jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengru Huang | Dan Cai | Huaizhou Lin | Jiaxi Liu | Ziyuan Li | Bin Li | Yongjin Zou | Hailiang Chu | Lixian Sun | Fen Xu
Source: Scientia Sinica Chimica (article, 2022-09-07)
Field: Materials Science — Materials Chemistry

Materials genome engineering-based hydrogen storage materialsdatabase and its applications <p indent="0mm">The accelerated research and development of high-density hydrogen-storage materials are critical to the transition of the energy economy and the realization of the carbon peaking and carbon neutrality goals. The data-driven research paradigm-integrated high-throughput calculations, database, and machine learning is appealing to accelerate new material development. Due to their complex compositions, structures and morphologies, the data-driven research on hydrogen-storage materials is still limited, and a database on the comprehensive properties is necessary to be established. Herein, we show our results on establishing a Materials Genome Initiative database and the property prediction based on machine learning for hydrogen-storage materials. The datasets are constructed from published papers, Materials Genome Initiative databases, and high-throughput first-principle calculations. A hydrogen-storage materials database was established and hydrogen property predictions are carried out by using machine-learning methods. We believe that the accelerated development of new hydrogen-storage materials would be benefited from our database and platforms. Hydrogen storage ; Computer science ; Genome ; Computational biology ; Biology ; Hydrogen ; Chemistry ; Genetics ; Gene Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; X-ray Diffraction in Crystallography
Authors: I. Aruna Devi | R. V. Maheswari | R. Rajesh
Source: IETE Journal of Research (article, 2022-03-06)
Field: Materials Science — Materials Chemistry

Recognition of Fused Partial Discharge Patterns in High Voltage Insulation Systems: A Hybrid DCNN and SVM Based Approach The performance assessment of insulation materials within the components of a high voltage power system depends upon effective condition monitoring techniques of the insulation. One of the important phenomena to be considered in the diagnostics and performance assessment of high voltage insulation materials is the Partial Discharge (PD) measurement. The improved Deep learning techniques help in the effective identification and classification of various sources of PD mechanism. In this work, Deep Convolution Neural Network (DCNN) technique is proposed for the fusion of several features that are extracted from the PD signals. Three-dimensional Phase Resolved patterns of PD images can be used for training the DNN. For the classification of PD patterns, linear and non-linear type of multi-class support vector machine has been proposed. The results obtained are compared with linear SVM and non-linear SVMs with the polynomial kernel, Radial Basis Function (RBF) kernel, and Sigmoidal kernel. The SVM type with a higher pattern recognition rate is identified to be effective in PD pattern recognition and classification. The results of the proposed work show that the fusion approach of PD patterns supports applying huge PD data sets as input, generated by multiple faults, for effective PD pattern recognition. Pattern recognition (psychology) ; Support vector machine ; Partial discharge ; Artificial intelligence ; Kernel (algebra) ; Radial basis function kernel ; Computer science ; Radial basis function ; Artificial neural network ; Convolution (computer science) ; Polynomial kernel ; Convolutional neural network ; Sigmoid function ; Voltage ; Kernel method ; Engineering ; Mathematics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Image and Signal Denoising Methods
Authors: Ayako TAMURA | Gerardo Valadez Huerta | Yūsuke Nanba | Kaoru Hisama | Michihisa KOYAMA
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

CO Adsorption on Ternary Nanoalloys by Universal Neural Network Potential Multi-element alloy nanoparticles have attracted attention for their potentially high catalytic properties. However, a high degree of freedom in configurations of metal atoms within nanoparticle increases the distinct adsorption sites, making it difficult to theoretically analyze its catalytic properties because the first-principles calculation requires a considerable computational cost. In this study, we develop a sequential scheme to calculate hundreds of adsorption sites by employing a pre-trained universal neural network potential named PFP. Our automated scheme is applied to CO single-molecule adsorption of CO onto PtRuIr ternary alloy nanoparticles. The calculation results are first compared with DFT results to confirm the accuracy. Adsorption energies in the alloy systems are widely distributed in comparison with those of the monometal counterparts, indicating that the alloy nanoparticle includes adsorption sites with various catalytic activities. Ternary operation ; Adsorption ; Alloy ; Nanoparticle ; Catalysis ; Artificial neural network ; Materials science ; Metal ; Molecule ; Computer science ; Nanotechnology ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry ; Artificial intelligence Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: K. Zongo | Laurent Karim Béland | Claudiane Ouellet‐Plamondon
Source: MRS Advances (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

First-principles database for fitting a machine-learning silicon interatomic force field Interatomic potential ; Density functional theory ; Materials science ; Field (mathematics) ; Focus (optics) ; Silicon ; Tensor (intrinsic definition) ; Computer science ; Experimental data ; Force field (fiction) ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Giuseppe Ciccone | Matteo Cucchi | Yanfei Gao | Ankush Kumar | Lennart Maximilian Seifert | Anton Weissbach | Hsin Tseng | Hans Kleemann | Fabien Alibart | Karl Leo
Source: Discover Materials (article, 2022-07-22)
Field: Materials Science — Polymers and Plastics

Growth and design strategies of organic dendritic networks Abstract A new paradigm of electronic devices with bio-inspired features is aiming to mimic the brain’s fundamental mechanisms to achieve recognition of very complex patterns and more efficient computational tasks. Networks of electropolymerized dendritic fibers are attracting much interest because of their ability to achieve advanced learning capabilities, form neural networks, and emulate synaptic and plastic processes typical of human neurons. Despite their potential for brain-inspired computation, the roles of the single parameters associated with the growth of the fiber are still unclear, and the intrinsic randomness governing the growth of the dendrites prevents the development of devices with stable and reproducible properties. In this manuscript, we provide a systematic study on the physical parameters influencing the growth, defining cause-effect relationships for direction, symmetry, thickness, and branching of the fibers. We build an electrochemical model of the phenomenon and we validate it in silico using Montecarlo simulations. This work shows the possibility of designing dendritic polymer fibers with controllable physical properties, providing a tool to engineer polymeric networks with desired neuromorphic features. Computer science ; Neuromorphic engineering ; Randomness ; Computation ; Artificial neural network ; Branching (polymer chemistry) ; Artificial intelligence ; Distributed computing ; Nanotechnology ; Materials science ; Biological system ; Biology ; Mathematics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Alexander Ryabov | Iskander Akhatov | Petr Zhilyaev
Source: Scientific Reports (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Application of two-component neural network for exchange-correlation functional interpolation Density functional theory (DFT) is one of the primary approaches to solving the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Instead, various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. We develop NN XC functional, which gives exchange potential and energy density without direct derivatives of exchange-correlation energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, adding new flexibility to XC functional. We also show that the developed NN XC functional converges in the self-consistent cycle and gives reasonable energies when applied to atoms, molecules, and crystals. Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Energy functional ; Interpolation (computer graphics) ; Functional theory ; Statistical physics ; Artificial neural network ; Physics ; Algorithm ; Quantum mechanics ; Computer science ; Artificial intelligence ; Classical mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications
Authors: Zijian Wang | Qingzhu Zhang | Guoqiang Wang | Wenxing Wang | Qiao Wang
Source: International Journal of Molecular Sciences (article, 2022-12-27)
Field: Chemistry — Organic Chemistry

Hydrolysis Mechanism of Carbamate Methomyl by a Novel Esterase PestE: A QM/MM Approach Methomyl is one of the most important carbamates that has caused potential hazardous effects on both human beings and the environment. Here, we systematically investigated the hydrolysis mechanism of methomyl catalyzed by esterase PestE using molecular dynamics simulations (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. The hydrolysis mechanism involves two elementary steps: (Ⅰ) serine-initiated nucleophilic attack and (Ⅱ) C-O bond cleavage. Our work elicits the atomic level details of the hydrolysis mechanism and free energy profiles along the reaction pathway. The Boltzmann-weighted average potential barriers are 19.1 kcal/mol and 7.5 kcal/mol for steps Ⅰ and Ⅱ, respectively. We identified serine-initiated nucleophilic attack as the rate determining-step. The deep learning-based <i>k</i><sub>cat</sub> prediction model indicated that the barrier of the rate-determining step is 15.4 kcal/mol, which is in good agreement with the calculated results using Boltzmann-weighted average method. We have elucidated the importance of the protein-substrate interactions and the roles of the key active site residues during the hydrolysis process through noncovalent interactions analysis and electrostatic potential (ESP) analysis. The results provide practical value for achieving efficient degradation of carbamates by hydrolases. Methomyl ; Carbamate ; Esterase ; Hydrolysis ; Chemistry ; QM/MM ; Organic chemistry ; Biochemistry ; Enzyme ; Catalysis ; Biology ; Pesticide Chemical Reaction Mechanisms ; Pesticide Residue Analysis and Safety ; Pesticide and Herbicide Environmental Studies
Authors: Fulun Wu | Feng Zheng | Wei-bo He | Xinrui Cao | Tie‐Yu Lü | Zi‐Zhong Zhu | Shunqing Wu
Source: Physical Review Materials (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Deep learning interatomic potential for Ca-O system at high pressure Calcium-containing oxides are fundamental components of the Earth's crust and mantle. Analysis of their structural behavior contributes to our understanding of the Earth's interior, which needs a reliable interatomic potential. Here, we present an interatomic potential for the Ca-O system using the deep neural network method. The initial training data set for the deep interatomic potential (DP) consists of snapshots of prototype binary structures from materials projects and crystal-derived structures based on Ca-O binary compounds, as well as their molecular dynamic trajectories. The accuracy of the DP is evaluated by predicting forces and energies in comparison with those from ab initio calculations. We demonstrate that the vibrational and thermodynamic properties based on DP calculations are in excellent agreement with those from ab initio calculations. Besides, we also construct a temperature-pressure phase diagram of Ca-O compounds with DP at a lower cost compared to ab initio methods. Finally, we use the DP to explore Ca-O structures by combining it with the genetic algorithm, the accuracy of which is validated by a principal component analysis for the local atomic environments. The DP training procedure used in this work is equally applicable to other systems for accurate atomistic simulations as an effective method. Interatomic potential ; Ab initio ; Materials science ; Phase diagram ; Binary number ; Molecular dynamics ; Crystal structure prediction ; Ab initio quantum chemistry methods ; Crystal structure ; Thermodynamics ; Phase (matter) ; Crystallography ; Computational chemistry ; Physics ; Chemistry ; Molecule Machine Learning in Materials Science ; High-pressure geophysics and materials ; X-ray Diffraction in Crystallography
Authors: Xiaonan Lu | Lu Deng | Stéṕhane Gin | Benjamin Parruzot | Joelle T. Reiser | Joseph V. Ryan | John D. Vienna | Jincheng Du
Source: Journal of the American Ceramic Society (article, 2022-10-17)
Field: Materials Science — Ceramics and Composites

Predicting initial dissolution rates using structural features from molecular dynamics simulations Abstract Predicting the chemical durability of glass materials is important for various applications from daily life such as cell phone screens and kitchenware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored the prediction of the initial glass dissolution rates using structural features from molecular dynamics (MD) simulations for a series of glass compositions (total 28), including ZrO 2 ‐ and V 2 O 5 ‐containing boroaluminosilicate, borosilicate, and aluminosilicate glasses. The initial dissolution rates ( r 0 ) measured experimentally at 90°C with varying solution conditions were correlated with structural features (e.g., polyhedral linkages and non‐bridging oxygen species) obtained from MD simulations, either from this study or from literature. As hydrolysis of the glass network through breaking of the network former linkages (e.g., Si–O–Si, Si–O–Al, etc.) is a critical step of network glass dissolution, the statistics of these linkages obtained from MD were also correlated to r 0 through linear regression, where the coefficients of determination ( R 2 ) and root mean square error are found to be 0.949 and 0.681, respectively. This model was compared and discussed with existing models developed by various approaches, including machine learning, the kinetic rate equation, topological constraint theory, and other descriptors from MD simulations. The discussion provides insights on future model improvements to predict glass dissolution. In addition, as the effect of V 2 O 5 on glass dissolution was not well studied comparing to ZrO 2 , the impact of V 2 O 5 was emphasized in this paper, suggesting that the impact is not the same across all glass compositions and test conditions. Dissolution ; Borosilicate glass ; Molecular dynamics ; Materials science ; Aluminosilicate ; Glass transition ; Mineralogy ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Composite material ; Computational chemistry Glass properties and applications ; Pigment Synthesis and Properties ; Luminescence Properties of Advanced Materials
Authors: Bernard R. Becker | Eric Hintsala | Benjamin Stadnick | Ude Hangen | Douglas Stauffer
Source: Journal of Applied Physics (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Automated analysis method for high throughput nanoindentation data with quantitative uncertainty High throughput nanoindentation techniques can provide rapid materials screening and property mapping and can span millimeter length scales and up to 106 data points. To facilitate rapid sorting of these data into similar groups, a necessary task for establishing structure–property relationships, use of an unsupervised machine learning analysis called clustering has grown in popularity. Here, a method is proposed and tested that evaluates the uncertainty associated with various clustering algorithms for an example high entropy alloy data set and explores the effect of the number of data points in a second Damascus steel data set. The proposed method utilizes the bootstrapping method of Efron to resample a modeled probability distribution function based upon the original data, which allows the uncertainty related to the clustering to be evaluated in contrast to the classical standard error on the mean calculations. For the Damascus, it was found that results data from a 104 point subsample are comparable to those from the full 106 set while representing a significant reduction in data acquisition. Cluster analysis ; Computer science ; Data set ; Data mining ; Bootstrapping (finance) ; Sorting ; Data point ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Armin Sahinovic | Benjamin Geisler
Source: Journal of Physics Condensed Matter (article, 2022-03-02)
Field: Materials Science — Materials Chemistry

Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides We combine density functional theory simulations and active learning (AL) of element-embedding neural networks (NNs) to explore the sample efficiency for the prediction of vacancy layer formation energies and lattice parameters in<i>ABX</i><sub><i>n</i></sub>infinite-layer (<i>n</i>= 2) versus perovskite (<i>n</i>= 3) nitrides, oxides, and fluorides in the spirit of transfer learning. Following a comprehensive data analysis from different thermodynamic, structural, and statistical perspectives, we show that NNs model these observables with high precision, using merely∼30%of the data for training and exclusively the<i>A</i>-,<i>B</i>-, and<i>X</i>-site element names as minimal input devoid of any physical<i>a priori</i>information. Element embedding autonomously arranges the chemical elements with a characteristic recurrent topology, such that their relations are consistent with human knowledge. We compare two different embedding strategies and show that these techniques render additional input such as atomic properties negligible. Simultaneously, we demonstrate that AL is largely independent of the initial training set, and exemplify its superiority over randomly composed training sets. Despite their highly distinct chemistry, the present approach successfully identifies fundamental quantum-mechanical universalities between nitrides, oxides, and fluorides that enhance the combined prediction accuracy by up to 16% with respect to three specialized NNs at equivalent numerical effort. This quantification of synergistic effects provides an impression of the transfer learning improvements one may expect for similarly complex materials. Finally, by embedding the tensor product of the<i>B</i>and<i>X</i>sites and subsequent quantitative cluster analysis, we establish from an unbiased artificial-intelligence perspective that oxides and nitrides exhibit significant parallels, whereas fluorides constitute a rather distinct materials class. Embedding ; Transfer of learning ; Observable ; Artificial neural network ; Vacancy defect ; Perovskite (structure) ; Density functional theory ; Tensor product ; Element (criminal law) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Ahmer A.B. Baloch | Omar Albadwawi | Badreyya AlShehhi | Vivian Alberts
Source: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

Bandgap model using symbolic regression for environmentally compatible lead-free inorganic double perovskites Data-driven models have become an essential practice of scientific research in the perovskite field, along with theory and experiments. Material informatics has emerged as a viable alternative method of exploring and formulating novel perovskite compounds using a descriptor-based approach. Herein, we develop a method that includes feature augmentation with symbolic regression to rapidly estimate and screen non-toxic lead-free inorganic double perovskites (A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> BB'X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> ) using machine learning. Predictive models were created by identifying a physico-chemical relevant descriptor from an extensive pool of augmented features. Using primary atomic and molecular features, a high dimensional space of descriptors <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{containing}\approx 3\times 10^{5}$</tex> features) was reconstructed using mathematical operators. By increasing the complexity from 1-D to 5-D descriptor, the correlation coefficient was increased from 81.6% to 92.4%. These accurate and interpretable models can then be employed for screening lead-free perovskites with appropriate bandgaps and stability. Artificial intelligence ; Perovskite (structure) ; Computer science ; Regression ; Stability (learning theory) ; Machine learning ; Linear regression ; Algorithm ; Mathematics ; Chemistry ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: Kristopher Nolte | Yunyun Gao | Sabrina Stäb | Philip Kollmannsberger | Andrea Thorn
Source: Acta Crystallographica Section D Structural Biology (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Detecting ice artefacts in processed macromolecular diffraction data with machine learning Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software. Convolutional neural network ; Diffraction ; Set (abstract data type) ; Data set ; Artificial neural network ; Training set Enzyme Structure and Function ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications