jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maximilian AH Jakobs | Andrea Dimitracopoulos | Kristian Franze
Source: eLife (article, 2019-08-12)
Field: Chemistry — Spectroscopy

KymoButler, a deep learning software for automated kymograph analysis Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based 'one-click' application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis. Computer science ; Software ; Artificial intelligence ; Adaptation (eye) ; Deep learning ; Machine learning ; Programming language ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Zhaocheng Liu | Dayu Zhu | Kyu‐Tae Lee | Andrew S. Kim | Lakshmi Raju | Wenshan Cai
Source: Advanced Materials (article, 2019-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, metamolecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of metamolecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multielement systems impede an effective strategy for the design and optimization of metamolecules. Here, a hybrid artificial-intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of metamolecules in metasurfaces is proposed. The framework breaks the design of the metamolecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. The proposed framework is leveraged to design metallic metamolecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner. Computer science ; Materials science ; Controllability ; Nanotechnology ; Artificial intelligence ; Mathematics Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Photonic Crystals and Applications
Authors: Tian Xie | Arthur France‐Lanord | Yanming Wang | Yang Shao‐Horn | Jeffrey C. Grossman
Source: Nature Communications (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems. Molecular dynamics ; Atomic units ; Computer science ; Scale (ratio) ; Dynamical systems theory ; Lithium (medication) ; Graph ; Nanotechnology ; Statistical physics ; Chemical physics ; Theoretical computer science ; Materials science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Ernest Teye | Charles Lloyd Yeboah Amuah | Terry McGrath | Christopher T. Elliott
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2019-03-26)
Field: Chemistry — Analytical Chemistry

Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics Principal component analysis ; Chemometrics ; Preprocessor ; Chemistry ; Pattern recognition (psychology) ; Training set ; Data set ; Statistics ; Artificial intelligence ; Mathematics ; Computer science ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Lamyaa M. Almehmadi | Stephanie M. Curley | Natalya Tokranova | Scott A. Tenenbaum | Igor K. Lednev
Source: Scientific Reports (article, 2019-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface Enhanced Raman Spectroscopy for Single Molecule Protein Detection A two-step process of protein detection at a single molecule level using SERS was developed as a proof-of-concept platform for medical diagnostics. First, a protein molecule was bound to a linker in the bulk solution and then this adduct was chemically reacted with the SERS substrate. Traut's Reagent (TR) was used to thiolate Bovine serum albumin (BSA) in solution followed by chemical cross linking to a gold surface through a sulfhydryl group. A Glycine-TR adduct was used as a control sample to identify the protein contribution to the SER spectra. Gold SERS substrates were manufactured by electrochemical deposition. Solutions at an ultralow concentration were used for attaching the TR adducts to the SERS substrate. Samples showed the typical behavior of a single molecule SERS including spectral fluctuations, blinking and Raman signal being generated from only selected points on the substrate. The fluctuating SER spectra were examined using Principle Component Analysis. This unsupervised statistics allowed for the selecting of spectral contribution from protein moiety indicating that the method was capable of detecting a single protein molecule. Thus we have demonstrated, that the developed two-step methodology has the potential as a new platform for medical diagnostics. Adduct ; Substrate (aquarium) ; Molecule ; Raman spectroscopy ; Moiety ; Chemistry ; Surface-enhanced Raman spectroscopy ; Bovine serum albumin ; Reagent ; Analytical Chemistry (journal) ; Combinatorial chemistry ; Raman scattering ; Stereochemistry ; Chromatography ; Physical chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Shuang Zhu | Linji Gong | Yijian Li | Haiwei Xu | Zhanjun Gu | Yuliang Zhao
Source: Advanced Science (review, 2019-06-20)
Field: Materials Science — Materials Chemistry

Safety Assessment of Nanomaterials to Eyes: An Important but Neglected Issue The production and application of nanomaterials have grown tremendously during last few decades. The widespread exposure of nanoparticles to the public is provoking great concerns regarding their toxicity to the human body. However, in comparison with the extensive studies carried out to examine nanoparticle toxicity to the human body/organs, one especially vulnerable organ, the eye, is always neglected. Although it is a small part of the body, 90% of outside information is obtained via the ocular system. In addition, eyes usually directly interact with the surrounding environment, which may get severer damage from toxic nanoparticles compared to inner organs. Therefore, the study of assessing the potential nanoparticle toxicity to the eyes is of great importance. Here, the recent advance of some representative manufactured nanomaterials on ocular toxicity is summarized. First, a brief introduction of ocular anatomy and disorders related to particulate matter exposure is presented. Following, the factors that may influence toxicity of nanoparticles to the eye are emphasized. Next, the studies of representative manufactured nanoparticles on eye toxicity are summarized and classified. Finally, the limitations that are associated with current nanoparticle-eye toxicity research are proposed. Nanomaterials ; Nanotechnology ; Risk analysis (engineering) ; Materials science ; Medicine Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Mohammad Mahbubul Hassan | C. Carr
Source: Journal of Advanced Research (review, 2019-01-31)
Field: Materials Science — Surfaces, Coatings and Films

A review of the sustainable methods in imparting shrink resistance to wool fabrics Wool fiber is a natural protein fiber, which is used for the manufacturing of apparels, and floorcoverings because of its excellent fire retardancy, stain-resistance, antistatic and odor control properties along with exceptional warmth and resilience. However, wool fiber has several serious demerits, such as garments made of wool fibers extensively shrink during their laundering. To overcome this problem, wool fibers, especially those are used in apparel, are frequently shrink-resist treated to make them machine-washable. A wide range of treatments including oxidative, enzymatic, radiation, polymeric coatings, sol-gel coatings, and plasma treatments have been investigated to make wool fiber shrink-resistant. In this review, the mechanisms of wool fiber shrinkage, the research carried out until recently to make wool fiber shrink-resistant, and the current status of the sustainable alternatives developed, have been compiled and presented. The various methods investigated have been critically discussed with their merits and demerits, shrink-resist performance, and their shrink-resistance mechanisms. The chemistry and synthesis of various polymers used for the shrink-resistance and their reactions with wool fiber have been outlined. This review also includes the current challenges to make shrink-resist treatments green and sustainable, and also the future directions to meet these challenges. Some of the treatments investigated may affect the biodegradability of wool fibers, especially those are based on coating with synthetic polymers. A sustainable alternative polymeric coating based on sustainably produced polymeric resins, especially bio-based resins, needs to be developed so that the future treatments become sustainable. Wool ; Materials science ; Resist ; Natural fiber ; Textile ; Fiber ; Coating ; Composite material ; Synthetic fiber ; Resilience (materials science) ; Pulp and paper industry ; Polymer science ; Engineering ; Layer (electronics) Surface Modification and Superhydrophobicity ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution
Authors: Alberto Fabrizio | Andrea Grisafi | Benjamin Meyer | Michele Ceriotti | Clémence Corminbœuf
Source: Chemical Science (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Electron density learning of non-covalent systems Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density <i>ρ</i>( <b><i>r</i></b> ) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying <i>ρ</i>( <b><i>r</i></b> ) in a diverse ensemble of sidechain-sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides. Covalent bond ; Electron ; Electron density ; Chemistry ; Chemical physics ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry Crystallography and molecular interactions ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Liyan Wang | Jinhua Li | Jianan Wang | Xiaotong Guo | Xiaoyan Wang | Jaebum Choo | Lingxin Chen
Source: Journal of Colloid and Interface Science (article, 2019-01-22)
Field: Chemistry — Analytical Chemistry

Green multi-functional monomer based ion imprinted polymers for selective removal of copper ions from aqueous solution Adsorption ; Aqueous solution ; Chemistry ; Metal ions in aqueous solution ; Selectivity ; Langmuir adsorption model ; Polymer ; Indigo carmine ; Selective adsorption ; Copper ; Inorganic chemistry ; Metal ; Nuclear chemistry ; Catalysis ; Organic chemistry Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Molecular Sensors and Ion Detection
Authors: Yang Hong | Bo Hou | Hengle Jiang | Jingchao Zhang
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2019-11-20)
Field: Materials Science — Materials Chemistry

Machine learning and artificial neural network accelerated computational discoveries in materials science Abstract Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state‐of‐the‐art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML‐based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized. This article is categorized under: Computer and Information Science &gt; Chemoinformatics. Structure and Mechanism &gt; Computational Materials Science. Computer and Information Science &gt; Computer Algorithms and Programming. Software &gt; Molecular Modeling. Computer science ; Artificial intelligence ; Cheminformatics ; Toolbox ; Artificial neural network ; Machine learning ; Computational model ; Generative grammar ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications
Authors: Weiran Zheng | Chui‐Shan Tsang | Lawrence Yoon Suk Lee | Kwok‐Yin Wong
Source: Materials Today Chemistry (article, 2019-01-15)
Field: Materials Science — Materials Chemistry

Two-dimensional metal-organic framework and covalent-organic framework: synthesis and their energy-related applications Nanotechnology ; Metal-organic framework ; Materials science ; Covalent bond ; Chemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Marco Eckhoff | Jörg Behler
Source: Journal of Chemical Theory and Computation (article, 2019-05-15)
Field: Materials Science — Materials Chemistry

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5 The development of first-principles-quality reactive atomistic potentials for organic-inorganic hybrid materials is still a substantial challenge because of the very different physics of the atomic interactions-from covalent via ionic bonding to dispersion-that have to be described in an accurate and balanced way. In this work we used a prototypical metal-organic framework, MOF-5, as a benchmark case to investigate the applicability of high-dimensional neural network potentials (HDNNPs) to this class of materials. In HDNNPs, which belong to the class of machine learning potentials, the energy is constructed as a sum of environment-dependent atomic energy contributions. We demonstrate that by the use of this approach it is possible to obtain a high-quality potential for the periodic MOF-5 crystal using density functional theory (DFT) reference calculations of small molecular fragments only. The resulting HDNNP, which has a root-mean-square error (RMSE) of 1.6 meV/atom for the energies of molecular fragments not included in the training set, is able to provide the equilibrium lattice constant of the bulk MOF-5 structure with an error of about 0.1% relative to DFT, and also, the negative thermal expansion behavior is accurately predicted. The total energy RMSE of periodic structures that are completely absent in the training set is about 6.5 meV/atom, with errors on the order of 2 meV/atom for energy differences. We show that in contrast to energy differences, achieving a high accuracy for total energies requires careful variation of the stoichiometries of the training structures to avoid energy offsets, as atomic energies are not physical observables. The forces, which have RMSEs of about 94 meV/ a<sub>0</sub> for the molecular fragments and 130 meV/ a<sub>0</sub> for bulk structures not included in the training set, are insensitive to such offsets. Therefore, forces, which are the relevant properties for molecular dynamics simulations, provide a realistic estimate of the accuracy of atomistic potentials. Atom (system on chip) ; Ionic bonding ; Density functional theory ; Artificial neural network ; Mean squared error ; Lattice energy ; Binding energy ; Lattice constant ; Statistical physics ; Materials science ; Chemistry ; Chemical physics ; Crystal structure ; Physics ; Computational chemistry ; Atomic physics ; Computer science ; Mathematics ; Quantum mechanics ; Crystallography ; Machine learning ; Diffraction ; Ion Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Henry Chan | Badri Narayanan | Mathew J. Cherukara | Fatih G. Sen | Kiran Sasikumar | Stephen K. Gray | Maria K. Y. Chan | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry C (article, 2019-01-03)
Field: Materials Science — Materials Chemistry

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials. Molecular dynamics ; Computer science ; Scalability ; Characterization (materials science) ; Interatomic potential ; Flexibility (engineering) ; Atom (system on chip) ; Statistical physics ; Nanotechnology ; Physics ; Materials science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Mass Spectrometry Techniques and Applications