jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natasha Dropka | Martin Holeňa
Source: Crystals (article, 2020-08-01)
Field: Materials Science — Materials Chemistry

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time. Artificial neural network ; Crystal (programming language) ; Computer science ; Process (computing) ; Artificial intelligence ; Key (lock) ; Quality (philosophy) ; Machine learning ; Biological system ; Physics Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Shuqian Ye | Jiechun Liang | Rulin Liu | Xi Zhu
Source: The Journal of Physical Chemistry A (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Symmetrical Graph Neural Network for Quantum Chemistry with Dual Real and Momenta Space Most of the current neural network models in quantum chemistry (QC) exclude the molecular symmetry and separate the well-correlated real space (<i>R</i> space) and momenta space (<i>K</i> space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehendible method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to orbital symmetry for both ground and excited states. SY-GNN is an end-to-end model that can predict multiple properties in both <i>K</i> and <i>R</i> space within a single model, and it shows excellent performance in predicting both the absolute and relative <i>R</i> and <i>K</i> space quantities. Besides the numerical properties, SY-GNN can also predict orbital properties, providing the active regions of chemical reactions. We believe the symmetry-endorsed deep learning scheme covers the significant physics inside and is essential for the application of neural networks in QC and many other research fields in the future. Artificial neural network ; Symmetry (geometry) ; Space (punctuation) ; Quantum chemistry ; Homogeneous space ; Graph ; Excited state ; Dual space ; Physics ; Theoretical physics ; Quantum ; Quantum mechanics ; Computer science ; Topology (electrical circuits) ; Statistical physics ; Chemistry ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Molecule ; Pure mathematics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Keisuke Takahashi | Lauren Takahashi | Thanh Nhat Nguyen | Ashutosh Thakur | Toshiaki Taniike
Source: The Journal of Physical Chemistry Letters (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data. Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C<sub>2</sub> selectivity, CH<sub>4</sub> conversion, and their composition in each calssified group. Thus, systematic design of catalysts can be achieved in principle on the basis of the unique features of catalysts uncovered via data science. Catalysis ; Oxidative coupling of methane ; Throughput ; Computer science ; Selectivity ; Visualization ; Biological system ; Chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yabo Dan | Rongzhi Dong | Zhuo Cao | Li Xiang | Chengcheng Niu | Shaobo Li | Jianjun Hu
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Computational Prediction of Critical Temperatures of Superconductors Based on Convolutional Gradient Boosting Decision Trees Superconductors have been one of the most intriguing materials since they were discovered more than a century ago. However, superconductors at room temperature have yet to be discovered. On the other hand, machine learning and especially deep learning has been increasingly used in material properties prediction and discovery in recent years. In this paper, we propose to combine the deep convolutional neural network (CNN) model with fully convolutional layers for feature extraction with gradient boosting decision tree (GBDT) for superconductors critical temperature (Tc) prediction. Our prediction model only uses the elemental property statistics of the materials as original input and learns a hierarchical representation of superconductors using convolutional layers. Computational experiments showed that our convolutional gradient boosting decision tree (ConvGBDT) model achieved the state-of-the-art results on three superconductor data sets: DataS, DataH, and DataK. By visually comparing the raw elemental feature distribution and the learned feature distribution, it is found that the convolutional layers of our ConvGBDT can learn features that can more effectively distinguish cuprate and iron-based superconductors. On the other hand, the GBDT part of our ConvGBDT model can learn the sophisticated mapping relationship between extracted features and the critical temperatures to obtain good prediction performance. Gradient boosting ; Boosting (machine learning) ; Computer science ; Decision tree ; Alternating decision tree ; Artificial intelligence ; Machine learning ; Decision tree learning ; Random forest Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Jinkwan Kwoen | Yasuhiko Arakawa
Source: Crystal Growth & Design (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

Classification of Reflection High-Energy Electron Diffraction Pattern Using Machine Learning Reflection high-energy electron diffraction (RHEED) has wide application because it allows in situ observation of the sample surface behavior during molecular beam epitaxy growth. In particular, the RHEED pattern has been used as a milestone for growth condition calibration because it dynamically changes depending on the sample temperature, material supply rate, and supply ratio. However, RHEED pattern analysis depends on the accumulated know-how of the operator and has a time limitation; thus, its application to real-time feedback control is difficult. Moreover, with the conventional computerization method, it is difficult to correctly reflect and recognize the changes in RHEED due to changes in the observation conditions. On the other hand, the machine learning method using the convolutional neural network (CNN) recognizes feature points in the input database and is suitable for the classification of images with variability. In this study, we propose a measurement method for identifying the RHEED pattern of GaAs substrates during continuous rotation and build a data set of the growth conditions. A classification model is established by training the deep learning model using CNN, and is found to be more than 99% accurate. This is expected to be useful in the field of highquality III–V growth on GaAs. Reflection high-energy electron diffraction ; Electron diffraction ; Reflection (computer programming) ; Sample (material) ; Computer science ; Diffraction ; Artificial neural network ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Optics ; Chemistry ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Zijiang Yang | Stefanos Papanikolaou | Andrew Reid | Wei‐keng Liao | Alok Choudhary | Carelyn E. Campbell | Ankit Agrawal
Source: Scientific Reports (article, 2020-05-19)
Field: Materials Science — Materials Chemistry

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles. Materials science ; Dislocation ; Plasticity ; Nanoscopic scale ; Crystal plasticity ; Work hardening ; Inverse ; Compression (physics) ; Residual ; Strain hardening exponent ; Deformation (meteorology) ; Hardening (computing) ; Statistical physics ; Computer science ; Composite material ; Algorithm ; Nanotechnology ; Geometry ; Microstructure ; Mathematics ; Physics Microstructure and mechanical properties ; Integrated Circuits and Semiconductor Failure Analysis ; Ion-surface interactions and analysis
Authors: P.E. Lokhande | Umesh Chavan
Source: Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (article, 2020-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cyclic voltammetry behavior modeling of fabricated nanostructured Ni(OH)<sub>2</sub> electrode using artificial neural network for supercapacitor application In this article, we demonstrate the fabrication of binder-free nanostructured Ni(OH) 2 electrode for supercapacitor by the chemical bath deposition method in addition to modeling of cyclic voltammetry using artificial neural network. The prepared material acted as binder-free electrode that facilitated increase in the capacitance value. The electrochemical study demonstrated the maximum specific capacitance of 958 F·g −1 . Further, cyclic behavior of the prepared material was investigated by modeling via artificial neural network, which is based on the multilayer perception concept. It is observed that results obtained from artificial neural network analysis is analogous to the experimental value with an error rate of 0.14%. Supercapacitor ; Cyclic voltammetry ; Capacitance ; Materials science ; Electrode ; Artificial neural network ; Electrochemistry ; Horizontal scan rate ; Fabrication ; Chemical engineering ; Nanotechnology ; Computer science ; Artificial intelligence ; Chemistry ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Neuroscience and Neural Engineering
Authors: Mukil V. Ayyasamy | Jeroen A. Deijkers | H.N.G. Wadley | Prasanna V. Balachandran
Source: Journal of the American Ceramic Society (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Density functional theory and machine learning guided search for RE <sub>2</sub> Si <sub>2</sub> O <sub>7</sub> with targeted coefficient of thermal expansion Abstract Density functional theory (DFT) calculations and machine learning (ML) methods are used to establish a relationship between the crystal structures of rare‐earth (RE) disilicates (RE 2 Si 2 O 7 ) and their coefficient of thermal expansion (CTE). The DFT total energy data predict the presence of several energetically competing crystal structures, which is rationalized as one of the reasons for observing polymorphism. An ensemble of support vector regression models is trained to rapidly predict the CTE as a function of RE 2 Si 2 O 7 crystal chemistry. Experiments subsequently validated the structure and CTE predictions for Sm 2 Si 2 O 7 . Thermal expansion ; Density functional theory ; Crystal structure ; Crystal (programming language) ; Materials science ; Support vector machine ; Thermodynamics ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Crystallography ; Machine learning Nuclear materials and radiation effects ; Crystal Structures and Properties ; Advanced Condensed Matter Physics
Authors: Dongbo Zhao | Xiaoling Shen | Zheng Cheng | Wei Li | Hao Dong | Shuhua Li
Source: Journal of Chemical Theory and Computation (article, 2020-04-17)
Field: Chemistry — Spectroscopy

Accurate and Efficient Prediction of NMR Parameters of Condensed-Phase Systems with the Generalized Energy-Based Fragmentation Method We have implemented the calculations of NMR parameters within the generalized energy-based fragmentation (GEBF) method for condensed-phase systems with periodic boundary conditions (PBC). In this PBC-GEBF approach, NMR parameters of molecules in a unit cell are assembled as a linear combination of the corresponding quantities from a series of small embedded subsystems. To treat condensed-phase systems containing large molecules, we propose a novel "fragment-based" strategy for building subsystems, while our previously reported "molecule-based" strategy for construction of subsystems is appropriate for periodic systems with small molecules. The "fragment-based" strategy in PBC-GEBF is demonstrated to be much more efficient than its "molecule-based" counterpart to treat crystals of large molecules. With the "molecule-based" PBC-GEBF method, we obtained consistently good NMR parameters of liquid water with B3LYP on top of neural-network-potential-based <i>ab initio</i> molecular dynamics (AIMD) snapshots. With the "fragment-based" PBC-GEBF approach, we predicted the <sup>1</sup>H chemical shifts of a large macrocycle in solution based on a series of classical MD snapshots. The calculated results are in good accord with the experimental chemical shifts. Therefore, the PBC-GEBF method is expected to be a reliable and efficient tool for predicting NMR parameters of large complex systems in solutions. Fragmentation (computing) ; Computer science ; Energy (signal processing) ; Phase (matter) ; Biological system ; Statistical physics ; Chemical physics ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Quantum mechanics Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Solid-state spectroscopy and crystallography
Authors: Nattane Luíza da Costa | Maxwell Severo da Costa | Rommel Barbosa
Source: Food Analytical Methods (review, 2020-09-26)
Field: Chemistry — Analytical Chemistry

A Review on the Application of Chemometrics and Machine Learning Algorithms to Evaluate Beer Authentication Machine learning ; Chemometrics ; Computer science ; Artificial intelligence ; Algorithm ; Feature selection Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Wonseok Jeong | Dongsun Yoo | Kyuhyun Lee | Jisu Jung | Seungwu Han
Source: The Journal of Physical Chemistry Letters (article, 2020-06-29)
Field: Materials Science — Materials Chemistry

Efficient Atomic-Resolution Uncertainty Estimation for Neural Network Potentials Using a Replica Ensemble Neural network potentials (NNPs) are gaining much attention as they enable fast molecular dynamics (MD) simulations for a wide range of systems while maintaining the accuracy of density functional theory calculations. Since NNP is constructed by machine learning on training data, its prediction uncertainty increases drastically as atomic environments deviate from training points. Therefore, it is essential to monitor the uncertainty level during MD simulations to judge the soundness of the results. In this work, we propose an uncertainty estimator based on the replica ensemble in which NNPs are trained over atomic energies of a reference NNP that drives MD simulations. The replica ensemble is trained quickly, and its standard deviation provides atomic-resolution uncertainties. We apply this method to a highly reactive silicidation process of Si(001) overlaid with Ni thin films and confirm that the replica ensemble can spatially and temporally trace simulation errors at atomic resolution, which in turn guides the augmentation of the training set. The refined NNP completes a 3.6 ns simulation without any noticeable problems. By suggesting an efficient and atomic-resolution uncertainty indicator, this work will contribute to achieving reliable MD simulations by NNPs. Replica ; Estimator ; Computer science ; Artificial neural network ; Range (aeronautics) ; Uncertainty quantification ; Resolution (logic) ; Algorithm ; Molecular dynamics ; Machine learning ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Statistics ; Chemistry ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Joshua Schrier
Source: Journal of Chemical Information and Modeling (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)? Coulomb matrix eigenvalues (CMEs) are global 3D representations of molecular structure, which have been previously used to predict atomization energies, prioritize geometry searches, and interpret rotational spectra. The properties of the CME representation and its relationship to molecular structure are established using the Gershgorin circle theorem. Numerical bounds are studied using a data set of 309 000 conformational samples of all constitutional isomers of acyclic alkanes, C<sub><i>n</i></sub>H<sub>2<i>n</i>+2</sub>, from methane (<i>n</i> = 1) to undecane (<i>n</i> = 11), to establish the extent to which the CME preserves chemical intuitions about isomer and conformer similarity and its ability to distinguish constitutional isomers. Neither supervised nor unsupervised machine-learning algorithms can perfectly distinguish constitutional isomers as the molecular size increases, but the misclassification rate can be kept below 1%. Eigenvalues and eigenvectors ; Conformational isomerism ; Matrix (chemical analysis) ; Molecule ; Representation (politics) ; Coulomb ; Similarity (geometry) ; Matrix representation ; Set (abstract data type) ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Atreyi Dasmahapatra | Loredana Edith Daga | Antti J. Karttunen | Lorenzo Maschio | Silvia Casassa
Source: The Journal of Physical Chemistry C (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Key Role of Defects in Thermoelectric Performance of TiMSn (M = Ni, Pd, and Pt) Half-Heusler Alloys Half-Heusler alloys are thermoelectric materials that enable direct conversion of waste heat to electricity. A systematic study of these alloys has never been attempted using local Gaussian type orbitals (GTOs) and hybrid density functional theory methods within a periodic approach. In this work, we study the thermoelectric properties of TiMSn (M = Ni, Pd, and Pt) alloys with space group F4̅3m using the CRYSTAL code. We, first, set benchmarks for TiNiSn by comparing our data to existing literature values of Seebeck coefficient, power-factor, and thermoelectric figure-of-merit. Our results agree well. We, then, extend these calculations to TiPdSn and TiPtSn, for which consistent previous data are limited. Our computations show that all TiMSn (M = Ni, Pd, and Pt) alloys prefer p-type carriers and exhibit a figure-of-merit of ≈1 at a chosen carrier concentration and temperature. In addition, we aim to explain the low band-gap of TiNiSn by modeling defects in the pure system. Our defect model proves to have a smaller band-gap, and its power-factor is found to be almost twice of the pure TiNiSn. Thermoelectric effect ; Figure of merit ; Materials science ; Thermoelectric materials ; Seebeck coefficient ; Band gap ; Condensed matter physics ; Electronic band structure ; Optoelectronics ; Engineering physics ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; 2D Materials and Applications
Authors: Mikhail Y. Shalaginov | Sensong An | Yifei Zhang | Fan Yang | Peter Su | Vladimir Liberman | Jeffrey B. Chou | Christopher Roberts | Myungkoo Kang | Carlos Rı́os | Qingyang Du | Clayton Fowler | Anu Agarwal | Kathleen Richardson | Clara Rivero‐Baleine | Hualiang Zhang | Juejun Hu | Tian Gu
Source: Research Square (preprint, 2020-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable all-dielectric metalens with diffraction-limited performance Diffraction ; Dielectric ; Optics ; Materials science ; Optoelectronics ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Stephen Lam | Qing‐Jie Li | Jonathan P. Mailoa | Charles Forsberg | R. G. Ballinger | Ju Li
Source: Journal of Materials Chemistry A (article, 2020-12-21)
Field: Chemistry — Inorganic Chemistry

The impact of hydrogen valence on its bonding and transport in molten fluoride salts In molten fluoride salt systems, the chemistry and transport of hydrogen are coupled to its valence state, which controls the balance of tritium leakage and corrosion. Valence (chemistry) ; Hydrogen ; Fluoride ; Molten salt ; Chemistry ; Inorganic chemistry ; Corrosion ; Materials science ; Metallurgy ; Organic chemistry Inorganic Fluorides and Related Compounds ; Molten salt chemistry and electrochemical processes ; Inorganic Chemistry and Materials