jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marwah M. M. Madkhali | Conor D. Rankine | Thomas J. Penfold
Source: Molecules (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

The Role of Structural Representation in the Performance of a Deep Neural Network for X-ray Spectroscopy An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space-the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC)-we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations. XANES ; Representation (politics) ; Artificial neural network ; Absorption (acoustics) ; Computer science ; Matrix (chemical analysis) ; Spectroscopy ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Algorithm ; Statistical physics ; Mathematics ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Marcos F. Calegari Andrade | Annabella Selloni
Source: Physical Review Materials (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Structure of disordered <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>TiO</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> phases from <i>ab initio</i> based deep neural network simulations Amorphous ${\mathrm{TiO}}_{2}$ (a-${\mathrm{TiO}}_{2}$) is widely used in many fields, ranging from photoelectrochemistry to bioengineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered ${\mathrm{TiO}}_{2}$ with molecular dynamics. Our DP reproduces the structural properties of all 11 ${\mathrm{TiO}}_{2}$ crystalline phases, predicts the densities and structure factors of molten and amorphous ${\mathrm{TiO}}_{2}$ with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. It can be extended to model additional structures and compositions, and can be thus of great value in the study of ${\mathrm{TiO}}_{2}$-based nanomaterials. Amorphous solid ; Materials science ; Ab initio ; Machine learning ; Crystallography ; Physics ; Computer science ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Kohei Shimamura | Yusuke Takeshita | Shogo Fukushima | Akihide Koura | Fuyuki Shimojo
Source: The Journal of Chemical Physics (article, 2020-12-15)
Field: Materials Science — Materials Chemistry

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-Ag<sub>2</sub>Se, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of Ag<sub>2</sub>Se to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated. We clarified that using a rigorous heat flux formula for the ANN potential, even for highly ionic α-Ag<sub>2</sub>Se, the resulting thermal conductivity was reasonably consistent with the reference value without explicitly considering Coulomb interaction. It was found that ANN training including the virial term played an important role in reducing the dependency of thermal conductivity on the initial values of the weight parameters of the ANN. Thermal conductivity ; Artificial neural network ; Conductor ; Interatomic potential ; Coulomb ; Heat flux ; Materials science ; Ionic conductivity ; Range (aeronautics) ; Thermodynamics ; Computer science ; Statistical physics ; Molecular dynamics ; Machine learning ; Artificial intelligence ; Chemistry ; Physics ; Electron ; Computational chemistry ; Heat transfer ; Physical chemistry ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: M Hodapp | A Shapeev
Source: Machine Learning Science and Technology (article, 2020-07-07)
Field: Materials Science — Materials Chemistry

In operando active learning of interatomic interaction during large-scale simulations Abstract A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with tens to hundreds of atoms this can be solved by using active learning which is able to select atomic configurations on which a potential attempts extrapolation and add them to the ab initio -computed training set. In this sense an active learning algorithm can be viewed as an on-the-fly interpolation of an ab initio model. For large-scale problems, possibly involving tens of thousands of atoms, this is not feasible because one cannot afford even a single density functional theory (DFT) computation with such a large number of atoms. This work marks a new milestone toward fully automatic ab initio -accurate large-scale atomistic simulations. We develop an active learning algorithm that identifies local subregions of the simulation region where the potential extrapolates. Then the algorithm constructs periodic configurations out of these local, non-periodic subregions, sufficiently small to be computable with plane-wave DFT codes, in order to obtain accurate ab initio energies. We benchmark our algorithm on the problem of screw dislocation motion in bcc tungsten and show that our algorithm reaches ab initio accuracy, down to typical magnitudes of numerical noise in DFT codes. We show that our algorithm reproduces material properties such as core structure, Peierls barrier, and Peierls stress. This unleashes new capabilities for computational materials science toward applications which have currently been out of scope if approached solely by ab initio methods. Extrapolation ; Interpolation (computer graphics) ; Interatomic potential ; Benchmark (surveying) ; Computation ; Active learning (machine learning) ; Noise (video) ; Work (physics) ; Coulomb Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Electron Microscopy Techniques and Applications
Authors: João Basso | Maria Mendes | Jéssica Silva | Tânia Cova | Edurne Luque-Michel | Andreia F. Jorge | Santiago Grijalvo | Lídia Gonçalves | Ramón Eritja | María J. Blanco‐Prieto | António J. Almeida | Alberto A. C. C. Pais | Carla Vitorino
Source: International Journal of Pharmaceutics (article, 2020-11-18)
Field: Materials Science — Biomaterials

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms Cytotoxicity ; Cationic polymerization ; Zeta potential ; Nanoparticle ; Chemistry ; Ligand (biochemistry) ; Glioblastoma ; Biophysics ; Nanotechnology ; Combinatorial chemistry ; Computer science ; Materials science ; Biochemistry ; In vitro ; Organic chemistry ; Biology ; Cancer research ; Receptor Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; Nanoplatforms for cancer theranostics
Authors: Chun-Yen Liu | Shijia Zhang | Daniel Martínez | Meng Li | Thomas P. Senftle
Source: npj Computational Materials (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports Abstract Metal/oxide interactions mediated by charge transfer influence reactivity and stability in numerous heterogeneous catalysts. In this work, we use density functional theory (DFT) and statistical learning (SL) to derive models for predicting how the adsorption strength of metal atoms on MgO(100) surfaces can be enhanced by modifications of the support. MgO(100) in its pristine form is relatively unreactive, and thus is ideal for examining ways in which its electronic interactions with metals can be enhanced, tuned, and controlled. We find that the charge transfer characteristics of MgO are readily modified either by adsorbates on the surface (e.g., H, OH, F, and NO 2 ) or dopants in the oxide lattice (e.g., Li, Na, B, and Al). We use SL methods (i.e., LASSO, Horseshoe prior, and Dirichlet–Laplace prior) that are trained against DFT data to identify physical descriptors for predicting how the adsorption energy of metal atoms will change in response to support modification. These SL-derived feature selection tools are used to screen through more than one million candidate descriptors that are generated from simple chemical properties of the adsorbed metals, MgO, dopants, and adsorbates. Among the tested SL tools, we demonstrate that Dirichlet–Laplace prior predicts metal adsorption energies on MgO most accurately, while also identifying descriptors that are most transferable to chemically similar oxides, such as CaO, BaO, and ZnO. Dopant ; Density functional theory ; Adsorption ; Oxide ; Metal ; Materials science ; Curse of dimensionality ; Charge (physics) ; Chemical physics ; Computational chemistry ; Physical chemistry ; Chemistry ; Doping ; Machine learning ; Physics ; Computer science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Simon Carter | Robert Clough | Andrew Fisher | Bridget Gibson | Ben Russell | Julia Waack
Source: Journal of Analytical Atomic Spectrometry (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials This review covers advances in the analysis of advanced materials, metals, fuels and lubricants, nanostructures, ceramics, refractories, organic and inorganic chemicals, catalysts and nuclear materials by a range of techniques including X-ray, ICP, LIBS, mass spectrometry, synchrotron-based techniques, plus non-destructive and ablation surface techniques. Mass spectrometry ; Materials science ; Synchrotron ; Ceramic ; Nanotechnology ; Chemistry ; Metallurgy ; Chromatography ; Physics Analytical chemistry methods development ; Ion-surface interactions and analysis ; Laser-induced spectroscopy and plasma
Authors: Srihari Sundar | Veera Sundararaghavan
Source: Materials Today Communications (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Database development and exploration of process–microstructure relationships using variational autoencoders Autoencoder ; Representation (politics) ; Database ; Process (computing) ; Computer science ; Development (topology) ; Texture (cosmology) ; Space (punctuation) ; Sequence (biology) ; Data mining ; Compression (physics) ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Materials science ; Mathematics ; Artificial neural network ; Image (mathematics) Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Manufacturing Process and Optimization
Authors: Sara Abalde‐Cela | Rita Rebelo | Lei Wu | Ana I. Barbosa | Laura Rodríguez‐Lorenzo | Krishna Kant | Rui L. Reis | Vítor M. Correlo | Lorena Diéguez
Source: Materials Advances (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A SERS-based 3D nanobiosensor: towards cell metabolite monitoring A biocompatible surface enhanced Raman spectroscopy (SERS) hybrid material is developed by embedding gold-based nanostructures into gellan gum “sponge-like” hydrogels, which is used for biochemical detection of disease associated cell metabolites. Metabolite ; Surface-enhanced Raman spectroscopy ; Gellan gum ; Nanotechnology ; Biocompatible material ; Chemistry ; Biosensor ; Raman spectroscopy ; Materials science ; Raman scattering ; Biochemistry ; Biomedical engineering Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Ankit Nalin Mehta | Nicolas Gauquelin | Magnus Nord | Andrey Orekhov | H. Bender | Dorin Cerbu | Johan Verbeeck | Wilfried Vandervorst
Source: Nanotechnology (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

Unravelling stacking order in epitaxial bilayer MX <sub>2</sub> using 4D-STEM with unsupervised learning Following an extensive investigation of various monolayer transition metal dichalcogenides (MX<sub>2</sub>), research interest has expanded to include multilayer systems. In bilayer MX<sub>2</sub>, the stacking order strongly impacts the local band structure as it dictates the local confinement and symmetry. Determination of stacking order in multilayer MX<sub>2</sub> domains usually relies on prior knowledge of in-plane orientations of constituent layers. This is only feasible in case of growth resulting in well-defined triangular domains and not useful in-case of closed layers with hexagonal or irregularly shaped islands. Stacking order can be discerned in the reciprocal space by measuring changes in diffraction peak intensities. Advances in detector technology allow fast acquisition of high-quality four-dimensional datasets which can later be processed to extract useful information such as thickness, orientation, twist and strain. Here, we use 4D scanning transmission electron microscopy combined with multislice diffraction simulations to unravel stacking order in epitaxially grown bilayer MoS<sub>2</sub>. Machine learning based data segmentation is employed to obtain useful statistics on grain orientation of monolayer and stacking in bilayer MoS<sub>2</sub>. Stacking ; Materials science ; Bilayer ; Monolayer ; Diffraction ; Crystallography ; Orientation (vector space) ; Epitaxy ; Condensed matter physics ; Zigzag ; Symmetry (geometry) ; Chemical physics ; Nanotechnology ; Optics ; Layer (electronics) ; Geometry ; Physics ; Membrane ; Nuclear magnetic resonance ; Mathematics ; Chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Weiwei Wang | Junting Fan | Guang Zhu | Jing Wang | Yumei Qian | Hongxia Li | Jianming Ju | Lingling Shan
Source: International Journal of Nanomedicine (article, 2020-04-01)
Field: Materials Science — Biomaterials

&lt;p&gt;Targeted Prodrug-Based Self-Assembled Nanoparticles for Cancer Therapy&lt;/p&gt; This work demonstrates a new combination of nanomaterials for chemotherapy and may promote prodrug-based DDS clinical applications in the future. Prodrug ; Paclitaxel ; In vivo ; Drug delivery ; Pharmacology ; Chemistry ; In vitro ; Fluorescein isothiocyanate ; Targeted drug delivery ; Chemotherapy ; Biochemistry ; Medicine ; Fluorescence ; Biology ; Organic chemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Samuel A. Bradley | Jie Zhang | Michael K. Jensen
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-10-23)
Field: Chemistry — Organic Chemistry

Deploying Microbial Synthesis for Halogenating and Diversifying Medicinal Alkaloid Scaffolds Plants produce some of the most potent therapeutics and have been used for thousands of years to treat human diseases. Today, many medicinal natural products are still extracted from source plants at scale as their complexity precludes total synthesis from bulk chemicals. However, extraction from plants can be an unreliable and low-yielding source for human therapeutics, making the supply chain for some of these life-saving medicines expensive and unstable. There has therefore been significant interest in refactoring these plant pathways in genetically tractable microbes, which grow more reliably and where the plant pathways can be more easily engineered to improve the titer, rate and yield of medicinal natural products. In addition, refactoring plant biosynthetic pathways in microbes also offers the possibility to explore new-to-nature chemistry more systematically, and thereby help expand the chemical space that can be probed for drugs as well as enable the study of pharmacological properties of such new-to-nature chemistry. This perspective will review the recent progress toward heterologous production of plant medicinal alkaloids in microbial systems. In particular, we focus on the refactoring of halogenated alkaloids in yeast, which has created an unprecedented opportunity for biosynthesis of previously inaccessible new-to-nature variants of the natural alkaloid scaffolds. Alkaloid ; Traditional medicine ; Biotechnology ; Chemistry ; Biology ; Botany ; Medicine Chemical synthesis and alkaloids ; Alkaloids: synthesis and pharmacology ; Chemical Reactions and Isotopes
Authors: Dongdong Xiao | Lin Gu
Source: Nano Select (article, 2020-06-28)
Field: Materials Science — Materials Chemistry

Origin of functionality for functional materials at atomic scale Abstract The physical properties of functional materials are closely related to the intrinsic symmetry and applied fields, wherein symmetry breaking under external fields generates functionalities for functional materials that underlie various technological applications. Understanding the origin of functionalities requires us to determine the precise atomic structure and electronic configurations of functional materials in the broken‐symmetry states, thereby establishing the direct correlation between the structural details and functional properties of materials. The recent advances in aberration‐corrected transmission electron microscopy (TEM) have enabled real‐space imaging and spectroscopy at sub‐ångström resolution with extraordinary sensitivity to structure, chemistry, and bonding, providing great opportunities to bridge the gap between structure and functionality. Here we present case studies highlighting the utility of advanced TEM in understanding the origin of functionality for functional materials, alongside these examples is a discussion of the physical underpinnings of specific functional properties. We then conclude with a personal perspective on future directions as well as fundamental challenges. Nanotechnology ; Characterization (materials science) ; Atomic units ; Space (punctuation) ; Symmetry (geometry) ; Symmetry breaking ; Scanning transmission electron microscopy ; Materials science ; Chemical physics ; Computer science ; Physics ; Transmission electron microscopy ; Quantum mechanics ; Mathematics Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Magnetic and transport properties of perovskites and related materials
Authors: Xiaoyan Wang | Zhiwei Li | Wenjun Wang | Jiawei Wang
Source: Ciência Rural (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops. Hyperspectral imaging ; Partial least squares regression ; Mathematics ; Mean squared error ; Correlation coefficient ; Content (measure theory) ; Pattern recognition (psychology) ; Normalized Difference Vegetation Index ; Artificial intelligence ; Support vector machine ; Remote sensing ; Biological system ; Computer science ; Leaf area index ; Statistics ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: S. Ghadami | E. Taheri-Nassaj | Hamid Reza Baharvandi | Farzin Ghadami
Source: Scientific Reports (article, 2020-10-06)
Field: Materials Science — Ceramics and Composites

Effect of in situ VSi2 and SiC phases on the sintering behavior and the mechanical properties of HfB2-based composites In situ HfB<sub>2</sub>-SiC-VSi<sub>2</sub> composite was fabricated by reactive pressureless sintering at the temperature of 2150 °C for 4 h under a vacuum atmosphere. In situ SiC and VSi<sub>2</sub> reinforcements were formed using VC and Si powders as starting materials according to the following reaction: VC + 3Si = SiC + VSi<sub>2</sub>. Microstructural studies and thermodynamic calculations revealed that in situ VSi<sub>2</sub> and SiC phases were mostly formed and homogeneously distributed in HfB<sub>2</sub> skeleton. The results showed that the density of in situ HfB<sub>2</sub>-SiC-VSi<sub>2</sub> composite was 98%. Besides, the mechanical properties of the composite were effectively enhanced by the formation of in situ second phases. The Vickers hardness and the fracture toughness of the composite reached 20.1 GPa and 5.8 MPa m<sup>-1/2</sup>, respectively. In situ ; Materials science ; Composite number ; Sintering ; Fracture toughness ; Composite material ; Pressureless sintering ; Vickers hardness test ; Toughness ; Microstructure ; Chemistry Advanced ceramic materials synthesis ; Advanced materials and composites ; MXene and MAX Phase Materials