jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anh D. Phan | Katsunori Wakabayashi | Marian Paluch | Vũ Đình Lãm
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecule as a coupled process of cage-scale dynamics and collective molecular rearrangement beyond the first coordination shell. The coupling between local and non-local dynamics behaves distinctly in different substances. Theoretical calculations for the structural relaxation time, glass transition temperature, and dynamic fragility are carried out over twenty-two amorphous drugs and polymers. Numerical results have a quantitatively good accordance with experimental data and the extracted physical quantities using the Vogel-Fulcher-Tammann fit function and machine learning. The machine learning method reveals the linear relation between the glass transition temperature and the melting point, which is a key factor for pharmaceutical solubility. Our predictive approaches are reliable tools for developing drug formulations. Glass transition ; Amorphous solid ; Fragility ; Relaxation (psychology) ; Molecular dynamics ; Thermodynamics ; Melting point ; Materials science ; Statistical physics ; Polymer ; Chemistry ; Physics ; Computational chemistry ; Crystallography ; Composite material Material Dynamics and Properties ; Thermodynamic properties of mixtures ; Phase Equilibria and Thermodynamics
Authors: Nouamane Laanait | Joshua Romero | Junqi Yin | M. Todd Young | Sean Treichler | Vitalii Starchenko | Albina Y. Borisevich | A. E. Sergeev | Michael A. Matheson
Source: arXiv (Cornell University) (preprint, 2019-09-24)
Field: Materials Science — Materials Chemistry

Exascale Deep Learning for Scientific Inverse Problems We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$. Inverse ; Deep learning ; Computer science ; Artificial intelligence ; Data science ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and Data Classification
Authors: Maxim Ziatdinov | Ondrej Dyck | Stephen Jesse | Sergei V. Kalinin
Source: Advanced Functional Materials (article, 2019-11-22)
Field: Materials Science — Materials Chemistry

Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk Abstract The dynamic behavior of e‐beam irradiated Si atoms in the bulk and at the edges of single‐layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene. Zigzag ; Graphene ; Impurity ; Scanning transmission electron microscopy ; Atom (system on chip) ; Chemical bond ; Crystal (programming language) ; Enhanced Data Rates for GSM Evolution ; Silicon Graphene research and applications ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Janis Timoshenko | Mahdi Ahmadi | Beatriz Roldán Cuenya
Source: The Journal of Physical Chemistry C (article, 2019-07-19)
Field: Materials Science — Materials Chemistry

Is There a Negative Thermal Expansion in Supported Metal Nanoparticles? An in Situ X-ray Absorption Study Coupled with Neural Network Analysis Interactions with their support, adsorbates and unique structural motifs are responsible for the many intriguing properties and potential applications of supported metal nanoparticles (NPs). At the same time, they complicate the interpretation of experimental data. In fact, the methods and approaches that work well for the ex situ analysis of bulk materials may be inaccurate or introduce artifacts in the in situ analysis of nanomaterials. Here we revisit the controversial topic of negative thermal expansion and anomalies in the Debye temperature reported for oxide-supported metal NPs. In situ X-ray absorption experimental data collected for Pt NPs in ultrahigh vacuum and an advanced data analysis approach based on an artificial neural network demonstrate that Pt NPs do not exhibit intrinsic negative thermal expansion. Similarly as for bulk materials, in the absence of adsorbates the bond lengths in metal NPs increase with temperature. The previously reported anomalies in particle size-dependent Debye temperatures can also be linked to the artifacts in the interpretation of conventional X-ray absorption data of disordered materials such as NPs. Nanoparticle ; Materials science ; In situ ; Absorption (acoustics) ; Thermal expansion ; Nanomaterials ; Particle (ecology) ; Metal ; Oxide ; Debye model ; Nanotechnology ; Thermal ; Chemical physics ; Chemistry ; Thermodynamics ; Composite material ; Physics ; Organic chemistry Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Emanuele Bosoni | Davide Campi | Davide Donadio | Gabriele C. Sosso | Jörg Behler | Marco Bernasconi
Source: Journal of Physics D Applied Physics (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Atomistic simulations of thermal conductivity in GeTe nanowires Abstract The thermal conductivity of GeTe crystalline nanowires has been computed by means of non-equilibrium molecular dynamics simulations employing a machine learning interatomic potential. This material is of interest for application in phase change non-volatile memories. The resulting lattice thermal conductivity of an ultrathin nanowire (7.3 nm diameter) of 1.57 W m −1 K −1 is sizably lower than the corresponding bulk value of 3.15 W m −1 K −1 obtained within the same framework. The analysis of the phonon dispersion relations and lifetimes reveals that the lower thermal conductivity in the nanowire is mostly due to a reduction in the phonon group velocities. We further predict the presence of a minimum in the lattice thermal conductivity for thicker nanowires. Thermal conductivity ; Nanowire ; Materials science ; Molecular dynamics ; Conductivity ; Thermal ; Condensed matter physics ; Thermal conduction ; Nanotechnology ; Thermodynamics ; Composite material ; Physics ; Chemistry ; Computational chemistry ; Physical chemistry Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Grzegorz Chojnowski | Joana Pereira | Victor S. Lamzin
Source: Acta Crystallographica Section D Structural Biology (article, 2019-07-31)
Field: Materials Science — Materials Chemistry

Sequence assignment for low-resolution modelling of protein crystal structures The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP. Computer science ; Sequence (biology) ; Fragmentation (computing) ; Low resolution ; Docking (animal) ; Resolution (logic) ; Algorithm ; Artificial intelligence ; High resolution ; Chemistry ; Programming language ; Geography ; Remote sensing Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Tongqi Wen | Beilin Ye | Honghua Liu | Shanshan Ning | Cai‐Zhuang Wang | Yanhui Chu
Source: Journal of the American Ceramic Society (article, 2019-12-31)
Field: Materials Science — Materials Chemistry

Formation criterion for binary metal diboride solid solutions established through combinatorial methods Abstract Establishing the formation criterion is urgent for accelerating the discovery and design of solid‐solution materials with desirable properties. The previously reported formation criterion mainly focused on solid‐solution alloys, while the formation criterion was rarely established in solid‐solution ceramics. To solve this problem, herein, we take a class of solid‐solution ceramics, namely binary metal diboride ((M x N 1‐ x )B 2 ) solid solutions, as a prototype. Through combinatorial methods including high‐throughput molten salt syntheses and high‐throughput first‐principles calculations combined with the machine learning approach, the correlation between influential factors, including atomic size difference ( δ ), mixing enthalpy at 0 K and 0 Pa ( ), doping condition ( φ ), and valence electron concentration ( VEC ), and the formation ability of (M x N 1‐ x )B 2 solid solutions was first studied systematically, and then their formation criterion was well established. The results showed that the influential degree of the aforementioned four factors on the formation ability of (M x N 1‐ x )B 2 solid solutions could be described as follows: δ &gt; &gt; φ &gt; VEC . In addition, a newly proposed parameter, β , could well reflect the formation ability of (M x N 1‐ x )B 2 solid solutions: when β &gt; 0, the single‐phase (M x N 1‐ x )B 2 solid solutions could be successfully synthesized in our work and vice versa. This study may provide a theoretical guidance in the discovery and design of various solid‐solution ceramics, such as the metal borides, carbides, nitrides, etc, with desirable properties. Solid solution ; Materials science ; Ceramic ; Bonding in solids ; MAX phases ; Thermodynamics ; Metal ; Physics ; Metallurgy MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Hsiu‐Wen Wang | Trent R. Graham | Eugene Mamontov | Katharine Page | Andrew G. Stack | Carolyn I. Pearce
Source: The Journal of Physical Chemistry Letters (article, 2019-05-30)
Field: Materials Science — Biomaterials

Countercations Control Local Specific Bonding Interactions and Nucleation Mechanisms in Concentrated Water-in-Salt Solutions One of the continuing challenges presented in salt solutions is understanding ion association reactions driving dynamic demixing from solvation, complexation, and solute clustering. The problems understanding this phenomenon are exacerbated in the highly concentrated water-in-salt solutions, where the deficiency of water leads to a dramatic retardation of water solvent and formation of extended solvent-solute clustering networks. By probing microscopic dynamics of water and prenucleation clusters using quasi-elastic neutron scattering and proton nuclear magnetic resonance spectroscopy, we observed contrasting mechanistic specifics of ion-water mobilities in highly concentrated Na<sup>+</sup>- versus K<sup>+</sup>-based aluminate solutions (diffusion coefficients of 0.2 vs 2.6 × 10<sup>-10</sup> m<sup>2</sup> s<sup>-1</sup> at 293 K, respectively). The magnitude of the differences is far beyond countercations acting as simple innocent charge-balancing species or water solvents functioning as a simple medium for ion diffusion. The distinct crystallization mechanisms observed further imply that different prenucleation cluster dynamics can either frustrate or promote crystallization, as described by nonclassical nucleation theory. Nucleation ; Salt (chemistry) ; Salt water ; Chemistry ; Chemical engineering ; Chemical physics ; Salt solution ; Control (management) ; Environmental science ; Environmental engineering ; Computer science ; Physical chemistry ; Organic chemistry ; Engineering Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions
Authors: Michał Maciusowicz | Grzegorz Psuj
Source: Sensors (article, 2019-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Use of Time-Dependent Multispectral Representation of Magnetic Barkhausen Noise Signals for the Needs of Non-Destructive Evaluation of Steel Materials Due to the existing relationship between microstructural properties and magnetic ones of the ferromagnetic materials, the application potential of the magnetic Barkhausen noise (BN) method to non-destructive testing is constantly growing. However, the stochastic nature of the Barkhausen effect requires the use of advanced signal processing methods. Recently, the need to apply time-frequency (<i>TF</i>) transformations to the processing of BN signals arose. However, various <i>TF</i> methods have been used in the majority of cases for qualitative signal conditioning and no extensive analysis of <i>TF</i>-based information has been conducted so far. Therefore, in this paper, the wide analysis of BN <i>TF</i> representation was carried out. Considering the properties of <i>TF</i> transformations, the Short-Time Fourier Transform (STFT) was used. A procedure for definition of the envelopes of the <i>TF</i> characteristic was proposed. To verify the quality of extracted features, an analysis was performed on the basis of BN signals acquired during stress loading experiments of steel elements. First, the preliminary experiments were processed for various parameters of the measuring system and calculation procedures. The feature extraction procedure was performed for different modes of <i>TF</i> representations. Finally, the distributions of <i>TF</i> features over the loading stages are presented and their information content was validated using commonly used features derived from time <i>T</i> and frequency <i>F</i> domains. Barkhausen effect ; Time–frequency analysis ; Short-time Fourier transform ; Noise (video) ; Computer science ; Nondestructive testing ; Signal processing ; Representation (politics) ; Acoustics ; SIGNAL (programming language) ; Basis (linear algebra) ; Fourier transform ; Feature (linguistics) ; Biological system ; Pattern recognition (psychology) ; Materials science ; Electronic engineering ; Artificial intelligence ; Engineering ; Fourier analysis ; Mathematics ; Physics ; Computer vision Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Structural Health Monitoring Techniques
Authors: Sebastian Schwalbe | Kai Trepte | Lenz Fiedler | Alexander I. Johnson | Jakob Kraus | Torsten Hahn | Juan E. Peralta | Koblar Alan Jackson | Jens Kortus
Source: Journal of Computational Chemistry (article, 2019-09-10)
Field: Materials Science — Materials Chemistry

Interpretation and Automatic Generation of Fermi‐Orbital Descriptors We present an interpretation of Fermi-orbital descriptors (FODs) and argue that these descriptors carry chemical bonding information. We show that a bond order derived from these FODs agrees well with reference values, and highlight that optimized FOD positions used within the Fermi-Löwdin orbital self-interaction correction (FLO-SIC) method correspond to expectations from Linnett's double-quartet theory, which is an extension of Lewis theory. This observation is independent of the underlying exchange-correlation functional, which is shown using the local spin density approximation, the Perdew-Burke-Ernzerhof generalized gradient approximation (GGA), and the strongly constrained and appropriately normed meta-GGA. To make FOD positions generally accessible, we propose and discuss four independent methods for the generation of Fermi-orbital descriptors, their implementation as well as their advantages and drawbacks. In particular, we introduce a re-implementation of the electron force field, an approach based on the centers of mass of orbital densities, a Monte Carlo-based algorithm, and a method based on Lewis-like bonding information. All results are summarized with respect to future developments of FLO-SIC and related methods. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc. Interpretation (philosophy) ; Fermi Gamma-ray Space Telescope ; Density functional theory ; Fermi level ; Statistical physics ; Field (mathematics) ; Physics ; Chemistry ; Electron ; Theoretical physics ; Quantum mechanics ; Computer science ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Jesús Ferrando‐Soria | Antonio Fernández | Deepak Asthana | Selina Nawaz | Íñigo J. Vitórica‐Yrezábal | George F. S. Whitehead | C.A. Muryn | Floriana Tuna | Grigore A. Timco | Neil D. Burton | Richard E. P. Winpenny
Source: Nature Communications (article, 2019-08-16)
Field: Chemistry — Organic Chemistry

A [13]rotaxane assembled via a palladium molecular capsule Molecules that are the size of small proteins are difficult to make. The most frequently examined route is via self-assembly, and one particular approach involves molecular nanocapsules, where ligands are designed that will enforce the formation of specific polyhedra of metals within the core of the structure. Here we show that this approach can be combined with mechanically interlocking molecules to produce nanocapsules that are decorated on their exterior. This could be a general route to very large molecules, and is exemplified here by the synthesis and structural characterization of a [13]rotaxane, containing 150 metal centres. Small angle X-ray scattering combined with atomistic molecular dynamics simulations demonstrate the compound is intact in solution. Nanocapsules ; Rotaxane ; Molecule ; Palladium ; Materials science ; Nanotechnology ; Molecular dynamics ; Self-assembly ; Chemistry ; Nanoparticle ; Supramolecular chemistry ; Computational chemistry ; Organic chemistry ; Catalysis Supramolecular Chemistry and Complexes ; Advanced NMR Techniques and Applications ; Magnetism in coordination complexes
Authors: Steven Kearnes | Li Li | Patrick Riley
Source: arXiv (Cornell University) (preprint, 2019-04-18)
Field: Materials Science — Materials Chemistry

Decoding Molecular Graph Embeddings with Reinforcement Learning We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated decoders that increase the complexity of training and evaluation (such as requiring parallel encoders and decoders or non-trivial graph matching). Here, we repurpose a simple graph generator to enable efficient decoding and generation of molecular graphs. Autoencoder ; Molecular graph ; Computer science ; Reinforcement learning ; Graph ; Decoding methods ; Theoretical computer science ; Encoder ; Matching (statistics) ; Artificial intelligence ; Algorithm ; Deep learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Monika Duhan | Raminder Kaur
Source: Environmental Nanotechnology Monitoring & Management (article, 2019-07-31)
Field: Materials Science — Polymers and Plastics

Phytic acid doped polyaniline nanofibers: An advanced adsorbent for methylene blue dye Freundlich equation ; Polyaniline ; Adsorption ; Nanofiber ; Langmuir adsorption model ; Materials science ; Polyaniline nanofibers ; Langmuir ; BET theory ; Chemical engineering ; Specific surface area ; Nuclear chemistry ; Polymerization ; Chemistry ; Organic chemistry ; Nanotechnology ; Composite material ; Polymer Conducting polymers and applications ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal
Authors: Enrique Blázquez‐Blázquez | Ernesto Pérez | Vicente Lorenzo | María L. Cerrada
Source: Polymers (article, 2019-11-13)
Field: Materials Science — Biomaterials

Crystalline Characteristics and Their Influence in the Mechanical Performance in Poly(ε-Caprolactone) / High Density Polyethylene Blends Blends of poly(ε-caprolactone) (PCL) and high-density polyethylene (HDPE) have been prepared at different compositions in order to assess the effect of HDPE on gas transport and mechanical behaviors of PCL. Previous to this evaluation, a complete morphological, structural, and thermal characterization were performed using techniques, including SEM, contact angle, FTIR, differential scanning calorimetry, and X-ray diffraction with synchrotron radiation at small and wide angles. Low HDPE incorporations allow interactions to be established at interfaces in the amorphous regions and the enhancement of the mechanical performance. Consequently, the addition of a small amount of HDPE (ranging from 5 to 10 wt%) appears to be appropriate in certain bio-applications where a higher mechanical behavior is required. High-density polyethylene ; Differential scanning calorimetry ; Materials science ; Caprolactone ; Polyethylene ; Composite material ; Amorphous solid ; Contact angle ; Fourier transform infrared spectroscopy ; Polyoxymethylene ; Polymer ; Chemical engineering ; Copolymer ; Chemistry ; Organic chemistry ; Thermodynamics biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Polymer crystallization and properties