jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ulf Anderegg | Norbert Halfter | Matthias Schnabelrauch | Vera Hintze
Source: Biological Chemistry (review, 2021-07-04)
Field: Materials Science — Biomaterials

Collagen/glycosaminoglycan-based matrices for controlling skin cell responses Wound healing and tissue regeneration are orchestrated by the cellular microenvironment, e.g. the extracellular matrix (ECM). Including ECM components in biomaterials is a promising approach for improving regenerative processes, e.g. wound healing in skin. This review addresses recent findings for enhanced epidermal-dermal regenerative processes on collagen (coll)/glycosaminoglycan (GAG)-based matrices containing sulfated GAG (sGAG) in simple and complex <i>in vitro</i> models. These matrices comprise 2D-coatings, electrospun nanofibrous scaffolds, and photo-crosslinked acrylated hyaluronan (HA-AC)/coll-based hydrogels. They demonstrated to regulate keratinocyte and fibroblast migration and growth, to stimulate melanogenesis in melanocytes from the outer root sheath (ORS) of hair follicles and to enhance the epithelial differentiation of human mesenchymal stem cells (hMSC). The matrices' suitability for delivery of relevant growth factors, like heparin-binding epidermal growth factor like growth factor (HB-EGF), further highlights their potential as bioinspired, functional microenvironments for enhancing skin regeneration. Extracellular matrix ; Cell biology ; Glycosaminoglycan ; Wound healing ; Self-healing hydrogels ; Chemistry ; Regeneration (biology) ; Keratinocyte growth factor ; Mesenchymal stem cell ; Fibroblast ; Hyaluronic acid ; Matrix (chemical analysis) ; Epidermal growth factor ; Regenerative medicine ; Fibroblast growth factor ; Growth factor ; Tissue engineering ; Stem cell ; In vitro ; Biomedical engineering ; Immunology ; Anatomy ; Biology ; Biochemistry ; Medicine ; Polymer chemistry Silk-based biomaterials and applications ; Wound Healing and Treatments ; Proteoglycans and glycosaminoglycans research
Authors: Paolo Rissone | Eric I. Corwin | Giorgio Parisi
Source: Physical Review Letters (article, 2021-07-12)
Field: Materials Science — Materials Chemistry

Long-Range Anomalous Decay of the Correlation in Jammed Packings We numerically study the structure of the interactions occurring in three-dimensional systems of hard spheres at jamming, focusing on the large-scale behavior. Given the fundamental role in the configuration of jammed packings, we analyze the propagation through the system of the weak forces and of the variation of the coordination number with respect to the isostaticity condition, ΔZ. We show that these correlations can be successfully probed by introducing a correlation function weighted on the density-density fluctuations. The results of this analysis can be further improved by introducing a representation of the system based on the contact points between particles. In particular, we find evidence that the weak forces and the ΔZ fluctuations support the hypothesis of randomly jammed packings of spherical particles being hyperuniform by exhibiting an anomalous long-range decay. Moreover, we find that the large-scale structure of the density-density correlation exhibits a complex behavior due to the superimposition of two exponentially damped oscillating signals propagating with linearly depending frequencies. Physics ; Jamming ; Range (aeronautics) ; Correlation function (quantum field theory) ; Statistical physics ; Scale (ratio) ; SPHERES ; Representation (politics) ; Granular matter ; Soft matter ; Condensed matter physics ; Granular material ; Quantum mechanics ; Materials science ; Colloid Material Dynamics and Properties ; Theoretical and Computational Physics ; Force Microscopy Techniques and Applications
Authors: Qinglei Ji | Mo Chen | Xi Vincent Wang | Lihui Wang | Lei Feng
Source: Robotics and Computer-Integrated Manufacturing (article, 2021-07-17)
Field: Materials Science — Polymers and Plastics

Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning 4D printing technology, as a new generation of Additive Manufacturing methods, enables printed objects to further change their shapes or other properties upon external stimuli. One main category of 4D printing research is 4D printed thermal Shape Memory Polymer (SMP). Its morphing process has large time delay, is nonlinear time variant, and susceptible to unpredictable disturbances. Reaching an arbitrary position with high precision is an active research question. This paper applies the Reinforcement Learning (RL) method to develop an optimal control method to perform closed loop control of the SMP actuation. Precise and prompt shape morphing is achieved compared with previous control methods using a PI controller. The training efforts of RL are further reduced by simplifying the optimal control policy using the structural property of the prior trained results. Customized protective visors against COVID-19 are fabricated using the proposed control method. Morphing ; Reinforcement learning ; Computer science ; Shape-memory polymer ; Process (computing) ; Reinforcement ; Controller (irrigation) ; Control (management) ; Property (philosophy) ; Position (finance) ; Control theory (sociology) ; Artificial intelligence ; Shape-memory alloy ; Engineering ; Structural engineering Polymer composites and self-healing ; Advanced Materials and Mechanics ; Biomimetic flight and propulsion mechanisms
Authors: Hassna El‐Bousiydy | Teo Lombardo | Emiliano N. Primo | Marc Duquesnoy | Mathieu Morcrette | Patrik Johansson | Patrice Simon | Alexis Grimaud | Alejandro A. Franco
Source: Batteries & Supercaps (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits? Abstract Artificial Intelligence (AI) has the promise of providing a paradigm shift in battery R&amp;D by significantly accelerating the discovery and optimization of materials, interfaces, phenomena, and processes. However, the efficiency of any AI approach ultimately relies on rapid access to high‐quality and interpretable large datasets. Scientific publications contain a tremendous wealth of relevant data and these can possibly, but not certainly, be used to develop reliable AI algorithms useful for battery R&amp;D. To address this, we present here a text mining study wherein we unravel lithium‐ion battery researchers’ habits when reporting results, reason on how these habits link to issues of lacking reproducibility and discuss the remaining challenges to be tackled in order to develop a more credible and impactful AI for battery R&amp;D. Battery (electricity) ; Computer science ; Lithium (medication) ; Data science ; Lithium-ion battery ; Quality (philosophy) ; Artificial intelligence ; Psychology ; Physics ; Power (physics) ; Psychiatry Machine Learning in Materials Science
Authors: Ajit Vikram | Ken Brudnak | Arwa Zahid | Moonsub Shim | Paul J. A. Kenis
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology Colloidal semiconductor nanocrystals with tunable optical and electronic properties are opening up exciting opportunities for high-performance optoelectronics, photovoltaics, and bioimaging applications. Identifying the optimal synthesis conditions and screening of synthesis recipes in search of efficient synthesis pathways to obtain nanocrystals with desired optoelectronic properties, however, remains one of the major bottlenecks for accelerated discovery of colloidal nanocrystals. Conventional strategies, often guided by limited understanding of the underlying mechanisms remain expensive in both time and resources, thus significantly impeding the overall discovery process. In response, an autonomous experimentation platform is presented as a viable approach for accelerated synthesis screening and optimization of colloidal nanocrystals. Using a machine-learning-based predictive synthesis approach, integrated with automated flow reactor and inline spectroscopy, indium phosphide nanocrystals are autonomously synthesized. Their polydispersity for different target absorption wavelengths across the visible spectrum is simultaneously optimized during the autonomous experimentation, while utilizing minimal self-driven experiments (less than 50 experiments within 2 days). Starting with no-prior-knowledge of the synthesis, an ensemble neural network is trained through autonomous experiments to accurately predict the reaction outcome across the entire synthesis parameter space. The predicted parameter space map also provides new nucleation-growth kinetic insights to achieve high monodispersity in size of colloidal nanocrystals. Nanocrystal ; Materials science ; Nanotechnology ; Dispersity ; Quantum dot ; Computer science Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Lei Guo | Shilin Ding | Shuai Yuan | Xiaofeng Gou | Fenglin Cai | Dongyang Wang | Haiquan Zhao
Source: e-Polymers (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Study on the thermal properties and insulation resistance of epoxy resin modified by hexagonal boron nitride Abstract To study the effect of doping hexagonal boron nitride (h-BN) on the thermal properties and insulation resistance of epoxy resin (EP) and the mechanism of this effect, h-BN/epoxy composites with h-BN content of 0, 10, 20, 30, and 40 phr were prepared. Meanwhile, the corresponding molecular dynamics model of h-BN/epoxy composites was established, and the thermal conductivity, volume resistivity, glass transition temperature, and microstructure parameters of h-BN/epoxy composites were obtained. When the h-BN content is 40 phr, the thermal conductivity of h-BN/epoxy composite is increased by 138% compared to pure EP, and the glass transition temperature is increased by 76 K. At the same time, doping h-BN will reduce the insulation performance of EP. However, the lowest volume resistivity of h-BN/epoxy composite is still 1.43 × 10 15 Ω·cm, and the EP composite still has good insulation performance. The fraction free volume and mean square displacement of EP decrease with the doping of h-BN, which indicates that h-BN can hinder the movement of molecular segments of EP, which is the reason for the increase in glass transition temperature. Epoxy ; Materials science ; Composite material ; Glass transition ; Composite number ; Microstructure ; Boron nitride ; Volume fraction ; Doping ; Thermal conductivity ; Boron ; Electrical resistivity and conductivity ; Polymer ; Chemistry Thermal properties of materials ; High voltage insulation and dielectric phenomena ; Epoxy Resin Curing Processes
Authors: Arantzazu Valdés | Carmen Martínez‐Graciá | Marı́a Carmen Garrigós | Alfonso Jiménez
Source: Polymers (article, 2021-10-20)
Field: Materials Science — Biomaterials

Multilayer Films Based on Poly(lactic acid)/Gelatin Supplemented with Cellulose Nanocrystals and Antioxidant Extract from Almond Shell By-Product and Its Application on Hass Avocado Preservation In this work, poly(lactic acid) (PLA)/gelatin/PLA multilayer films supplemented with cellulose nanocrystals and antioxidant extract from almond shell (AS) by-products were developed by solvent casting technique for active food packaging. The almond shell antioxidant extract (ASE) was obtained by microwave-assisted extraction, while cellulose nanocrystals (CNCs) were extracted from AS by a sequential process of alkalization, acetylation and acid hydrolysis. Four formulations were obtained by adding 0 (control), 6 wt.% of ASE (FG/ASE), 4.5 wt.% of CNCs (FG/CNC) and 6 wt.% + 4.5 wt.% of ASE + CNCs, respectively, (FG/ASE + CNC) into fish gelatin (FG). PLA/FG/PLA multilayer films were prepared by stacking two outer PLA layers into a middle FG film. A surface modification of PLA by air atmospheric plasma treatment was optimized before multilayer development to improve PLA adhesion. Complete characterization of the multilayers underlined the FG/ASE + CNC formulation as a promising active reinforced packaging system for food preservation, with low values of transparency, lightness and whiteness index. A good adhesion and homogeneity of the multilayer system was obtained by SEM, and they also demonstrated low oxygen permeability (40.87 ± 5.20 cm<sup>3</sup> mm m<sup>-2</sup> day) and solubility (39.19 ± 0.16%) values, while mechanical properties were comparable with commercial plastic films. The developed multilayer films were applied to Hass avocado preservation. The initial degradation temperature (T<sub>ini</sub>), DSC parameters and in vitro antioxidant capacity of the films were in accordance with the low peroxide and anisidine values obtained from avocado pulp after packaging for 14 days at 4 °C. The developed PLA/FG/PLA films supplemented with 6 wt.% ASE+ 4.5 wt.% CNCs may be potential bioactive packaging systems for fat food preservation. Materials science ; Gelatin ; Cellulose ; Oxygen permeability ; Lactic acid ; Solubility ; Chemical engineering ; Composite material ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Oxygen Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Agricultural and Food Sciences
Authors: Xinyu Li | Raymond Chiong | Alister J. Page
Source: The Journal of Physical Chemistry Letters (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts Machine learning has recently emerged as an efficient and powerful alternative to density functional theory for studying heterogeneous catalysis. Machine learning methods rely on a geometrical representation of the chemical environment around the catalytic adsorption site based on physical or chemical descriptors. Here, we show that replacing the atomic number in geometrical representations with elemental groups and periods (GP) yields significant improvements in predicted adsorption energies on bimetallic alloy surfaces. Notably, the GP-based Labeled Site Crystal Graph representation reported here achieves mean absolute error (MAE) ∼0.05 eV (near chemical accuracy) in predicting hydrogen adsorption and MAE ∼0.10 eV for other strong binding adsorbates such as carbon, nitrogen, oxygen, and sulfur. We also show GP-based representations to be robust in predicting adsorption on surface facets, elements, and alloys that are not included in the initial training set. This reliability makes GP-based representations an ideal basis for high-throughput approaches and materials discovery based on active learning techniques, which often involve limited training sets. Bimetallic strip ; Adsorption ; Catalysis ; Density functional theory ; Alloy ; Representation (politics) ; Materials science ; Chemistry ; Computer science ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Bruno Cuevas-Zuviría | Luis F. Pacios
Source: Journal of Chemical Information and Modeling (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing Machine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magnitudes as a basis to derive many other properties. By using a model of the electron density consisting of an analytical expansion on a linear set of isotropic and anisotropic functions, we implemented in this work a message-passing neural network able to reproduce electron density in molecules with just a 2.5% absolute error in complex cases. We also adapted our methodology to describe electron density in large biomolecules (proteins) and to obtain atomic charges, interaction energies, and DFT energies. We show that electron density learning is a new promising avenue with a variety of forthcoming applications. Electron density ; Isotropy ; Computer science ; Set (abstract data type) ; Electron ; Density functional theory ; Basis (linear algebra) ; Statistical physics ; Biomolecule ; Path (computing) ; Artificial neural network ; Message passing ; Basis set ; Artificial intelligence ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Materials science ; Nanotechnology ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Elizabeth S. Thrall | Seung Eun Lee | Joshua Schrier | Yijun Zhao
Source: Journal of Chemical Education (article, 2021-09-03)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning for Functional Group Identification in Vibrational Spectroscopy: A Pedagogical Lab for Undergraduate Chemistry Students Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces undergraduate physical chemistry students to ML in the context of vibrational spectroscopy. In the first part of the activity, students use ML binary classification algorithms to distinguish between carbonyl-containing and noncarbonyl-containing molecules on the basis of their infrared absorption spectra. In the second part of the activity, students test modifications to this basic analysis including different analysis parameters, different ML algorithms, and different test data sets. In a final extension of the activity, students implement a multiclass classification to predict whether carbonyl-containing molecules contain a ketone, a carboxylic acid, or another carbonyl group. This activity is designed to introduce students both to the basic workflow of a ML classification analysis and to some of the ways in which ML analyses can fail. We provide a comprehensive handout for the activity, including theoretical background and a detailed protocol, as well as data sets and code to implement the exercise in Python or Mathematica. This activity is designed as a standalone exercise for physical chemistry lab classes but can also be integrated with courses or modules on vibrational spectroscopy and computational chemistry. On the basis of student surveys, we conclude that this activity was successful in introducing students to applications of ML in chemistry. Chemistry ; Context (archaeology) ; Workflow ; Computer science ; Python (programming language) ; Artificial intelligence ; Mathematics education ; Programming language ; Mathematics ; Database Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science
Authors: Yanli Zhao | Chunmei Fu | L. Fu | Ying-Dong Liu | Zhipeng Lu | Xunchi Pu
Source: Materials Today Chemistry (article, 2021-11-12)
Field: Materials Science — Materials Chemistry

Data-driven machine learning models for quick prediction of thermal stability properties of OLED materials Stability (learning theory) ; OLED ; Thermal stability ; Materials science ; Thermal ; Computer science ; Machine learning ; Composite material ; Engineering ; Thermodynamics ; Chemical engineering ; Physics Machine Learning in Materials Science ; Organic Light-Emitting Diodes Research ; Thin-Film Transistor Technologies
Authors: Yoshinori Shiihara | Ryosuke Kanazawa | Daisuke MATSUNAKA | Ivan Lobzenko | Tomohito Tsuru | Masanori Kohyama | Hideki Mori
Source: Scripta Materialia (article, 2021-09-17)
Field: Materials Science — Metals and Alloys

Artificial neural network molecular mechanics of iron grain boundaries This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in α-iron using molecular mechanics based on an artificial neural network (ANN) potential and compares the results with calculations based on the density functional theory (DFT), the embedded atom method (EAM), and the modified EAM (MEAM). The results by the ANN potential are in excellent agreement with those of the DFT (5% on average), while the EAM and MEAM significantly differ from the DFT results (about 27% on average). In a uniaxial tensile calculation of ∑3(11¯2) GB, the ANN potential reproduced the brittle fracture tendency of the GB observed in the DFT while the EAM and MEAM mistakenly showed ductile behaviors. These results demonstrate the effectiveness of the ANN potential in calculating grain boundaries of iron, which is in high demand in modern industry. Materials science ; Grain boundary ; Density functional theory ; Brittleness ; Artificial neural network ; Embedded atom model ; Tilt (camera) ; Molecular dynamics ; Atom (system on chip) ; Thermodynamics ; Statistical physics ; Metallurgy ; Microstructure ; Physics ; Computer science ; Artificial intelligence ; Mathematics ; Geometry ; Quantum mechanics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Gabriele M. Coli | Marjolein Dijkstra
Source: ACS Nano (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB<sub>13</sub> Crystal Colloidal suspensions of two species have the ability to form binary crystals under certain conditions. The hunt for these functional materials and the countless investigations on their formation process are justified by the plethora of synergetic and collective properties these binary superlattices show. Among the many crystal structures observed over the past decades, the highly exotic colloidal <i>icosahedral</i> AB<sub>13</sub> crystal was predicted to be stable in binary hard-sphere mixtures nearly 30 years ago, yet the kinetic pathway of how homogeneous nucleation occurs in this system is still unknown. Here we investigate binary nucleation of the AB<sub>13</sub> crystal from a binary fluid phase of nearly hard spheres. We calculate the nucleation barrier and nucleation rate as a function of supersaturation and draw a comparison with nucleation of single-component and other binary crystals. To follow the nucleation process, we employ a neural network to identify the AB<sub>13</sub> phase from the binary fluid phase and the competing fcc crystal with single-particle resolution and significant accuracy in the case of bulk phases. We show that AB<sub>13</sub> crystal nucleation proceeds <i>via</i> a coassembly process where large spheres and icosahedral small-sphere clusters simultaneously attach to the nucleus. Our results lend strong support for a classical pathway that is well-described by classical nucleation theory, even though the binary fluid phase is highly structured and exhibits local regions of high bond orientational order. Nucleation ; Crystal (programming language) ; Chemical physics ; Materials science ; Icosahedral symmetry ; Phase (matter) ; Classical nucleation theory ; Colloidal crystal ; Binary number ; Crystallography ; Colloid ; Thermodynamics ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Crystallization and Solubility Studies