jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cristina García–Cardona | Ramakrishnan Kannan | Travis Johnston | Thomas Proffen | Katharine Page | Sudip K. Seal
Source:  (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Learning to Predict Material Structure from Neutron Scattering Data Understanding structural properties of materials and how they relate to its atomic structure, while extremely challenging, is a key scientific quest that has dominated the landscape of materials research for decades. Neutron and X-ray scattering is a state-of-the-art method to investigate material structure on the atomic scale. Traditional methods of processing neutron scattering data to decipher the structure of target materials have relied on computing scattering patterns using physics-based forward models and comparing them with experimentally gathered scattering profiles within a computationally expensive optimization loop. Here, we report an initial design of a data-driven machine learning pipeline for material structure prediction that is computationally faster (once trained) and potentially more accurate. We describe the architecture of the ML pipeline and a preliminary benchmarking study of shallow machine learning models in terms of their prediction accuracy and limitations. We show that material structure prediction from neutron scattering data using shallow learning models is feasible to within 90% prediction accuracy for certain classes of materials but deeper models are required for more general material structure predictions. Pipeline (software) ; Computer science ; Neutron scattering ; Scattering ; Benchmarking ; DECIPHER ; Machine learning ; Artificial intelligence ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Jianfeng Li | Hongdong Zhang | Jeff Z. Y. Chen
Source: Physical Review Letters (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Structural Prediction and Inverse Design by a Strongly Correlated Neural Network Macromolecules contain molecular units as the coding information for their correlated structures in physical dimensions. The relationship between these two features is governed by the interaction energies of the involved molecular units and their encoded sequences. We present a neural network algorithm that treats molecular units themselves as neural networks, which has the flexibility to allow each unit to respond to its own environment and to influence others in the system. Through a deep neural network and a self-consistent procedure, molecular units in the network establish a strong correlation to produce the desirable features in the physical world. The proposed framework is applied to the HP model. Both the forward problem of predicting folded structures from given sequences and the inverse problem of predicting required sequences for a given structure are examined. Artificial neural network ; Flexibility (engineering) ; Computer science ; Inverse ; Coding (social sciences) ; Inverse problem ; Artificial intelligence ; Algorithm ; Statistical physics ; Physics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Somayeh Faraji | S. Alireza Ghasemi | Behnam Parsaeifard | Stefan Goedecker
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Surface reconstructions and premelting of the (100) CaF<sub>2</sub>surface In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method (MHM) coupled to a machine-learning interatomic potential, that is based on a charge equilibration scheme steered by a neural network (CENT). The combination of these powerful methods revealed about 80 different morphologies for the (100) surface with very similar surface formation energies differing by not more than 0.3 J m-2. To take into account the effect of temperature on the dynamics of this surface as well as to study the solid-liquid transformation, molecular dynamics simulations were carried out in the canonical (NVT) ensemble. By analyzing the atomic mean-square displacements (MSD) of the surface layer in the temperature range of 300-1200 K, it was found that in the surface region the F sublattice is less stable and more diffusive than the Ca sublattice. Based on these results we demonstrate that not only a bulk system, but also a surface can exhibit a sublattice premelting that leads to superionicity. Both the surface sublattice premelting and surface premelting occur at temperatures considerably lower than the bulk values. The complex behaviour of the (100) surface is contrasted with the simpler behavior of other low index crystallographic surfaces. Premelting ; Surface (topology) ; Materials science ; Mathematics ; Geometry ; Composite material Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies ; High-pressure geophysics and materials
Authors: Xun Liu | Zhufeng Hou | Dabao Lu | Bo Da | Hideki Yoshikawa | Shigeo Tanuma | Yang Sun | Zejun Ding
Source: Science and Technology of Advanced Materials (article, 2019-11-13)
Field: Materials Science — Surfaces, Coatings and Films

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters. A priori and a posteriori ; Inelastic mean free path ; Computer science ; Path (computing) ; Artificial intelligence ; Machine learning ; Electron ; Stability (learning theory) ; Electron scattering ; Statistical physics ; Mean free path ; Physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Shensheng Chen | Xin Yong
Source: Langmuir (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Janus Nanoparticles Enable Entropy-Driven Mixing of Bicomponent Hydrogels Mixing incompatible polymers in water to form homogeneous hydrogels possessing both hydrophilic and lipophilic components is challenging due to high enthalpic penalty and negligible entropic gain in total Gibbs free energy. Here we performed dissipative particle dynamics simulations and machine learning to uncover the influence of Janus nanoparticles on immiscible polymer mixtures with high water content and to predict the phase behavior of bicomponent hydrogels. An intriguing transition from kinetically arrested demixing to spontaneous mixing was observed with increasing particle concentration and decreasing particle size. The analysis reveals that the mixing is driven by a significant entropic gain of small nanoparticles being well dispersed in aqueous solvent of high-volume fraction. This finding highlights an entropy-driven mixing mechanism for nanocomposite bicomponent hydrogels. Supervised machine learning algorithms were used to establish a microstructure phase diagram with respect to particle concentration and radius, in which homogeneous, percolated, clustered, and separated phases, as well as corresponding phase boundaries, were clearly identified. Self-healing hydrogels ; Dissipative particle dynamics ; Polymer ; Nanoparticle ; Chemical engineering ; Mixing (physics) ; Materials science ; Janus particles ; Phase diagram ; Entropy (arrow of time) ; Solvent ; Volume fraction ; Phase (matter) ; Janus ; Chemical physics ; Nanotechnology ; Thermodynamics ; Chemistry ; Polymer chemistry ; Organic chemistry ; Composite material Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization
Authors: Melkie Getnet Tadesse | Yan Chen | Lichuan Wang | Vincent Nierstrasz | Carmen Loghin
Source: Fibers and Polymers (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems Subjective and objective evaluations of the handle of textile materials are very important to describe its tactile comfort for next-to-skin goods. In this paper, the applicability of artificial neural-network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modeling approaches for the prediction of the psychological perceptions of functional fabrics from mechanical properties were investigated. Six distinct functional fabrics were evaluated using human subjects for their tactile score and total hand values (THV) using tactile and comfort-based fabric touch attributes. Then, the measurement of mechanical properties of the same set of samples using KES-FB was performed. The RMSE values for ANN and ANFIS predictions were 0.014 and 0.0122 and are extremely lower than the variations of the perception scores of 0.644 and 0.85 for ANN and ANFIS, respectively with fewer prediction errors. The observed results indicated that the predicted tactile score and THV are almost very close to the actual output obtained using the human judgment. Fabric objective measurement technology, therefore, provides reliable measurement approaches for functional fabric quality inspection, control, and design specification. Adaptive neuro fuzzy inference system ; Tactile perception ; Artificial neural network ; Artificial intelligence ; Tactile sensor ; Computer science ; Mean squared error ; Perception ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Fuzzy logic ; Statistics ; Mathematics ; Fuzzy control system Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Industrial Vision Systems and Defect Detection
Authors: Alireza Cheshmeh Sefidi | Farhad Ajorkaran
Source: Petroleum Science and Technology (article, 2019-10-01)
Field: Chemistry — Analytical Chemistry

A novel MLP-ANN approach to predict solution gas-oil ratio The better understanding and estimation of reservoir fluids properties have straight effects on accuracy of different processes such as simulation, well testing, and material balance calculations, so importance of accurate estimation of PVT properties such as solution gas-oil ratio is obvious. To this end, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel predictive tool to estimate solution gas-oil ratio in terms of temperature, bubble point pressure, oil American Petroleum Institute gravity API, and gas specific gravity. Therefore, a total number of 1,137 experimental solution gas-oil ratios has been gathered from reliable databank for evaluation of model outputs. The different graphical and statistical analyses such as determination of average absolute relative deviation (AARD), the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the performance of MLP-ANN algorithm. The comparisons show that MLP-ANN algorithm has great accuracy in prediction of solution gas-oil ratio, so it can be used as a simple tool to predict phase behavior of reservoir fluids. Mean squared error ; Gas oil ratio ; Multilayer perceptron ; Artificial neural network ; Bubble point ; API gravity ; Approximation error ; Specific gravity ; Coefficient of determination ; Absolute deviation ; Petroleum engineering ; Computer science ; Mathematics ; Bubble ; Statistics ; Chemistry ; Crude oil ; Machine learning ; Geology ; Mineralogy Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Sean Donegan | Navneet Kumar | Michael A. Groeber
Source: Materials Characterization (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Associating local microstructure with predicted thermally-induced stress hotspots using convolutional neural networks Materials science ; Microstructure ; Convolutional neural network ; Stress (linguistics) ; Composite material ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; X-ray Diffraction in Crystallography
Authors: Xingcheng Lin | Nicholas P. Schafer | Wei Lu | Shikai Jin | Xun Chen | Mingchen Chen | José N. Onuchic | Peter G. Wolynes
Source: Proceedings of the National Academy of Sciences (article, 2019-04-18)
Field: Materials Science — Materials Chemistry

Forging tools for refining predicted protein structures Refining predicted protein structures with all-atom molecular dynamics simulations is one route to producing, entirely by computational means, structural models of proteins that rival in quality those that are determined by X-ray diffraction experiments. Slow rearrangements within the compact folded state, however, make routine refinement of predicted structures by unrestrained simulations infeasible. In this work, we draw inspiration from the fields of metallurgy and blacksmithing, where practitioners have worked out practical means of controlling equilibration by mechanically deforming their samples. We describe a two-step refinement procedure that involves identifying collective variables for mechanical deformations using a coarse-grained model and then sampling along these deformation modes in all-atom simulations. Identifying those low-frequency collective modes that change the contact map the most proves to be an effective strategy for choosing which deformations to use for sampling. The method is tested on 20 refinement targets from the CASP12 competition and is found to induce large structural rearrangements that drive the structures closer to the experimentally determined structures during relatively short all-atom simulations of 50 ns. By examining the accuracy of side-chain rotamer states in subensembles of structures that have varying degrees of similarity to the experimental structure, we identified the reorientation of aromatic side chains as a step that remains slow even when encouraging global mechanical deformations in the all-atom simulations. Reducing the side-chain rotamer isomerization barriers in the all-atom force field is found to further speed up refinement. Molecular dynamics ; Force field (fiction) ; Atom (system on chip) ; Refining (metallurgy) ; Isomerization ; Deformation (meteorology) ; Similarity (geometry) ; Side chain ; Work (physics) ; Conformational isomerism ; Computer science ; Diffraction ; Statistical physics ; Chemical physics ; Crystallography ; Chemistry ; Computational chemistry ; Physics ; Molecule ; Thermodynamics ; Artificial intelligence Enzyme Structure and Function ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Nadi Braidy | Ryan Gosselin
Source: Scientific Reports (article, 2019-12-11)
Field: Chemistry — Electrochemistry

Unmixing noisy co-registered spectrum images of multicomponent nanostructures Analytical electron microscopy plays a key role in the development of novel nanomaterials. Electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDX) datasets are typically processed to isolate the background-subtracted elemental signal. Multivariate tools have emerged as powerful methods to blindly map the components, which addresses some of the shortcomings of the traditional methods. Here, we demonstrate the superior performance of a new multivariate optimization method using a challenging EELS and EDX dataset. The dataset was recorded from a spectrum image P-type metal-oxide-semiconductor stack with 7 components exhibiting heavy spectral overlap and a low signal-to-noise ratio. Compared to peak integration, independent component analysis, Baysian Linear Unmixing and Non-negative matrix factorization, the method proposed was the only one to identify the EELS spectra of all 7 components with the corresponding abundance profiles. Using the abundance of each component, it was possible to retrieve the EDX spectra of all the components, which were otherwise impossible to isolate, regardless of the method used. We expect that this robust method will bring a significant improvement for the chemical analysis of nanomaterials, especially for weak signals, dose-sensitive specimen or signals suffering heavy spectral overlap. Spectroscopy ; Nanomaterials ; Spectral line ; SIGNAL (programming language) ; Materials science ; Signal-to-noise ratio (imaging) ; Noise (video) ; Multivariate statistics ; Biological system ; Energy (signal processing) ; Computer science ; Component (thermodynamics) ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Physics ; Nanotechnology ; Chemistry ; Artificial intelligence ; Optics ; Image (mathematics) ; Mathematics ; Statistics ; Biology ; Machine learning Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Blind Source Separation Techniques
Authors: Olalekan Alade | Dhafer Al Shehri | Mohamed Mahmoud | Kyuro Sasaki
Source: Energies (article, 2019-06-21)
Field: Chemistry — Analytical Chemistry

Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN) The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study. Viscosity ; Asphalt ; Atmospheric pressure ; Analytical Chemistry (journal) ; Empirical modelling ; Asphaltene ; Chemistry ; Thermodynamics ; Materials science ; Mineralogy ; Chromatography ; Composite material ; Physics ; Meteorology ; Organic chemistry ; Simulation Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Lester Ingber
Source: Sci (article, 2019-03-21)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum Calcium-Ion Interactions with EEG Background: Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated Annealing (ASA) has been developed to perform fits to such nonlinear stochastic systems. An N-dimensional path-integral algorithm for quantum systems, qPATHINT, has been developed from classical PATHINT. Both fold short-time propagators (distributions or wave functions) over long times. Previous papers applied qPATHINT to two systems, in neocortical interactions and financial options. Objective: In this paper the quantum path-integral for Calcium ions is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Using fits of this SMNI model to EEG data, including these effects, will help determine if this is a reasonable approach. Method: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used for this project. Studies using supercomputer resources tested various dimensions for their scaling limits. In this paper the quantum path-integral is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Results: The mathematical-physics and computer parts of the study are successful, in that there is modest improvement of cost/objective functions used to fit EEG data using these models. Conclusions: This project points to directions for more detailed calculations using more EEG data and qPATHINT at each time slice to propagate quantum calcium waves, synchronized with PATHINT propagation of classical SMNI. Quantum ; Propagator ; Path integral formulation ; Scaling ; Electroencephalography ; Quantum computer ; Nonlinear system ; Lattice (music) Advanced Physical and Chemical Molecular Interactions ; Statistical Mechanics and Entropy ; Functional Brain Connectivity Studies
Authors: Lia Yeh | Lincoln Satterthwaite | David Patterson
Source: The Journal of Chemical Physics (article, 2019-05-28)
Field: Chemistry — Spectroscopy

Automated, context-free assignment of asymmetric rotor microwave spectra We present a new algorithm, Robust Automated Assignment of Rigid Rotors (RAARR), for assigning rotational spectra of asymmetric tops. The RAARR algorithm can automatically assign experimental spectra under a broad range of conditions, including spectra comprised of multiple mixture components, in ≲100 s. The RAARR algorithm exploits constraints placed by the conservation of energy to find sets of connected lines in an unassigned spectrum. The highly constrained structure of these sets eliminates all but a handful of plausible assignments for a given set, greatly reducing the number of potential assignments that must be evaluated. We successfully apply our algorithm to automatically assign 15 experimental spectra, including 5 previously unassigned species, without prior estimation of molecular rotational constants. In 9 of the 15 cases, the RAARR algorithm successfully assigns two or more mixture components. Spectral line ; Context (archaeology) ; Set (abstract data type) ; Algorithm ; Range (aeronautics) ; Computer science ; Rotor (electric) ; Microwave ; Physics ; Materials science ; Quantum mechanics Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies