jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Feiler | Di Mei | Bahram Vaghefinazari | Tim Würger | Robert H. Meißner | Bérengère Luthringer-Feyerabend | David A. Winkler | Mikhail L. Zheludkevich | Sviatlana V. Lamaka
Source: Corrosion Science (article, 2019-09-27)
Field: Materials Science — Materials Chemistry

In silico screening of modulators of magnesium dissolution The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders currently used experimental discovery methods time- and resource-consuming. Fortunately, emerging computer-assisted methods can explore large areas of chemical space with less effort. Here we show how density functional theory calculations and machine learning methods can work synergistically to generate robust and predictive models that recapitulate experimentally-derived corrosion inhibition efficiencies of small organic compounds for pure magnesium. We further validate our methods by predicting a priori the corrosion modulation properties of seven hitherto untested small molecules and confirm the prediction in subsequent experiments. Dissolution ; Chemical space ; In silico ; Corrosion ; Magnesium ; Small molecule ; Density functional theory ; Materials science ; Biological system ; Computer science ; Modulation (music) ; A priori and a posteriori ; Molecule ; Nanotechnology ; Biochemical engineering ; Chemistry ; Drug discovery ; Computational chemistry ; Physics ; Metallurgy ; Engineering ; Biology ; Physical chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Metal and Thin Film Mechanics
Authors: Carl S. Adorf | Timothy C. Moore | Yannah J. U. Melle | Sharon C. Glotzer
Source: The Journal of Physical Chemistry B (article, 2019-12-08)
Field: Materials Science — Materials Chemistry

Analysis of Self-Assembly Pathways with Unsupervised Machine Learning Algorithms Colloidal and nanoparticle systems display a rich and exciting phase behavior including the self-assembly of highly complex crystal structures. Nucleation and growth pathways toward crystallization have been studied both computationally and experimentally, but the mechanisms for the formation of the precritical nucleus and consequent crystal growth are yet to be fully understood. Recent advances in the application of machine learning algorithms applied to many-particle systems have led to significant breakthroughs in the ability for high-throughput analysis of phase transitions and the identification of crystal structures. We build upon these techniques to identify and analyze pathways for nucleation and growth in supercooled liquids of colloidal systems modeled with isotropic pair potentials. Our study involves the development of unsupervised machine learning models trained on spherical-harmonics-based descriptors. These models allow us to determine clusters of local environments that are present prior to and during crystallization. We analyze these environments to identify prevalent motifs and local order within the supercooled liquid prior to formation of the critical nucleus. Nucleation ; Crystallization ; Supercooling ; Artificial intelligence ; Computer science ; Unsupervised learning ; Crystal growth ; Machine learning ; Biological system ; Materials science ; Nanotechnology ; Chemical physics ; Physics ; Chemistry ; Crystallography ; Thermodynamics Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Phase Equilibria and Thermodynamics
Authors: Nahdia Majeed | Maria Saladina | Michał Krompiec | Steve Greedy | Carsten Deibel | Roderick C. I. MacKenzie
Source: Advanced Functional Materials (article, 2019-12-15)
Field: Materials Science — Materials Chemistry

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells Abstract There is currently a worldwide effort to develop materials for solar energy harvesting which are efficient and cost effective, and do not emit significant levels of CO 2 during manufacture. When a researcher fabricates a novel device from a novel material system, it often takes many weeks of experimental effort and data analysis to understand why any given device/material combination produces an efficient or poorly optimized cell. It therefore takes the community tens of years to transform a promising material system to a fully optimized cell ready for production (perovskites are a contemporary example). Herein, developed is a new and rapid approach to understanding device/material performance, which uses a combination of machine learning, device modeling, and experiment. Providing a set of electrical device parameters (charge carrier mobilities, recombination rates, trap densities, etc.) in a matter of seconds thus offers a fast way to directly link fabrication conditions to device/material performance, pointing a way to further and more rapid optimization of light harvesting devices. The method is demonstrated by using it to understand annealing temperature and surfactant choice and in terms of charge carrier dynamics in organic solar cells made from the P3HT:PCBM, PBTZT‐stat‐BDTT‐8:PCBM, and PTB7:PCBM material systems. Materials science ; Fabrication ; Charge carrier ; Solar cell ; Annealing (glass) ; Organic solar cell ; Nanotechnology ; Simulated annealing ; Computer science ; Optoelectronics ; Process engineering ; Engineering physics ; Polymer ; Composite material ; Machine learning Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Guangfei Li | Kai Chen | Dan You | Mingyu Xia | Wen Li | Suna Fan | Renjie Chai | Yaopeng Zhang | Huawei Li | Shan Sun
Source: Frontiers in Bioengineering and Biotechnology (article, 2019-08-06)
Field: Materials Science — Biomaterials

Laminin-Coated Electrospun Regenerated Silk Fibroin Mats Promote Neural Progenitor Cell Proliferation, Differentiation, and Survival in vitro Neural progenitor cell (NPC) transplantation is a promising technique for central nervous system (CNS) reconstruction and regeneration. Biomaterial scaffolds, frameworks, and platforms can support NPC proliferation and differentiation <i>in vitro</i> as well as serve as a temporary extracellular matrix after transplantation. However, further applications of biomaterials require improved biological attributes. Silk fibroin (SF), which is produced by <i>Bombyx mori</i>, is a widely used and studied protein polymer for biomaterial application. Here, we prepared aligned and random eletrospun regenerated SF (RSF) scaffolds, and evaluated their impact on the growth of NPCs. First, we isolated NPCs and then cultured them on either laminin-coated RSF mats or conventional laminin-coated coverslips for cell assays. We found that aligned and random RSF led to increases in NPC proliferation of 143.8 ± 13.3% and 156.3 ± 14.7%, respectively, compared to controls. Next, we investigated neuron differentiation and found that the aligned and the random RSF led to increases in increase in neuron differentiation of about 93.2 ± 6.4%, and 3167.1 ± 4.8%, respectively, compared to controls. Furthermore, we measured the survival of NPCs and found that RSF promoted NPC survival, and found there was no difference among those three groups. Finally, signaling pathways in cells cultured on RSF mats were studied for their contributions in neural cell differentiation. Our results indicate that RSF mats provide a functional microenvironment and represent a useful scaffold for the development of new strategies in neural engineering research. Fibroin ; Laminin ; Cell biology ; Progenitor cell ; Extracellular matrix ; Transplantation ; Neural stem cell ; Biomaterial ; Bombyx mori ; Cell growth ; Chemistry ; Tissue engineering ; Neural tissue engineering ; In vitro ; Stem cell ; SILK ; Biomedical engineering ; Regeneration (biology) ; Biology ; Materials science ; Nanotechnology ; Biochemistry ; Medicine ; Internal medicine ; Composite material Silk-based biomaterials and applications ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Seyedeh‐Arefeh Safavi‐Mirmahalleh | Mehdi Salami‐Kalajahi | Hossein Roghani‐Mamaqani
Source: Cellulose (article, 2019-05-09)
Field: Materials Science — Biomaterials

Effect of surface chemistry and content of nanocrystalline cellulose on removal of methylene blue from wastewater by poly(acrylic acid)/nanocrystalline cellulose nanocomposite hydrogels Thermogravimetric analysis ; Materials science ; Nanocomposite ; Zeta potential ; Chemical engineering ; Self-healing hydrogels ; Acrylic acid ; Swelling capacity ; Fourier transform infrared spectroscopy ; Adsorption ; Nuclear chemistry ; Aqueous solution ; Methylene blue ; Polymer chemistry ; Swelling ; Nanoparticle ; Organic chemistry ; Composite material ; Polymer ; Chemistry ; Copolymer ; Nanotechnology ; Photocatalysis ; Catalysis Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal ; Hydrogels: synthesis, properties, applications
Authors: Hui Luo | Νικόλαος Παπαϊωάννου | Enrico Salvadori | Maxie M. Roessler | Gereon Ploenes | Ernst R. H. van Eck | Liviu C. Tănase | Jingyu Feng | Yiwei Sun | Yan Yang | Mohsen Danaie | Ana Belen Jorge | Andrei Sapelkin | James R. Durrant | Stoichko Dimitrov | Maria‐Magdalena Titirici
Source: ChemSusChem (article, 2019-08-15)
Field: Materials Science — Materials Chemistry

Manipulating the Optical Properties of Carbon Dots by Fine‐Tuning their Structural Features As a new class of sustainable carbon material, "carbon dots" is an umbrella term covering many types of materials. Herein, a broad range of techniques was used to develop the understanding of hydrothermally synthesized carbon dots, and it is shown how fine-tuning the structural features by simple reduction/oxidation reactions can drastically affect their excited-state properties. Structural and spectroscopic studies found that photoluminescence originates from direct excitation of localized fluorophores involving oxygen functional groups, whereas excitation at graphene-like features leads to ultrafast phonon-assisted relaxation and largely quenches the fluorescent quantum yields. This is arguably the first study to identify the dynamics of photoluminescence including Stokes shift and allow the relaxation pathways in these carbon dots to be fully resolved. This comprehensive investigation sheds light on how understanding the excited-state relaxation processes in different carbon structures is crucial for tuning the optical properties for any potential commercial applications. Carbon fibers ; Nanotechnology ; Quantum dot ; Materials science ; Composite material Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Marion Kieffer | Ana M. García | Cally J. E. Haynes | Slavko Kralj | Daniel Iglesias | Jonathan R. Nitschke | Silvia Marchesan
Source: Angewandte Chemie International Edition (article, 2019-03-28)
Field: Materials Science — Biomaterials

Embedding and Positioning of Two Fe<sup>II</sup><sub>4</sub>L<sub>4</sub> Cages in Supramolecular Tripeptide Gels for Selective Chemical Segregation An unreported d,l-tripeptide self-assembled into gels that embedded Fe<sup>II</sup><sub>4</sub> L<sub>4</sub> metal-organic cages to form materials that were characterized by TEM, EDX, Raman spectroscopy, rheometry, UV/Vis and NMR spectroscopy, and circular dichroism. The cage type and concentration modulated gel viscoelasticity, and thus the diffusion rate of molecular guests through the nanostructured matrix, as gauged by <sup>19</sup> F and <sup>1</sup> H NMR spectroscopy. When two different cages were added to spatially separated gel layers, the gel-cage composite material enabled the spatial segregation of a mixture of guests that diffused into the gel. Each cage selectively encapsulated its preferred guest during diffusion. We thus present a new strategy for using nested supramolecular interactions to enable the separation of small molecules. Supramolecular chemistry ; Tripeptide ; Spectroscopy ; Crystallography ; Nuclear magnetic resonance spectroscopy ; Molecule ; Diffusion ; Raman spectroscopy ; Materials science ; Chemistry ; Stereochemistry ; Organic chemistry ; Peptide Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Chemical Synthesis and Analysis
Authors: Jiemei Ou | Zidan Zhou | Zhong Chen | Huijun Tan
Source: International Journal of Molecular Sciences (review, 2019-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical Diagnostic Based on Functionalized Gold Nanoparticles Au nanoparticles (NPs) possess unique physicochemical and optical properties, showing great potential in biomedical applications. Diagnostic spectroscopy utilizing varied Au NPs has become a precision tool of in vitro and in vivo diagnostic for cancer and other specific diseases. In this review, we tried to comprehensively introduce the remarkable optical properties of Au NPs, including localized surfaces plasmon resonance (LSPR), surface-enhanced Raman scattering (SERS), and metal-enhanced fluorescence (MEF). Then, we highlighted the excellent works using Au NPs for optical diagnostic applications. Ultimately, the challenges and future perspective of using Au NPs for optical diagnostic were discussed. Surface plasmon resonance ; Nanotechnology ; Colloidal gold ; Nanoparticle ; Materials science ; Raman scattering ; Plasmon ; Raman spectroscopy ; Fluorescence ; Surface-enhanced Raman spectroscopy ; Optoelectronics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Christoph Staudinger | Johanna Breininger | Ingo Klimant | Sergey M. Borisov
Source: The Analyst (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Near-infrared fluorescent aza-BODIPY dyes for sensing and imaging of pH from the neutral to highly alkaline range New aza-BODIPY pH indicators with spectral properties modulated solely by photoinduced electron transfer (PET) are presented. The pH sensitive hydroxyl group is located in the meta-position of a phenyl substituent with respect to the aza-BODIPY core, which eliminates the conjugation to the chromophore. The new dyes show reversible "on"-"off" fluorescence response upon deprotonation of the receptor but no changes in the absorption spectrum, which is in contrast to state-of-the-art indicators of the aza-BODIPY family. This eliminates potential changes in the efficiency of the inner filter effect and Förster resonance energy transfer (FRET) and makes the new dyes suitable acceptors in light harvesting systems used for ratiometric pH imaging. The introduction of electron-withdrawing or electron-donating groups into the receptor results in a set of indicators suitable for measurements from physiological (pH 7) to very alkaline (pH 13) conditions. The new sensors are particularly promising for monitoring of pH changes in concrete, as was recently shown elsewhere. Fluorescence ; BODIPY ; Chemistry ; Infrared ; Photochemistry ; Range (aeronautics) ; Materials science ; Optics ; Physics Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Polydiacetylene-based materials and applications
Authors: Piyush Tagade | Shashishekar P. Adiga | S. Pandian | Min‐Sik Park | Krishnan S. Hariharan | Subramanya Mayya Kolake
Source: npj Computational Materials (article, 2019-12-20)
Field: Materials Science — Materials Chemistry

Attribute driven inverse materials design using deep learning Bayesian framework Abstract Much of computational materials science has focused on fast and accurate forward predictions of materials properties, for example, given a molecular structure predict its electronic properties. This is achieved with first principles calculations and more recently through machine learning approaches, since the former is computation-intensive and not practical for high-throughput screening. Searching for the right material for any given application, though follows an inverse path—the desired properties are given and the task is to find the right materials. Here we present a deep learning inverse prediction framework, Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS), for efficient and accurate prediction of molecules exhibiting target properties. We apply this framework to the computational design of organic molecules for three applications, organic semiconductors for thin-film transistors, small organic acceptors for solar cells and electrolyte additives with high redox stability. Our method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery. Computer science ; Inverse ; Stability (learning theory) ; Deep learning ; Computation ; Organic semiconductor ; Bayesian optimization ; Bayesian network ; Artificial intelligence ; Machine learning ; Materials science ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Prasanna V. Balachandran
Source: Computational Materials Science (article, 2019-04-08)
Field: Materials Science — Materials Chemistry

Machine learning guided design of functional materials with targeted properties Density functional theory ; Convex hull ; Octahedron ; Curie temperature ; Chemical space ; Space (punctuation) ; Materials science ; Computation ; Ferroelectricity ; Perovskite (structure) ; Crystal structure ; Crystal structure prediction ; Computer science ; Machine learning ; Computational chemistry ; Regular polygon ; Algorithm ; Chemistry ; Physics ; Mathematics ; Condensed matter physics ; Crystallography ; Ferromagnetism ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Miguel A.S. Matos | S.T. Pinho | Vito L. Tagarielli
Source: Scripta Materialia (article, 2019-03-22)
Field: Materials Science — Materials Chemistry

Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network Carbon nanotube ; Materials science ; Percolation (cognitive psychology) ; Percolation threshold ; Artificial neural network ; Composite material ; Conductivity ; Electrical resistivity and conductivity ; Polymer ; Finite element method ; Electrical conductor ; Percolation theory ; Carbon nanotube actuators ; Nanotube ; Computer science ; Mechanical properties of carbon nanotubes ; Structural engineering ; Artificial intelligence ; Electrical engineering Carbon Nanotubes in Composites ; Dielectric materials and actuators ; Smart Materials for Construction
Authors: Feng Guo | Yushi Wen | Shiquan Feng | Xiaodong Li | Hengshuai Li | Shouxin Cui | Zhenrong Zhang | Haiquan Hu | Guiqing Zhang | Xinlu Cheng
Source: Computational Materials Science (article, 2019-11-12)
Field: Materials Science — Materials Chemistry

Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning ReaxFF ; Computer science ; Artificial neural network ; Solver ; Artificial intelligence ; Backpropagation ; Molecular dynamics ; Force field (fiction) ; Deep learning ; Computational science ; Machine learning ; Chemistry ; Computational chemistry ; Interatomic potential Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Gunnar Alexander Schmitz | Ian H. Godtliebsen | Ove Christiansen
Source: The Journal of Chemical Physics (article, 2019-06-27)
Field: Materials Science — Materials Chemistry

Machine learning for potential energy surfaces: An extensive database and assessment of methods On the basis of a new extensive database constructed for the purpose, we assess various Machine Learning (ML) algorithms to predict energies in the framework of potential energy surface (PES) construction and discuss black box character, robustness, and efficiency. The database for training ML algorithms in energy predictions based on the molecular structure contains SCF, RI-MP2, RI-MP2-F12, and CCSD(F12<sup>*</sup>)(T) data for around 10.5 × 10<sup>6</sup> configurations of 15 small molecules. The electronic energies as function of molecular structure are computed from both static and iteratively refined grids in the context of automized PES construction for anharmonic vibrational computations within the n-mode expansion. We explore the performance of a range of algorithms including Gaussian Process Regression (GPR), Kernel Ridge Regression, Support Vector Regression, and Neural Networks (NNs). We also explore methods related to GPR such as sparse Gaussian Process Regression, Gaussian process Markov Chains, and Sparse Gaussian Process Markov Chains. For NNs, we report some explorations of architecture, activation functions, and numerical settings. Different delta-learning strategies are considered, and the use of delta learning targeting CCSD(F12<sup>*</sup>)(T) predictions using, for example, RI-MP2 combined with machine learned CCSD(F12<sup>*</sup>)(T)-RI-MP2 differences is found to be an attractive option. Kriging ; Computer science ; Gaussian process ; Artificial intelligence ; Support vector machine ; Markov chain ; Gaussian ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jimin Qian | Nam Phuong Nguyen | Yutaka Oya | Gota Kikugawa | Tomonaga Okabe | Yue Huang | Fumio S. Ohuchi
Source: Results in Materials (article, 2019-11-03)
Field: Materials Science — Materials Chemistry

Introducing self-organized maps (SOM) as a visualization tool for materials research and education Materials informatics is an emerging discipline that opens a new paradigm of science: data-driven materials discovery. The visualization of high dimensional material properties data is challenging due to a lack of appropriate tools. This article describes a workflow for using Self-Organizing Maps (SOM) as a time- and cost-efficient method of visualizing and validating property relationships between materials. SOM is a machine learning technique that uses dimensionality reduction based on similarities between properties to visualize the relationships between materials in a high-dimensional dataset. While the conventionally used visualization method, Ashby plot, displays a pair of material’s properties, SOM is a technique that can display multiple properties that are effective for high-dimensional materials correlation studies. In this paper, SOM was used to validate property correlations among 21 material properties in a dataset of over 398 commercial materials. We employed U-matrix map, clustering map and heatmaps to visualize the SOM we trained. The clustering and heat maps produced from SOM were used to identify unique materials and infer correlations between material properties and fundamental material structure. We have also shown that SOM method can provide multiple layers of interpretation through visualizing not only numerical properties but also categorical information of materials. Lastly, we have used SOM to provide new insight into the quantity of Grüneisen parameter across metals and ceramics. Through these examples, it is demonstrated that SOM can be used for different types of materials analyses for various applications. This paper advocates SOM as an integrated approach to study material property relationships that can be used for both materials education and discovery. Self-organizing map ; Visualization ; Computer science ; Cluster analysis ; Workflow ; Categorical variable ; Materials informatics ; Data mining ; Dimensionality reduction ; Artificial intelligence ; Machine learning ; Database ; Health informatics Machine Learning in Materials Science ; Neural Networks and Applications ; X-ray Diffraction in Crystallography