jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ayushi Mittal | Krishna | Aarti Aarti | Suchita Prasad | Prashant Kumar Mishra | Sunil Sharma | Badri Parshad
Source: Materials Advances (review, 2021-01-01)
Field: Materials Science — Biomaterials

Self-assembly of carbohydrate-based small amphiphiles and their applications in pathogen inhibition and drug delivery: a review Carbohydrate-based amphiphiles: self-assembled nanostructures of defined size and shape for various biomedical applications. Amphiphile ; Drug delivery ; Nanotechnology ; Carbohydrate ; Self-assembly ; Nanostructure ; Chemistry ; Small molecule ; Materials science ; Biochemistry ; Organic chemistry ; Copolymer ; Polymer Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior
Authors: Fanny d’Orlyé | Laura Trapiella‐Alfonso | Camille Lescot | Marie Pinvidic | Bich‐Thuy Doan | Anne Varenne
Source: Molecules (review, 2021-07-29)
Field: Materials Science — Biomaterials

Synthesis, Characterization and Evaluation of Peptide Nanostructures for Biomedical Applications There is a challenging need for the development of new alternative nanostructures that can allow the coupling and/or encapsulation of therapeutic/diagnostic molecules while reducing their toxicity and improving their circulation and in-vivo targeting. Among the new materials using natural building blocks, peptides have attracted significant interest because of their simple structure, relative chemical and physical stability, diversity of sequences and forms, their easy functionalization with (bio)molecules and the possibility of synthesizing them in large quantities. A number of them have the ability to self-assemble into nanotubes, -spheres, -vesicles or -rods under mild conditions, which opens up new applications in biology and nanomedicine due to their intrinsic biocompatibility and biodegradability as well as their surface chemical reactivity via amino- and carboxyl groups. In order to obtain nanostructures suitable for biomedical applications, the structure, size, shape and surface chemistry of these nanoplatforms must be optimized. These properties depend directly on the nature and sequence of the amino acids that constitute them. It is therefore essential to control the order in which the amino acids are introduced during the synthesis of short peptide chains and to evaluate their in-vitro and in-vivo physico-chemical properties before testing them for biomedical applications. This review therefore focuses on the synthesis, functionalization and characterization of peptide sequences that can self-assemble to form nanostructures. The synthesis in batch or with new continuous flow and microflow techniques will be described and compared in terms of amino acids sequence, purification processes, functionalization or encapsulation of targeting ligands, imaging probes as well as therapeutic molecules. Their chemical and biological characterization will be presented to evaluate their purity, toxicity, biocompatibility and biodistribution, and some therapeutic properties in vitro and in vivo. Finally, their main applications in the biomedical field will be presented so as to highlight their importance and advantages over classical nanostructures. Nanotechnology ; Surface modification ; Biomolecule ; Biocompatibility ; Combinatorial chemistry ; Nanomedicine ; Peptide ; Amino acid ; Nanostructure ; Molecule ; Materials science ; Chemistry ; Nanoparticle ; Biochemistry ; Organic chemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Sandeep Kumar Chaluvadi | Debashis Mondal | Chiara Bigi | Daniel Knez | Piu Rajak | Regina Ciancio | Jun Fujii | G. Panaccione | I. Vobornik | G. Rossi | P. Orgiani
Source: Journal of Physics Materials (article, 2021-02-15)
Field: Materials Science — Materials Chemistry

Pulsed laser deposition of oxide and metallic thin films by means of Nd:YAG laser source operating at its 1st harmonics: recent approaches and advances Abstract Quantum materials are central for the development of novel functional systems that are often based on interface specific phenomena. Fabricating controlled interfaces between quantum materials requires adopting a flexible growth technique capable to synthesize different materials within a single-run deposition process with high control of structure, stoichiometry, and termination. Among the various available thin film growth technologies, pulsed laser deposition (PLD) allows controlling the growth of diverse materials at the level of single atomic layers. In PLD the atomic species are supplied through an ablation process of a stoichiometric target either in form of polycrystalline powders or of a single crystal. No carrier gases are needed in the deposition process. The ablation process is compatible with a wide range of background pressure. We present results of thin-film growth by PLD obtained by using an Nd:YAG infrared pulsed laser source operating at its first harmonics. With respect to the traditional PLD systems—based on excimer KrF UV-lasers—optimal conditions for the growth of thin films and heterostructures are reached at large target-to-substrate distance. Merits and limitations of this approach for growing oxide and non-oxide thin films are discussed. The merits of an Nd:YAG laser to grow very high-quality thin films suggest the possibility of implementing compact in-situ setups e.g. integrated with analytical instrumentation under ultra-high vacuum conditions. Thin film ; Materials science ; Pulsed laser deposition ; Optoelectronics ; Laser ; Heterojunction ; Laser ablation ; Substrate (aquarium) ; Nanotechnology ; Optics Electronic and Structural Properties of Oxides ; ZnO doping and properties ; Magnetic and transport properties of perovskites and related materials
Authors: Yu Gu | Peirong Lin | Chengda Zhou | Mao Chen
Source: Science China Chemistry (article, 2021-05-17)
Field: Materials Science — Materials Chemistry

Machine learning-assisted systematical polymerization planning: case studies on reversible-deactivation radical polymerization Polymerization ; Polymer ; Radical polymerization ; Computer science ; Materials science ; Chemical engineering ; Polymer chemistry ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Fuel Cells and Related Materials
Authors: Yi Yao | Timothy Sullivan | Feng Yan | Jiaqi Gong | Lin Li
Source: Scripta Materialia (article, 2021-10-30)
Field: Materials Science — Materials Chemistry

Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys Overfitting ; Generalizability theory ; Machine learning ; Artificial intelligence ; Fidelity ; Artificial neural network ; Ternary operation ; Computer science ; Experimental data ; Materials informatics ; Data mining Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Metallic Glasses and Amorphous Alloys
Authors: Reed Kopp | Joshua Joseph | Xinchen Ni | Nicholas Roy | Brian L. Wardle
Source: Advanced Materials (article, 2021-11-20)
Field: Materials Science — Materials Chemistry

Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials Four-dimensional quantitative characterization of heterogeneous materials using in situ synchrotron radiation computed tomography can reveal 3D sub-micrometer features, particularly damage, evolving under load, leading to improved materials. However, dataset size and complexity increasingly require time-intensive and subjective semi-automatic segmentations. Here, the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials is presented, teaching on advanced aerospace-grade composite damage using ≈65 000 (trained) human-segmented tomograms. The trained CNN machine segments complex and sparse (<<1% of volume) composite damage classes to ≈99.99% agreement, unlocking both objectivity and efficiency, with nearly 100% of the human time eliminated, which traditional rule-based algorithms do not approach. The trained machine is found to perform as well or better than the human due to "machine-discovered" human segmentation error, with machine improvements manifesting primarily as new damage discovery and segmentation augmentation/extension in artifact-rich tomograms. Interrogating a high-level network hyperparametric space on two material configurations, DL is found to be a disruptive approach to quantitative structure-property characterization, enabling high-throughput knowledge creation (accelerated by two orders of magnitude) via generalizable, ultrahigh-resolution feature segmentation. Microscale chemistry ; Artificial intelligence ; Convolutional neural network ; Segmentation ; Deep learning ; Computer science ; Materials science ; Machine learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Rock Mechanics and Modeling
Authors: Eisuke F. Sato | Mayu Fujii | Hiroki Tanaka | Koichi Mitsudo | Masaru Kondo | Shinobu Takizawa | Hiroaki Sasai | Takeshi Washio | Kazunori Ishikawa | Seiji Suga
Source: The Journal of Organic Chemistry (article, 2021-08-06)
Field: Chemistry — Organic Chemistry

Application of an Electrochemical Microflow Reactor for Cyanosilylation: Machine Learning-Assisted Exploration of Suitable Reaction Conditions for Semi-Large-Scale Synthesis Cyanosilylation of carbonyl compounds provides protected cyanohydrins, which can be converted into many kinds of compounds such as amino alcohols, amides, esters, and carboxylic acids. In particular, the use of trimethylsilyl cyanide as the sole carbon source can avoid the need for more toxic inorganic cyanides. In this paper, we describe an electrochemically initiated cyanosilylation of carbonyl compounds and its application to a microflow reactor. Furthermore, to identify suitable reaction conditions, which reflect considerations beyond simply a high yield, we demonstrate machine learning-assisted optimization. Machine learning can be used to adjust the current and flow rate at the same time and identify the conditions needed to achieve the best productivity. Trimethylsilyl cyanide ; Flow chemistry ; Cyanide ; Yield (engineering) ; Chemistry ; Computer science ; Combinatorial chemistry ; Organic chemistry ; Materials science ; Catalysis Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Q. Wang | B. Zhai | Haipeng Wang | B. Wei
Source: Journal of Applied Physics (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential The knowledge of atomic structure for liquids, especially for liquid alloys with complex crystal structure and high liquidus temperatures, remains poorly understood. In this work, we have extended the development of deep neural network (DNN) potential for liquid Nb5Si3. The DNN potential captures the structural features of liquid alloys compared with ab initio results. The chemical short-range order parameter suggests that there exists strong affinity between Nb and Si atoms. The dynamic property was investigated, and the diffusion coefficient obeys the Arrhenius relationship. The atomic structure has been subsequently explored for normal and undercooled liquid Nb5Si3. Large amounts of fivefold symmetry Honeycutt–Andersen pairs have been identified in liquid Nb5Si3. However, due to the violent thermal motion in a high-temperature Nb5Si3 melt, icosahedral symmetry and distorted icosahedrons (ICOs) account for little proportion according to Voronoi polyhedron (VP) analysis. The effect of thermal motion on VPs has been discussed. Except from the well documented ⟨0,2,8,2⟩ and ⟨0,1,10,2⟩ distorted ICOs, six more quasi-ICOs (⟨0,1,9,3⟩, ⟨0,2,8,1⟩, ⟨0,2,8,4⟩, ⟨0,2,8,5⟩, ⟨0,1,10,3⟩, and ⟨0,1,10,4⟩) have been proven to deform from ICOs at high temperatures. The local environment motif obtained by the atomic cluster alignment method demonstrates the existence of dominant distorted ICOs. At last, the atomic structure during melting process is discussed by VP analysis. It is found that ⟨0,2,8,1⟩, ⟨0,2,8,2⟩, ⟨0,2,8,5⟩, and ⟨0,1,10,4⟩ prefer to form at the beginning of the melting but rapidly reduce when it is fully melted. Atomic diffusion ; Liquidus ; Intermetallic ; Materials science ; Icosahedral symmetry ; Crystal structure ; Ab initio ; Alloy ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Masaru Kondo | Akimasa Sugizaki | Md. Imrul Khalid | H. D. P. Wathsala | Kazunori Ishikawa | Satoshi Hara | Takayuki Takaai | Takashi Washio | Shinobu Takizawa | Hiroaki Sasai
Source: Green Chemistry (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation A highly efficient synthesis of α-ketiminophosphonates has been established for the electrochemical oxidation of α-amino phosphonates with the utilization of machine-learning-assisted simultaneous multiparameter screening. Electrochemistry ; Computer science ; Energy (signal processing) ; Chemistry ; Combinatorial chemistry ; Electrode ; Mathematics ; Physical chemistry Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods
Authors: Jeffrey G. Ethier | Rohan K. Casukhela | Joshua J. Latimer | Matthew Jacobsen | Allen B. Shantz | Richard A. Vaia
Source: ACS Macro Letters (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Deep Learning of Binary Solution Phase Behavior of Polystyrene Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory-Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by building a machine learning framework to make fast and accurate predictions of polymer solution cloud point temperatures. Using polystyrene, both upper and lower critical solution temperatures are predicted within experimental uncertainty (1-2 °C) with a deep neural network, Gaussian process regression (GPR) model, and a combination of polymer, solvent, and state features. The GPR model also enables intelligent exploration of solution phase space, where as little as 25 cloud points are required to make predictions within 2 °C for polystyrene of arbitrary molecular weight in cyclohexane. This study demonstrates the effectiveness of machine learning for the prediction of liquid-liquid equilibrium of polymer solutions and establishes a framework to incorporate other polymers and complex macromolecular architectures. Polystyrene ; Polymer ; Binary number ; Materials science ; Cyclohexane ; Lower critical solution temperature ; Cloud point ; Phase (matter) ; Computer science ; Thermodynamics ; Biological system ; Statistical physics ; Artificial intelligence ; Mathematics ; Chemistry ; Physics ; Organic chemistry ; Copolymer ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Haochuan Chen | Han Liu | Heying Feng | Haohao Fu | Wensheng Cai | Xueguang Shao | Christophe Chipot
Source: Journal of Chemical Information and Modeling (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (MD) simulations. In practice, the model RC often consists of a handful of collective variables (CVs) chosen on the basis of chemical intuition. However, constructing manually a low-dimensional RC model to describe an intricate geometrical transformation for the purpose of free-energy calculations and analyses remains a daunting challenge due to the inherent complexity of the conformational transitions at play. To solve this issue, remarkable progress has been made in employing machine-learning techniques, such as autoencoders, to extract the low-dimensional RC model from a large set of CVs. Implementation of the differentiable, nonlinear machine-learned CVs in common MD engines to perform free-energy calculations is, however, particularly cumbersome. To address this issue, we present here a user-friendly tool (called MLCV) that facilitates the use of machine-learned CVs in importance-sampling simulations through the popular Colvars module. Our approach is critically probed with three case examples consisting of small peptides, showcasing that through hard-coded neural network in Colvars, deep-learning and enhanced-sampling can be effectively bridged with MD simulations. The MLCV code is versatile, applicable to all the CVs available in Colvars, and can be connected to any kind of dense neural networks. We believe that MLCV provides an effective, powerful, and user-friendly platform accessible to experts and nonexperts alike for machine-learning (ML)-guided CV discovery and enhanced-sampling simulations to unveil the molecular mechanisms underlying complex biochemical processes. Computer science ; Artificial intelligence ; Intuition ; Machine learning ; Bridging (networking) ; Dimensionality reduction ; Curse of dimensionality ; Artificial neural network ; Differentiable function ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Krzesimir Ciura | Strаhinjа Kоvаčеvić | Monika Pastewska | Hanna Kapica | Martyna Kornela | Wiesław Sawicki
Source: Journal of Chromatography A (article, 2021-11-05)
Field: Chemistry — Spectroscopy

Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating interactions between a molecule and a biological membrane. This study developed a quantitative structure-retention relationships model that would predict the chromatographically determined affinity of xenobiotics to phospholipids, expressed as a chromatographic hydrophobicity index determined using immobilized artificial membrane chromatography. A heterogeneous set of 261 molecules, mostly showing pharmacological activity or toxicity, was analyzed chromatographically to realize this goal. The chromatographic analysis was performed using the fast gradient protocol proposed by Valko, where acetonitrile was applied as an organic modifier. Next, quantitative structure-retention relationships modeling was performed using multiple linear regression (MLR) methods and artificial neural networks (ANNs) coupled with genetic algorithm (GA)-inspired selection. Subsequently, the selection of the best ANN was supported by statistical parameters, the sum of ranking differences approach with the comparison of rank by random numbers and hierarchical cluster analysis. Chemistry ; Chromatography ; Artificial neural network ; Synthetic membrane ; Membrane ; Molecular descriptor ; Quantitative structure–activity relationship ; Biological system ; Artificial intelligence ; Computer science ; Stereochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Protein purification and stability
Authors: Rafael Furlan de Oliveira | Verónica Montes‐García | Artur Ciesielski | Paolo Samorı́
Source: Materials Horizons (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Harnessing selectivity in chemical sensing <i>via</i> supramolecular interactions: from functionalization of nanomaterials to device applications Chemical sensing is a strategic field of science and technology ultimately aiming at improving the quality of our lives and the sustainability of our Planet. Sensors bear a direct societal impact on well-being, which includes the quality and composition of the air we breathe, the water we drink, and the food we eat. Pristine low-dimensional materials are widely exploited as highly sensitive elements in chemical sensors, although they suffer from lack of intrinsic selectivity towards specific analytes. Here, we showcase the most recent strategies on the use of (supra)molecular interactions to harness the selectivity of suitably functionalized 0D, 1D, and 2D low-dimensional materials for chemical sensing. We discuss how the design and selection of receptors <i>via</i> machine learning and artificial intelligence hold a disruptive potential in chemical sensing, where selectivity is achieved by the design and high-throughput screening of large libraries of molecules exhibiting a set of affinity parameters that dictates the analyte specificity. We also discuss the importance of achieving selectivity along with other relevant characteristics in chemical sensing, such as high sensitivity, response speed, and reversibility, as milestones for true practical applications. Finally, for each distinct class of low-dimensional material, we present the most suitable functionalization strategies for their incorporation into efficient transducers for chemical sensing. Surface modification ; Selectivity ; Nanotechnology ; Nanomaterials ; Analyte ; Chemical sensor ; Molecular recognition ; Materials science ; Chemical modification ; Computer science ; Molecule ; Chemistry ; Organic chemistry Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Marko Čanađija
Source: Carbon (article, 2021-09-06)
Field: Materials Science — Materials Chemistry

Deep learning framework for carbon nanotubes: Mechanical properties and modeling strategies Tensile tests at room temperature are performed using molecular dynamics on all configurations of single-walled carbon nanotubes up to 4 nm in diameter. Distributions of the Young's modulus , Poisson's ratio , ultimate tensile strength and fracture strain are determined and reported. The results show that the chirality of the nanotube has the greatest influence on the properties. An artificial neural network is developed for the dataset obtained by molecular dynamics and used to predict the mechanical properties. It is clearly shown that Deep Learning provides accurate predictions, with the further advantage that thermal fluctuations are smoothed out. In addition, a through analysis of the effect of dataset size on prediction quality is performed, providing modeling strategies for further researchers. • A complete set of mechanical properties for SWCNTs with diameter up to 4 nm is obtained by MD. • Young's modulus , Poisson's ratio , ultimate tensile strength and strain at fracture are provided at room temperature. • Deep learning is applied to MD results to obtain a neural network that can predict mechanical properties of SWCNTs. • Strategies relying on the deep learning for reducing computational costs are given. Carbon nanotube ; Ultimate tensile strength ; Molecular dynamics ; Materials science ; Modulus ; Poisson's ratio ; Young's modulus ; Chirality (physics) ; Fracture (geology) ; Thermal ; Artificial neural network ; Composite material ; Nanotechnology ; Poisson distribution ; Computer science ; Thermodynamics ; Artificial intelligence ; Physics ; Computational chemistry ; Mathematics ; Chemistry Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Machine Learning in Materials Science