyzed 79 860 cases, including 110 catalysts, and provided the optimal catalyst structures for maximizing CO conversion in different operating conditions such as a specific reaction temperature, given steam-to-CO ratios, and limited Pt loading level. In addition, the extended analysis of the CO oxidation capability of CexZr1−xO2 as support reveals major governing properties that determine the performance of Pt/CexZr1−xO2 catalysts in WGSR, from support reducibility at a low-temperature to thermal stability at a high-temperature. Catalysis ; Water-gas shift reaction ; Hydrogen production ; Hydrogen ; Chemistry ; Materials science ; Chemical engineering ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Ankur K. Gupta | Krishnan Raghavachari
Source: Journal of Chemical Theory and Computation (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Three-Dimensional Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques such as three-dimensional (3D)-convolutional neural networks become a natural choice to associate a molecular property with its structure due to the inherent 3D nature of a molecule. However, traditional 3D input data structures are intrinsically sparse in nature, which tend to induce instabilities during the learning process, which in turn may lead to underfitted results. To address this deficiency, in this project, we propose to use quantum-chemically derived molecular topological features, namely, localized orbital locator and electron localization function, as molecular descriptors, which provide a relatively denser input representation in a 3D space. Such topological features provide a detailed picture of the atomic and electronic configuration and interatomic interactions in the molecule and hence are ideal for predicting properties that are highly dependent on the physical or electronic structure of the molecule. Herein, we demonstrate the efficacy of our proposed model by applying it to the task of predicting atomization energies for the QM9-G4MP2 data set, which contains ∼134k molecules. Furthermore, we incorporated the Δ-machine learning approach into our model, which enabled us to reach beyond benchmark accuracy levels (∼1.0 kJ mol<sup>-1</sup>). As a result, we consistently obtain impressive mean absolute errors of the order 0.1 kcal mol<sup>-1</sup> (∼0.42 kJ mol<sup>-1</sup>) versus the G4(MP2) theory using relatively modest models, which could potentially be improved further in a systematic manner using additional compute resources. Computer science ; Convolutional neural network ; Context (archaeology) ; Benchmark (surveying) ; Representation (politics) ; Artificial neural network ; Topology (electrical circuits) ; Process (computing) ; Chemical space ; Set (abstract data type) ; Function (biology) ; Artificial intelligence ; Chemistry ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hao Deng | Jeffrey Comer | Bin Liu
Source: Molecular Simulation (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

A high-dimensional neural network potential for molecular dynamics simulations of condensed phase nickel and phase transitions A high-dimensional neural network interatomic potential was developed and used in molecular dynamics simulations of condensed phase Ni and Ni systems with liquid–solid phase coexistence. The reference data set was generated by sampling the potential energy surface over a broad temperature-pressure domain using ab initio MD simulations to train a unified potential. Excellent agreement was achieved between bulk face-centred cubic nickel thermal expansion simulations and relevant experimental data. The same potential also yields accurate structures and diffusivities in the liquid state. The phase transition between liquid and solid phases was simulated using the two-phase interface method. The predicted melting point temperature is within a few kelvins of the literature value. The general methodology could be applied to describe crystals with much more complex phase behaviours. Molecular dynamics ; Phase (matter) ; Interatomic potential ; Melting point ; Materials science ; Phase transition ; Chemical physics ; Thermodynamics ; Nickel ; Ab initio ; Thermodynamic integration ; Statistical physics ; Molecular physics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; High Temperature Alloys and Creep
Authors: S. Quondam Antonio | Francesco Riganti Fulginei | Gabriele Maria Lozito | Antonio Faba | Alessandro Salvini | Vincenzo Bonaiuto | F. Sargeni
Source: Mathematics (article, 2022-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the energy versus the amplitude of the magnetic induction) of soft ferromagnetic materials at different operating frequencies is proposed herein. Firstly, an innovative Fe-Si magnetic alloy, grade 35H270, is experimentally characterized via an Epstein frame in a wide range of frequencies, from 1 Hz up to 600 Hz. Parts of the dynamic hysteresis loops obtained through the experiments are involved in the training of a feedforward neural network, while the remaining ones are considered to validate the model. The training procedure is accurately designed to, firstly, identify the optimum network architecture (i.e., the number of hidden layers and the number of neurons per layer), and then, to effectively train the network. The model turns out to be capable of reproducing the magnetization processes and predicting the dynamic energy losses of the examined material in the whole range of inductions and frequencies considered. In addition, its computational and memory efficiency make the model a useful tool in the design stage of electrical machines and magnetic components. Hysteresis ; Artificial neural network ; Magnetic hysteresis ; Dynamic range ; Computer science ; Feed forward ; Amplitude ; Materials science ; Energy (signal processing) ; Range (aeronautics) ; Feedforward neural network ; Shape-memory alloy ; Magnetization dynamics ; Biological system ; Magnetization ; Control theory (sociology) ; Physics ; Condensed matter physics ; Magnetic field ; Artificial intelligence ; Engineering ; Control engineering ; Optics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic properties of thin films
Authors: Alexandre Boulle | A. Debelle
Source: Machine Learning Science and Technology (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Convolutional neural network analysis of x-ray diffraction data: strain profile retrieval in ion beam modified materials Abstract This work describes a proof of concept demonstrating that convolutional neural networks (CNNs) can be used to invert x-ray diffraction (XRD) data, so as to, for instance, retrieve depth-resolved strain profiles. The determination of strain distributions in disordered materials is critical in several technological domains, such as the semiconductor industry for instance. Using numerically generated data, a dedicated CNN has been developed, optimized, and trained, with the ultimate objective of inferring spatial strain profiles on the sole basis of XRD data, without the need of a priori knowledge or human intervention. With the example ZrO 2 single crystals, in which atomic disorder and strain are introduced by means of ion irradiation, we investigate the physical parameters of the disordered material that condition the performances of the CNN. Simple descriptors of the strain distribution, such as the maximum strain and the strained depth, are predicted with accuracies of 94% and 91%, respectively. The exact shape of the strain distribution is predicted with a 82% accuracy, and 76% for strain levels &lt;2% where the amount of meaningful information in the XRD data is significantly decreased. The robustness of the CNN against the number of predicted parameters and the size of the training dataset, as well as the uniqueness of the solution in some challenging cases, are critically discussed. Finally, the potential of the CNN has been tested on real, experimental, data. Interestingly, while the CNN has not been trained to operate on experimental data, it still shows promising performances with predictions achieved in a few seconds and corresponding root-mean-square errors in the 0.12–0.17 range for a fully automated approach, vs. a 0.06–0.12 range for a classical, human-based, approach that, in turn, requires several tens of minutes to optimize the solution. While the overall accuracy of the CNN has to be improved, these results pave the way for a fully automated XRD data analysis. Convolutional neural network ; Diffraction ; Robustness (evolution) ; Materials science ; Experimental data ; Computer science ; A priori and a posteriori ; Strain (injury) ; Uniqueness ; Ion ; Artificial intelligence ; Algorithm ; Optics ; Physics ; Mathematics ; Chemistry ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yutaka Okuyama | Takeshi Ohmori
Source: Japanese Journal of Applied Physics (article, 2022-09-15)
Field: Materials Science — Surfaces, Coatings and Films

Automated measurement method based on deep learning for cross-sectional SEM images of semiconductor devices Abstract Feature length measurement in cross-sectional scanning electron microscopy images of modern semiconductor devices is time-consuming and laborious. We propose an automated measurement method based on deep learning technology and applied it to trench pattern images. The method combines two image-recognition tasks: (1) object detection for determining the coordinates of each unit of a pattern and (2) semantic segmentation for obtaining the boundaries of each area (mask, substrate, and background). By combining the results of these two tasks, typical feature lengths, such as width and depth, are precisely and immediately measured. The extraction speed of the proposed method is 240 times faster than manual measurement and provides measurement results independent of the engineer’s skills. Artificial intelligence ; Computer science ; Computer vision ; Feature (linguistics) ; Segmentation ; Pattern recognition (psychology) ; Feature extraction ; Object (grammar) ; Trench ; Materials science ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: Saptarshi Dutta | P. S. Robi
Source: Metals and Materials International (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Experimental Investigation and Modeling of Creep Curve of Zr–2.5Nb Alloy by Machine Learning Techniques Creep ; Materials science ; Solid mechanics ; Alloy ; Work (physics) ; Phase (matter) ; Composite material ; Predictability ; Metallurgy ; Thermodynamics ; Mathematics Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Fusion materials and technologies
Authors: Shashank Pathrudkar | Hsuan Ming Yu | Susanta Ghosh | Amartya Banerjee
Source: arXiv (Cornell University) (article, 2022-02-02)
Field: Materials Science — Materials Chemistry

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to important classes of materials such as nanotubes, nanoribbons, nanowires, miscellaneous chiral structures and nano-assemblies, for all of which, tuning the interplay of mechanical deformations and electronic fields is an active area of investigation in the literature. Our model incorporates global structural symmetries and atomic relaxation effects, benefits from the use of helical coordinates to specify the electronic fields, and makes use of a specialized data generation process that solves the symmetry-adapted equations of Kohn-Sham Density Functional Theory in these coordinates. Using armchair single wall carbon nanotubes as a prototypical example, we demonstrate the use of the model to predict the fields associated with the ground state electron density and the nuclear pseudocharges, when three parameters - namely, the radius of the nanotube, its axial stretch, and the twist per unit length - are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can then be evaluated with low-overhead post-processing, typically to chemical accuracy. We also show how the nuclear coordinates can be reliably determined from the pseudocharge field using a clustering based technique. Remarkably, only about 120 data points are found to be enough to predict the three dimensional electronic fields accurately, which we ascribe to the symmetry in the problem setup, the use of low-discrepancy sequences for sampling, and presence of intrinsic low-dimensional features in the electronic fields. We comment on the interpretability of our machine learning model and discuss its possible future applications. Electronic structure ; Twist ; Electronic density ; Statistical physics ; Computer science ; Materials science ; Topology (electrical circuits) ; Physics ; Density functional theory ; Mathematics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Graphene research and applications ; Advanced Chemical Physics Studies
Authors: Zhipeng Zhang | Liuqing Chen | Junyi Guo | Xianyin Duan | Bin Shan | Xianbao Duan
Source: Physical review. B./Physical review. B (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Strategy to consider element distribution when constructing training datasets for developing machine learning potentials of alloys based on a Monte-Carlo-like method Molecular dynamics simulations can explore the characteristics and evolution of microstructures in alloys outside of experiments, with reliability and accuracy guaranteed by the interatomic potentials employed. Machine learning potential (MLP) is widely used for its accuracy close to first-principles calculations. When developing an MLP, the construction of the training dataset is crucial, determining the accuracy and generalization of the MLP. In this work, a Monte-Carlo-like (MCL) strategy is proposed to construct training datasets for developing MLPs of alloys, which is characterized by the efficient consideration of element distributions in alloys. As an example, a training dataset for the equimolar NbTiZrHf alloy is constructed based on the MCL strategy, and the corresponding MLP is developed subsequently. By comparing with two traditional strategies, it is found that the training dataset constructed based on the MCL strategy has greater dispersion, and the corresponding MLP has better prediction performance. In addition, a hybrid molecular statics and Monte Carlo simulation with the MCL-based MLP is performed to optimize the element distribution of the equimolar NbTiZrHf alloy, and segregation and short-range ordered structures are observed in the final configuration, which is consistent with the experimental results reported in the literature. The MCL strategy proposed in this work can provide a fast solution for considering the element distribution when constructing training datasets for developing MLPs of alloys. Monte Carlo method ; Computer science ; Generalization ; Range (aeronautics) ; Construct (python library) ; Artificial intelligence ; Statics ; Distribution (mathematics) ; Work (physics) ; Machine learning ; Algorithm ; Materials science ; Mathematics ; Mechanical engineering ; Engineering ; Physics ; Statistics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alireza Geramizadegan | Dadkhoda Ghazanfari | Aliasghar Amiri
Source: International Journal of Environmental & Analytical Chemistry (article, 2022-03-16)
Field: Chemistry — Analytical Chemistry

Determination of amount herbicide toxic fenpyroximate in surface water by analysis molecularly imprinted solid phase extraction method and relative error assessment using artificial neural network model This work describes the development of an analytical methodology to determine toxic fenpyroximate in surface water using molecularly imprinted polymers (MIPs) as the extraction and pre-concentration material, followed by high performance liquid chromatography. A new molecularly imprinted polymer, prepared following a non-covalent approach, was synthesised using toxic fenpyroximate as the template molecule, methacrylic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross-linker. A method for the determination of traces of toxic fenpyroximate was developed using this polymer as the enrichment sorbent coupled with high performance liquid chromatography focusing on surface water samples. The calibration curve was linear in the range of 0.1 to 1.0 µg L−1. The standard deviation is 2.9%, and the detection limit of the method is 0.2 µg L−1 and r2 = 0.9869. The observed outcomes confirmed the suitability recovery and a very low detection limit for measuring toxic fenpyroximate. It was demonstrated that the proposed MI-SPE-HPLC method could be applied for direct determination of toxic fenpyroximate in surface water. The observed outcomes confirmed the suitability of the artificial neural network model as a tool for MSE (mean square error) of fenpyroximate on (MSEANN = 0.41, MSEFL = 0.36 and MSEANFIS = 0.23) selectivity and permeability. It was demonstrated that the proposed MI-SPE-HPLC method could be applied for direct determination of toxic fenpyroximate in surface water. Molecularly imprinted polymer ; Solid phase extraction ; Detection limit ; Methacrylic acid ; Chromatography ; Ethylene glycol dimethacrylate ; High-performance liquid chromatography ; Sorbent ; Extraction (chemistry) ; Polymer ; Chemistry ; Adsorption ; Materials science ; Selectivity ; Monomer ; Organic chemistry Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Electrochemical Analysis and Applications
Authors: Stefan Chmiela | Valentín Vassilev-Galindo | Oliver T. Unke | Adil Kabylda | Huziel E. Sauceda | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2022-09-29)
Field: Materials Science — Materials Chemistry

Accurate global machine learning force fields for molecules with hundreds of atoms Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the model complexity with system size. For larger molecules, locality assumptions are typically introduced, with the consequence that non-local interactions are poorly or not at all described, even if those interactions are contained within the reference ab initio data. Here, we approach this challenge and develop an exact iterative parameter-free approach to train global symmetric gradient domain machine learning (sGDML) force fields for systems with up to several hundred atoms, without resorting to any localization of atomic interactions or other potentially uncontrolled approximations. This means that all atomic degrees of freedom remain fully correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of our MLFFs on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond long path-integral molecular dynamics simulations for the supramolecular complexes in the MD22 dataset. Robustness (evolution) ; Molecular dynamics ; Computer science ; Atom (system on chip) ; Statistical physics ; Force field (fiction) ; Complex system ; Locality ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Kristian Hovde Liland | Roman Svoboda | Giorgio Luciano | Nikita V. Muravyev
Source: Journal of Non-Crystalline Solids (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Neural networks applied in kinetic analysis of complex nucleation-growth processes: Outstanding solution for fully overlapping reaction mechanisms Kinetic energy ; Nucleation ; Artificial neural network ; Computer science ; Deconvolution ; Convolutional neural network ; Multilayer perceptron ; Biological system ; Algorithm ; Artificial intelligence ; Thermodynamics ; Physics Thermal and Kinetic Analysis ; Injection Molding Process and Properties ; Machine Learning in Materials Science