jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng Gong | Tian Xie | Taishan Zhu | Shuo Wang | Eric Fadel | Yawei Li | Jeffrey C. Grossman
Source: Physical review. B./Physical review. B (article, 2019-11-07)
Field: Materials Science — Materials Chemistry

Predicting charge density distribution of materials using a local-environment-based graph convolutional network The electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the scaling of density functional theory calculations with number of atoms limits the usage of charge-density-based calculations and analyses. Here we introduce a machine-learning scheme with local-environment-based graphs and graph convolutional neural networks to predict charge density on grid points from the crystal structure. We show the accuracy of this scheme through a comparison of predicted charge densities as well as properties derived from the charge density, and that the scaling is $O$($N$). More importantly, the transferability is shown to be high with respect to different compositions and structures, which results from the explicit encoding of geometry. Transferability ; Charge density ; Scaling ; Statistical physics ; Charge (physics) ; Convolutional neural network ; Graph ; Density functional theory ; Molecular graph ; Electron density ; Computer science ; Graph theory ; Grid ; Algorithm ; Theoretical computer science ; Physics ; Mathematics ; Electron ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Geometry ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; CO2 Reduction Techniques and Catalysts
Authors: Christopher Sutton | Luca M. Ghiringhelli | T. Yamamoto | Yury Lysogorskiy | Lars Blumenthal | Thomas Hammerschmidt | Jacek Gołębiowski | Xiang‐Yue Liu | Angelo Ziletti | Matthias Scheffler
Source: npj Computational Materials (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition Abstract A public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (Al x Ga y In 1– x – y ) 2 O 3 compounds. Its aim was to identify the best machine-learning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy. Pearson product-moment correlation coefficient ; Computer science ; Artificial intelligence ; Predictive modelling ; Correlation ; Machine learning ; Gradient boosting ; Representation (politics) ; Regression analysis ; Regression ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Yaolong Zhang | Reinhard J. Maurer | Bin Jiang
Source: The Journal of Physical Chemistry C (article, 2019-11-25)
Field: Materials Science — Materials Chemistry

Symmetry-Adapted High Dimensional Neural Network Representation of Electronic Friction Tensor of Adsorbates on Metals Nonadiabatic effects in chemical reaction at metal surfaces, due to excitation of electron–hole pairs, stand at the frontier of the studies of gas-surface reaction dynamics. However, the first-principles description of electronic excitation remains challenging. In an efficient molecular dynamics with electronic friction (MDEF) method, the nonadiabatic couplings are effectively included in a so-called electronic friction tensor (EFT), which can be computed from first-order time-dependent perturbation theory (TDPT) in terms of density functional theory (DFT) orbitals. This second-rank tensor depends on adsorbate position and features a complicated transformation with regard to the intrinsic symmetry operations of the system. In this work, we develop a new symmetry-adapted neural network representation of EFT, based on our recently proposed embedded atom neural network (EANN) framework. Inspired by the derivation of the nonadiabatic coupling matrix, we represent the tensorial friction by the first and second derivatives of multiple outputs of NNs with respect to atomic Cartesian coordinates. This rigorously preserves the positive semidefiniteness, directional property, and correct symmetry-equivariance of EFT. Unlike previous methods, our new approach can readily include both molecular and surface degrees of freedom, regardless of the type of surface. Tests on the H2 + Ag(111) system show that this approach yields an accurate, efficient, and continuous representation of EFT, making it possible to perform large scale TDPT-based MDEF simulations to study both adiabatic and nonadiabatic energy dissipation in a unified framework. Tensor (intrinsic definition) ; Symmetry (geometry) ; Adiabatic process ; Physics ; Electronic structure ; Representation (politics) ; Degrees of freedom (physics and chemistry) ; Excitation ; Atomic orbital ; Dissipation ; Density functional theory ; Classical mechanics ; Statistical physics ; Quantum mechanics ; Electron ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Tu C. Le | Matthew Penna | David A. Winkler | Irene Yarovsky
Source: Scientific Reports (article, 2019-01-22)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative design rules for protein-resistant surface coatings using machine learning Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r<sup>2</sup> of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types. Computer science ; Surface (topology) ; Mathematics Polymer Surface Interaction Studies ; Nanofabrication and Lithography Techniques ; Advanced Polymer Synthesis and Characterization
Authors: P. Nieves | S. Arapan | Jesús Maudes | Raúl Marticorena Sánchez | N.L. Del Brío | Alexander Kovacs | Cristina Echevarría–Bonet | Daniel Salazar | Jürgen Weischenberg | Hongbin Zhang | Olga Yu. Vekilova | Roberto Serrano-López | J.M. Barandiarán | Konstantin Skokov | Oliver Gutfleisch | Olle Eriksson | Heike C. Herper | T. Schrefl | Santiago Cuesta‐López
Source: Computational Materials Science (article, 2019-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Database of novel magnetic materials for high-performance permanent magnet development Magnetocrystalline anisotropy ; Magnet ; Coercivity ; Materials science ; Computer science ; Curie temperature ; Condensed matter physics ; Ferromagnetism ; Mechanical engineering ; Magnetic anisotropy ; Physics ; Magnetic field ; Engineering ; Magnetization Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Hydrogen Storage and Materials
Authors: Xiaoguang Ma | Zoey S. Davidson | Tim Still | Robert Ivancic | Samuel S. Schoenholz | Andrea J. Liu | Arjun G. Yodh
Source: Physical Review Letters (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Heterogeneous Activation, Local Structure, and Softness in Supercooled Colloidal Liquids We experimentally characterize heterogeneous nonexponential relaxation in bidisperse supercooled colloidal liquids utilizing a recent concept called "softness" [Phys. Rev. Lett. 114, 108001 (2015)PRLTAO0031-900710.1103/PhysRevLett.114.108001]. Particle trajectory and structure data enable classification of particles into subgroups with different local environments and propensities to hop. We determine residence times t_{R} between particle hops and show that t_{R} derived from particles in the same softness subgroup are exponentially distributed. Using the mean residence time t[over ¯]_{R} for each softness subgroup, and a Kramers' reaction rate model, we estimate the activation energy barriers E_{b} for particle hops, and show that both t[over ¯]_{R} and E_{b} are monotonic functions of softness. Finally, we derive information about the combinations of large and small particle neighbors that determine particle softness, and we explicitly show that multiple exponential relaxation channels in the supercooled liquid give rise to its nonexponential behavior. Supercooling ; Particle (ecology) ; Relaxation (psychology) ; Residence time (fluid dynamics) ; Colloid ; Statistical physics ; Monotonic function ; Chemical physics ; Exponential function ; Materials science ; Physics ; Condensed matter physics ; Thermodynamics ; Chemistry ; Physical chemistry ; Mathematics ; Mathematical analysis Material Dynamics and Properties
Authors: Chenlei Ru | Zhenhao Li | Renzhong Tang
Source: Sensors (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI) Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435-1042 nm) and short-wave infrared (SWIR, 898-1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs). VNIR ; Hyperspectral imaging ; Remote sensing ; Image fusion ; Artificial intelligence ; Support vector machine ; Computer science ; Sensor fusion ; Pattern recognition (psychology) ; Geography ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Michael A. Liu | Pedro E.J. Rivera-Díaz-del-Castillo | Jesús Israel Barraza-Fierro | Homero Castaneda | Ankit Srivastava
Source: Materials & Design (article, 2019-02-09)
Field: Materials Science — Metals and Alloys

Microstructural influence on hydrogen permeation and trapping in steels The microstructural influence on hydrogen permeation and trapping in pure iron and two ferritic-pearlitic steels, AISI 1018 and AISI 4340 is quantified. To this end, hydrogen is introduced into specimens of these materials through electrochemical charging and the total hydrogen content of the specimens are quantified following gas fusion analysis principle. Furthermore, a modeling framework based on Fickian diffusion equations including the relevant microstructural features, electrochemical charging conditions and three-dimensional geometry of the specimen affecting the overall diffusion behavior is adopted to describe the time-dependence of hydrogen content in the three materials. The approach quantitatively describes the hydrogen ingress into the three materials, as well as its distribution across various defects and microstructural features. Traps of two potencies are identified, dislocations and grain boundaries (trap 1), and ferrite/cementite interfaces (trap 2). The former are shown to be responsible for the trapped hydrogen at early stages of its ingress, whereas trap 2 is shown to gather trapped hydrogen at later stages. The ability to design microstructures to control hydrogen ingress and diffusion is discussed, showing how the framework presented here can be adopted for controlling hydrogen in commercial components, and how this can delay hydrogen-related embrittlement. Hydrogen ; Materials science ; Cementite ; Permeation ; Hydrogen embrittlement ; Trapping ; Diffusion ; Microstructure ; Ferrite (magnet) ; Embrittlement ; Metallurgy ; Grain boundary ; Electrochemistry ; Composite material ; Thermodynamics ; Corrosion ; Membrane ; Austenite ; Physical chemistry ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Nuclear Materials and Properties
Authors: Xiang‐Yu Chen | Maoping Pu | Hong‐Gang Cheng | Theresa Sperger | Franziska Schoenebeck
Source: Angewandte Chemie International Edition (article, 2019-06-13)
Field: Chemistry — Organic Chemistry

Arylation of Axially Chiral Phosphorothioate Salts by Dinuclear Pd<sup>I</sup> Catalysis S-aryl phosphorothioates are privileged motifs in pharmaceuticals, agrochemicals, and catalysts; yet, the challenge of devising a straightforward synthetic route to enantioenriched S-aryl phosphorothioates has remained unsolved to date. We demonstrate herein the first direct C-SP(=O)(OR')(OR'') coupling of diverse and chiral phosphorothioate salts with aryl iodides, enabled by an air- and moisture-stable Pd<sup>I</sup> dimer. Our mechanistic and computational data suggest distinct dinuclear Pd<sup>I</sup> catalysis to be operative, which allows for operationally simple couplings with broad scope and full retention of stereochemistry. Aryl ; Catalysis ; Chemistry ; Combinatorial chemistry ; Dimer ; Organic chemistry ; Computational chemistry ; Stereochemistry Sulfur-Based Synthesis Techniques ; Axial and Atropisomeric Chirality Synthesis ; Catalytic C–H Functionalization Methods
Authors: Taiqun Yang | Bingqian Shan | Fang Huang | Songqiu Yang | Bo Peng | En‐Hui Yuan | Peng Wu | Kun Zhang
Source: Communications Chemistry (article, 2019-11-15)
Field: Materials Science — Materials Chemistry

P band intermediate state (PBIS) tailors photoluminescence emission at confined nanoscale interface Abstract The availability of a range of excited states has endowed low dimensional quantum nanostructures with interesting luminescence properties. However, the origin of photoluminescence emission is still not fully understood, which has limited its practical application. Here we judiciously manipulate the delicate surface ligand interactions at the nanoscale interface of a single metal nanocluster, the superlattice, and mesoporous materials. The resulting interplay of various noncovalent interactions leads to a precise modulation of emission colors and quantum yield. A new p-band state, resulting from the strong overlapping of p orbitals of the heteroatoms (O, N, and S) bearing on the targeting ligands though space interactions, is identified as a dark state to activate the triplet state of the surface aggregated chromophores. The UV-Visible spectra calculated by time-dependent density functional theory (TD-DFT) are in quantitative agreement with the experimental adsorption spectra. The energy level of the p-band center is very sensitive to the local proximity ligand chromophores at heterogeneous interfaces. Photoluminescence ; Excited state ; Chromophore ; Quantum yield ; Density functional theory ; Chemical physics ; Materials science ; Luminescence ; Atomic orbital ; Chemistry ; Photochemistry ; Nanotechnology ; Atomic physics ; Optoelectronics ; Fluorescence ; Computational chemistry ; Physics ; Optics ; Electron ; Quantum mechanics Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Wilhelm F. Maier
Source: ACS Combinatorial Science (review, 2019-04-02)
Field: Materials Science — Materials Chemistry

Early Years of High-Throughput Experimentation and Combinatorial Approaches in Catalysis and Materials Science This is a report on the early years of combinatorial materials science and technology. High-throughput technologies (HTTs) are found in life- and materials-science laboratories. Although HTTs have long been the standard in life sciences in academia as well as in industry, HTTs in materials science have become the standard in industry but not in academia. In life science, successful drugs developed with HTTs have been reported, but there is no information on successful materials developed with HTTs that have made it to the market. Some initial development of HTTs in materials science is summarized, especially early applications of artificial intelligence. This outlook attempts to summarize the development of combinatorial materials sciences from the early years to today. Chemistry ; Nanotechnology ; Combinatorial chemistry ; Biochemical engineering ; Engineering ; Materials science Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Daniel J. Gaspar | Steven Phillips | Evgueni Polikarpov | Karl Albrecht | Susanne B. Jones | Anthe George | Alexander Landera | Daniel M. Santosa | Daniel Howe | Anna G. Baldwin | J. Timothy Bays
Source: Fuel (article, 2019-02-19)
Field: Chemistry — Organic Chemistry

Measuring and predicting the vapor pressure of gasoline containing oxygenates Oxygenate ; Gasoline ; Volatility (finance) ; Vapor pressure ; Chemistry ; Process engineering ; Environmental science ; Organic chemistry ; Catalysis ; Engineering ; Mathematics ; Econometrics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Thermodynamic properties of mixtures
Authors: Viswanathan S. Saji
Source: Corrosion Reviews (article, 2019-03-16)
Field: Materials Science — Materials Chemistry

Supramolecular concepts and approaches in corrosion and biofouling prevention Abstract Supramolecular chemistry is one of the exciting branches of chemistry where non-covalent interactions between molecules and the ensuing supramolecular structures have been studied for various applications. The present review provides a comprehensive outlook on the applications and potentials of supramolecular chemistry in corrosion and biofouling prevention. Reported works associating supramolecular chemistry with corrosion are systematically discussed under two sections: (i) surface coatings and (ii) corrosion inhibitors that include supramolecular polymers, host-guest inclusion compounds, organic-inorganic hybrid materials, and supramolecular structures of graphene, crown ethers, self-assembled monolayers, etc. Different strategies for making antifouling surfaces based on block copolymers/gel systems, host-guest systems, and metal-organic structures are briefed. Cyclodextrin and mesoporous silica-based host-guest systems are extensively discussed, as they are the most prominent materials of current research interest. Future potentials for developments are presented. The review is expected to be beneficial to enhance supramolecular chemistry-related research and development in corrosion and biofouling prevention. Supramolecular chemistry ; Biofouling ; Nanotechnology ; Materials science ; Supramolecular polymers ; Corrosion ; Molecule ; Chemistry ; Organic chemistry ; Metallurgy Corrosion Behavior and Inhibition ; Marine Biology and Environmental Chemistry ; Polymer Surface Interaction Studies