jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: 戚云超 | 方国东 | Jun Liang | 谢军波
Source: SHILAP Revista de lepidopterología (article, 2020-01-09)
Field: Materials Science — Ceramics and Composites

三维针刺C/C-SiC复合材料预制体工艺参数优化 A surrogate model was established to optimize needling process parameters of three dimensional needled C/C-SiC composites by using back propagation (BP) neural network and improved genetic algorithm. The relationship between needling process parameters and composites stiffness was obtained. The stiffness prediction obtained by BP neural network is in good agreement with the finite element calculated results. The maximum error of training data is 0.526%, and the maximum error of test data is 0.454%. Thus, the BP neural network model exhibits the high prediction accuracy. The genetic and optimization strategies of genetic algorithm were improved to optimize the needling process parameters. The calculated needling process parameters by the model can significantly improve the stiffness of the C/C-SiC composites. The in-plane tensile modulus increase by 11.07% and 11.48%, and the out-of-plane tensile modulus increase by 49.64% and 48.13%, respectively. The comprehensive stiffness performance of composite material increase by 18.17% and 18.21%, respectively. Business Advanced ceramic materials synthesis ; Material Selection and Properties ; Advanced machining processes and optimization
Authors: M.J. Keiser | S.D. Axen | B.L. Roth | L. Gendelev | Xinchen Huang | E.L. Cáceres
Source: UNC Libraries (article, 2020-08-07)
Field: Chemistry — Physical and Theoretical Chemistry

A Simple Representation of Three-Dimensional Molecular Structure Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the extended connectivity fingerprint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the extended three-dimensional fingerprint (E3FP). By integrating E3FP with the similarity ensemble approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442-0.637 kcal/mol/heavy atom. © 2017 American Chemical Society. Simple (philosophy) ; Representation (politics) ; Computer science ; Mathematics ; Biological system ; Biology ; Philosophy Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Kyoungmin Min | Eunseog Cho
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Interatomic Potential for Predicting the Formation of Planar Defect in Nanocrystal Recent advances in the development of interatomic potential using neural networks have proven that its accuracy reaches that of first-principles calculations but with considerably reduced computational cost. In this study, we successfully implement a neural network to construct the interatomic potential of the ZnSe structure by training its potential energy surface results obtained from density functional theory (DFT) calculations. The developed potential is used for molecular dynamics simulations and its accuracy lies within an error of 6% on average from the DFT results for predicting the total energy on pristine and defective bulk structures, slab, and cluster structures of ZnSe. The prediction accuracy is also demonstrated considering the lattice constant and mechanical properties of the pristine bulk structure. To demonstrate its transferability further, a neural network potential is constructed to predict the formation energy of planar defects (stacking fault and twin boundary) in the slab and the nanocrystal structures, and it precisely reproduces the order of stability for each defect type. These results reveal that the neural-network-based interatomic potential can be used to revolutionize atomistic simulations by significantly saving the computation time while maintaining accuracy comparable to that of the DFT. Interatomic potential ; Molecular dynamics ; Chemistry ; Slab ; Artificial neural network ; Transferability ; Density functional theory ; Stacking fault ; Nanocrystal ; Planar ; Stacking ; Potential energy ; Lattice (music) ; Statistical physics ; Cluster (spacecraft) ; Chemical physics ; Computational chemistry ; Nanotechnology ; Materials science ; Computer science ; Atomic physics ; Physics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices
Authors: Masashi Ishii | Fumihiko Uesugi | Tetsuya Ozawa
Source: Nihon Kessho Gakkaishi (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Objective and Rapid Crystal Structure Analysis by Integration of Measurement Informatics and Database Along with the advancement of measurement technologies, a large amount of experimental crystallographic data can be obtained easily, so that objective and rapid analysis suitable for batch process is strongly required. This article introduces recent research on objective and rapid analysis of crystal structure using X-rays or electron beams. For one-dimensional X-ray diffraction pattern, non-negative linear regression （NNLS） using diffraction patterns provided by public database has achieved more objective and faster analysis than conventional manual works. With regard to two-dimensional electron and X-ray diffraction, good objective and rapid analysis was realized for several types of materials by using artificial intelligence （AI） based on convolutional neural network. On the other hand, the sparsity inherent in the diffraction pattern was found to cause a lack of information, and universal applicability of material property prediction using AI that depends on training data is still open to question. However, the fact that AI itself has the potential ability to solve complex images, a breakthrough technology for universal two-dimensional objective and rapid analysis might be found in progressing research. Diffraction ; Computer science ; Convolutional neural network ; Crystal (programming language) ; Property (philosophy) ; Process (computing) ; Data mining ; Artificial intelligence ; Pattern recognition (psychology) ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Younes Khoshnamvand | Mehdi Assareh | Behrooz Molladavoodi
Source: Nashrieh Shimi va Mohandesi Shimi Iran (article, 2020-01-21)
Field: Chemistry — Analytical Chemistry

Effective Estimation of Paraffinic, Naphthenic, and Aromatic Families in Petroleum Fractions Having information on the chemical composition of hydrocarbon mixtures (the amount of paraffinic, naphthenic, and aromatics) is still a challenging problem in the area of reservoir fluids. Methods for determining these compositions include experimental techniques and empirical models. Laboratory methods are accurate, but they are costly and time-consuming. Therefore, researchers have tried to use empirical models instead of using laboratory methods. Generally, these models include two or more characteristic parameters such as Refractive Index (RI), normal boiling point (Tb), density in standard conditions (d), carbon to hydrogen ratio (CH), Watson coefficient (K), and viscosity - gravity constant(VGC). The problem with the proposed models is that for finding the compositions the mentioned characteristic parameters must be available, but usually, these parameters are not specified for petroleum cuts, therefore, in practice, the determination of chemical composition is difficult. In this research, we have tried to select parameters for the construction of a model that does not have this limitation. Selected parameters were MW, SG, and Tb which are molecular weight, specific gravity, and normal boiling point respectively. These parameters are commonly available for petroleum cuts, therefore for the determination of the composition of oil cuts, another specific characteristic parameter is not necessary. In this study, the neural network and the SAFT family equation of state have been used to determine the model for estimating the composition of families. For the development of this model, for synthetic cuts consisting of paraffinic, naphthenic, and aromatic, the specific gravity and normal boiling point values were determined using the PC-SAFT state equation. Finally, a neural network model was implemented on these data. In the end, the ability of the model for estimation has been tested using a series of evaluation data for oil cuts. The results indicate that the proposed model predicts the families present in the cuts effectively. Specific gravity ; API gravity ; Boiling point ; Pour point ; Petroleum ; Viscosity ; Boiling ; Thermodynamics ; Composition (language) ; Petroleum engineering ; Chemistry ; Biological system ; Mineralogy ; Organic chemistry ; Geology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Xiaoli Liu | Yang Xu | Jiali Li | Xuanwei Ong | Salwa Ali Ibrahim | Tonio Buonassisi | Xiaonan Wang
Source: arXiv (Cornell University) (preprint, 2020-10-27)
Field: Materials Science — Materials Chemistry

A robust low data solution: dimension prediction of semiconductor nanorods Precise control over dimension of nanocrystals is critical to tune the properties for various applications. However, the traditional control through experimental optimization is slow, tedious and time consuming. Herein a robust deep neural network-based regression algorithm has been developed for precise prediction of length, width, and aspect ratios of semiconductor nanorods (NRs). Given there is limited experimental data available (28 samples), a Synthetic Minority Oversampling Technique for regression (SMOTE-REG) has been employed for the first time for data generation. Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution. The prediction model is further validated with additional experimental data, showing accurate prediction results. Additionally, Local Interpretable Model-Agnostic Explanations (LIME) is used to interpret the weight for each variable, which corresponds to its importance towards the target dimension, which is approved to be well correlated well with experimental observations. Dimension (graph theory) ; Artificial neural network ; Oversampling ; Computer science ; Nanorod ; Regression ; Robust regression ; Regression analysis ; Artificial intelligence ; Algorithm ; Data mining ; Machine learning ; Mathematics ; Materials science ; Statistics ; Nanotechnology Machine Learning in Materials Science ; Machine Learning and ELM ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Ruimin Ma | Yamil J. Colón | Tengfei Luo
Source: ChemRxiv (preprint, 2020-04-15)
Field: Chemistry — Inorganic Chemistry

A Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nano-porous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce a transfer learning technique to improve the accuracy and applicability of ML models trained with small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural network (DNN) trained on H2 adsorption data with 13,506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H2 adsorption at 100 bar and 130 K (one target task), the predictive accuracy on target task was improved from 0.960 (direct training) to 0.991 (transfer learning). We also tested transfer learning across different gas species (i.e. from H2 to CH4), with predictive accuracy of CH4 adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning). Based on further analysis, transfer learning will always work on the target tasks with low generalizability. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy, which is attributed to the lack of common descriptors that is key to the underlying knowledge. Generalizability theory ; Transfer of learning ; Adsorption ; Task (project management) ; Computer science ; Artificial neural network ; Metal-organic framework ; Multi-task learning ; Limiting ; Artificial intelligence ; Machine learning ; Bar (unit) ; Knowledge transfer ; Transfer (computing) ; Chemistry ; Knowledge management ; Engineering ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Pál D. Mezei | O. Anatole von Lilienfeld
Source: Figshare (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

Noncovalent Quantum Machine Learning Corrections to\nDensity Functionals We\npresent noncovalent quantum machine learning corrections to\nsix physically motivated density functionals with systematic errors. We demonstrate\nthat the missing massively nonlocal and nonadditive physical effects\ncan be recovered by quantum machine learning models. The models seamlessly\naccount for various types of noncovalent interactions and enable accurate\npredictions of dissociation curves. The correction improves the description\nof molecular two- and three-body interactions crucial in large water\nclusters and provides a reasonable atomic-resolution picture about\nthe interaction energy errors of approximate density functionals that\ncan be useful information in the development of more accurate density\nfunctionals. We show that given sufficient training instances the\ncorrection is more flexible than standard molecular mechanical dispersion\ncorrections, and thus it can be applied for cases where many dispersion\ncorrected density functionals fail, such as hydrogen bonding. Quantum ; Non-covalent interactions ; Dissociation (chemistry) ; Statistical physics ; Density functional theory ; Dispersion (optics) ; Physics ; Resolution (logic) ; Computer science ; Quantum mechanics ; Artificial intelligence ; Hydrogen bond ; Molecule ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Alessandro Lunghi | Stefano Sanvito
Source: Figshare (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

The Limit of Spin Lifetime in Solid-State Electronic\nSpins The development of\nspin qubits for quantum technologies requires\ntheir protection from the main source of finite-temperature decoherence:\natomic vibrations. Here we eliminate one of the main barriers to the\nprogress in this field by providing a complete first-principles picture\nof spin relaxation that includes up to two-phonon processes. Our method\nis based on machine learning and electronic structure theory and makes\nthe prediction of spin lifetime in realistic systems feasible. We\nstudy a prototypical vanadium-based molecular qubit and reveal that\nthe spin lifetime at high temperature is limited by Raman processes\ndue to a small number of THz intramolecular vibrations. These findings\neffectively change the conventional understanding of spin relaxation\nin this class of materials and open new avenues for the rational design\nof long-living spin systems. Quantum decoherence ; Spin engineering ; Qubit ; Spin (aerodynamics) ; Spins ; Relaxation (psychology) ; Condensed matter physics ; Physics ; Quantum mechanics ; Quantum ; Spin polarization Machine Learning in Materials Science ; Organic and Molecular Conductors Research ; Molecular Junctions and Nanostructures
Authors: Yixuan Zhang | Chen Shen | Teng Long | Hongbin Zhang
Source: arXiv (Cornell University) (preprint, 2020-12-14)
Field: Materials Science — Materials Chemistry

Thermal conductivity of h-BN monolayers using machine learning interatomic potential Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential (GAP) method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation. Thermal conductivity ; Phonon ; Interatomic potential ; Materials science ; Boron nitride ; Work (physics) ; Gaussian ; Monolayer ; Condensed matter physics ; Thermal ; Lattice (music) ; Thermodynamics ; Nanotechnology ; Molecular dynamics ; Composite material ; Chemistry ; Physics ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Hythem Sidky | Wei Chen | Andrew L. Ferguson
Source: Chemical Science (preprint, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular latent space simulators Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous atomistic molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous atomistic simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce atomistic molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates. Molecular dynamics ; Statistical physics ; Discretization ; Trajectory ; Granularity ; State space ; Markov chain ; Configuration space ; Computer science ; Space (punctuation) ; Physics ; Mathematics ; Quantum mechanics ; Machine learning Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: R. Dai
Source: Open Scholarship Institutional Repository (Washington University in St. Louis) (article, 2020-07-09)
Field: Materials Science — Materials Chemistry

Structure, Thermophysical Properties of Liquids, and their Connection with Glass Formability Metallic glasses have drawn significant attention due to their unique properties, such as high strength, excellent elastic energy storage capacity, and versatile processability. However, why some liquids can easily form metallic glasses while others donմ is still unclear. Since metallic glasses are formed when liquids are cooled fast enough to bypass crystallization, we hope to better understand glass formation by investigating the structural evolution and thermophysical properties of the liquids as they are cooled toward the glass transition. Multiple molecular dynamics simulations suggest a crossover temperature for the dynamics near the liquidus temperature, which corresponds to the onset of cooperative structural rearrangements and may be the beginning of the glass transition. In this dissertation, a possible structural signature of this onset of cooperativity is first identified using high-energy synchrotron X-ray scattering studies and viscosity measurements on electrostatically levitated liquids. We also address the practical question of how to predict glass formation from properties of the high temperature liquids. A method to accurately predict the glass transition temperature in metallic glasses from properties of the equilibrium liquids is proposed. It uses the viscosity and the thermal expansion coefficient for the equilibrium liquid. Using the predicted glass transition temperature and a fragility parameter developed from the liquid properties, a new prediction formula is generated, which only uses the liquid properties. While the prediction formula works for most cases, in some cases, it fails. The analysis of these anomalous cases demonstrates that the structural similarity between the liquid and crystal phases plays an important role in the glass formability. This is the first demonstration of this important controlling factor for glass formability. We also used machine learning (Lasso regression and Random Forest) to predict the glass formability and identify important predictors. The identified important predictors are in good agreement with those from the empirical rules. Finally, the evolution of the Cu46Zr54 liquid structure is investigated by elastic neutron scattering (with isotopic substitution) and synchrotron X-ray scattering studies. The experimental results show that the number of Cu-Cu and Zr-Zr atom pairs increases as the temperature decreases, while the number of Cu-Zr atom pairs decreases on cooling. This result disagrees with predictions from previous molecular dynamics studies, suggesting that the potentials used in the molecular dynamics simulations should be reassessed. Formability ; Materials science ; Connection (principal bundle) ; Composite material ; Thermodynamics ; Metallurgy ; Mechanical engineering ; Physics ; Engineering Material Dynamics and Properties ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure