jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Renhai Shi | Jiashi Miao | Alan A. Luo
Source: Scripta Materialia (article, 2019-07-01)
Field: Materials Science — Biomaterials

A new magnesium sheet alloy and its multi-stage homogenization for simultaneously improved ductility and strength at room temperature Materials science ; Microstructure ; Alloy ; Homogenization (climate) ; Elongation ; CALPHAD ; Ductility (Earth science) ; Metallurgy ; Magnesium alloy ; Grain size ; Thermomechanical processing ; Nanoscopic scale ; Composite material ; Ultimate tensile strength ; Phase diagram ; Phase (matter) ; Nanotechnology ; Creep Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Anwesha Chakraborty | Goutam Ghosh | Deep Sankar Pal | Shinto Varghese | Suhrit Ghosh
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Biomaterials

Organobase triggered controlled supramolecular ring opening polymerization and 2D assembly A carboxylic acid appended naphthalene-diimide (NDI) derivative spontaneously aggregates in decane to generate a kinetically controlled product with irregular fibrillar morphology. By fine-tuning the sample preparation conditions, the carboxylic acid group can be trapped by intra-molecular H-bonds with the adjacent imide carbonyl, which retards the spontaneous aggregation. In the presence of a catalytic amount of a non-nucleophilic organic base (DBU or DMAP), the meta-stable monomer exhibits supramolecular polymerization through a thermodynamically controlled pathway involving simultaneous H-bonding and π-stacking and generates ultra-thin 2D nano-sheets. DMAP/DBU helps in ring-opening of the intra-molecularly H-bonded monomer and <i>in situ</i> breeds the free acid, which, beyond a critical concentration, initiates controlled supramolecular ring opening polymerization (SROP) <i>via</i> the chain-growth mechanism. The 2D polymer acts as a macro-initiator for subsequent two cycles of SROP and produces laterally extended ultra-thin nano-sheets. Supramolecular chemistry ; Ring (chemistry) ; Polymerization ; Ring-opening polymerization ; Supramolecular assembly ; Chemistry ; Nanotechnology ; Materials science ; Polymer chemistry ; Crystallography ; Polymer ; Organic chemistry ; Crystal structure Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Polydiacetylene-based materials and applications
Authors: Tomáš Urbánek | Eliézer Jäger | Alessandro Jäger | Martin Hrubý
Source: Polymers (review, 2019-06-19)
Field: Materials Science — Biomaterials

Selectively Biodegradable Polyesters: Nature-Inspired Construction Materials for Future Biomedical Applications In the last half-century, the development of biodegradable polyesters for biomedical applications has advanced significantly. Biodegradable polyester materials containing external stimuli-sensitive linkages are favored in the development of therapeutic devices for pharmacological applications such as delivery vehicles for controlled/sustained drug release. These selectively biodegradable polyesters degrade after particular external stimulus (e.g., pH or redox potential change or the presence of certain enzymes). This review outlines the current development of biodegradable synthetic polyesters materials able to undergo hydrolytic or enzymatic degradation for various biomedical applications, including tissue engineering, temporary implants, wound healing and drug delivery. Polyester ; Hydrolytic degradation ; Drug delivery ; Materials science ; Biodegradation ; Nanotechnology ; Biodegradable polymer ; Tissue engineering ; Biomedical engineering ; Chemistry ; Organic chemistry ; Polymer ; Medicine ; Composite material biodegradable polymer synthesis and properties ; Graphene and Nanomaterials Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Elmeri Lahtinen | Rafaella L. M. Precker | Manu Lahtinen | Evamarie Hey‐Hawkins | Matti Haukka
Source: ChemPlusChem (article, 2019-02-01)
Field: Chemistry — Inorganic Chemistry

Selective Laser Sintering of Metal‐Organic Frameworks: Production of Highly Porous Filters by 3D Printing onto a Polymeric Matrix Metal-organic frameworks (MOFs) have raised a lot of interest, especially as adsorbing materials, because of their unique and well-defined pore structures. One of the main challenges in the utilization of MOFs is their crystalline and powdery nature, which makes their use inconvenient in practice. Three-dimensional printing has been suggested as a potential solution to overcome this problem. We used selective laser sintering (SLS) to print highly porous flow-through filters containing the MOF copper(II) benzene-1,3,5-tricarboxylate (HKUST-1). These filters were printed simply by mixing HKUST-1 with an easily printable nylon-12 polymer matrix. By using the SLS, powdery particles were fused together in such a way that the structure of the printed solid material resembles the structure of a powder bed. The MOF additive is firmly attached only on the surface of partially fused polymer particles and therefore remains accessible to fluids passing through the filter. Powder X-ray analysis of the printed object confirmed that printing did not have any negative impact on the structure of the MOF. CO<sub>2</sub> -adsorption studies also showed that the activity of the MOF was not affected by the printing process. SLS offers a straightforward and easy way to fabricate tailor-made MOF-containing filters for practical applications. Porosity ; Materials science ; Metal-organic framework ; Selective laser sintering ; Sintering ; 3D printing ; Matrix (chemical analysis) ; Nanotechnology ; Chemical engineering ; Laser ; Composite material ; Organic chemistry ; Chemistry ; Optics Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Karl Ridier | Alin-Ciprian Bas | Victoria Shalabaeva | William Nicolazzi | Lionel Salmon | Gábor Molnár | Azzedine Bousseksou | Maciej Lorenc | Roman Bertoni | Éric Collet | H. Cailleau
Source: Advanced Materials (article, 2019-04-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite Size Effects on the Switching Dynamics of Spin‐Crossover Thin Films Photoexcited by a Femtosecond Laser Pulse Using ultrafast optical absorption spectroscopy, the room-temperature spin-state switching dynamics induced by a femtosecond laser pulse in high-quality thin films of the molecular spin-crossover (SCO) complex [Fe(HB(tz)<sub>3</sub> )<sub>2</sub> ] (tz = 1,2,4-triazol-1-yl) are studied. These measurements reveal that the early, sub-picosecond, low-spin to high-spin photoswitching event, with linear response to the laser pulse energy, can be followed under certain conditions by a second switching process occurring on a timescale of tens of nanoseconds, enabling nonlinear amplification. This out-of-equilibrium dynamics is discussed in light of the characteristic timescales associated with the different switching mechanisms, i.e., the electronic and structural rearrangements of photoexcited molecules, the propagation of strain waves at the material scale, and the thermal activation above the molecular energy barrier. Importantly, the additional, nonlinear switching step appears to be completely suppressed in the thinnest (50 nm) film due to the efficient heat transfer to the substrate, allowing the system to retrieve the thermal equilibrium state on the 100 ns timescale. These results provide a first milestone toward the assessment of the physical parameters that drive the photoresponse of SCO thin films, opening up appealing perspectives for their use as high-frequency all-optical switches working at room temperature. Materials science ; Femtosecond ; Thin film ; Laser ; Picosecond ; Spin crossover ; Ultrashort pulse ; Nanosecond ; Ultrafast laser spectroscopy ; Optoelectronics ; Optics ; Nanotechnology ; Condensed matter physics ; Physics Magnetism in coordination complexes ; Porphyrin and Phthalocyanine Chemistry ; Electron Spin Resonance Studies
Authors: Karol Krzempek
Source: Applied Sciences (review, 2019-07-15)
Field: Chemistry — Spectroscopy

A Review of Photothermal Detection Techniques for Gas Sensing Applications Photothermal spectroscopy (PTS) is a technique used for determining the composition of liquids, solids and gases. In PTS, the sample is illuminated with a radiation source, and the thermal response of the analyte (e.g., refractive index) is analyzed to gain information about its content. Recent advances in this unique method of detecting gaseous samples show that photothermal gas spectroscopy can be an interesting alternative to commonly used absorption techniques. Moreover, if designed properly, sensors using PTS detection technique can not only reach sensitivities comparable with other, more complex techniques, but can significantly simplify the design of the sensor. In this review, recent developments in photothermal spectroscopy of gases will be summarized and discussed. Photothermal therapy ; Materials science ; Analyte ; Spectroscopy ; Photothermal spectroscopy ; Nanotechnology ; Chemistry ; Chromatography ; Physics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Bjørn H. Morland | Morten Tjelta | Truls Norby | Gaute Svenningsen
Source: International journal of greenhouse gas control (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

Acid reactions in hub systems consisting of separate non-reactive CO2 transport lines Impurity ; Pipeline transport ; Corrosion ; Autoclave ; Carbon dioxide ; Materials science ; Hydrogen sulphide ; Carbon capture and storage (timeline) ; STREAMS ; Nitrogen ; Environmental science ; Petroleum engineering ; Chemistry ; Waste management ; Chemical engineering ; Metallurgy ; Sulfur ; Environmental engineering ; Engineering ; Geology ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hendrik Naatz | Bella B. Manshian | Carla Ríos‐Luci | Vasiliki Tsikourkitoudi | Yiannis Deligiannakis | Johannes Birkenstock | Suman Pokhrel | Lutz Mädler | Stefaan J. Soenen
Source: Angewandte Chemie International Edition (article, 2019-11-22)
Field: Materials Science — Biomaterials

Model‐Based Nanoengineered Pharmacokinetics of Iron‐Doped Copper Oxide for Nanomedical Applications The progress in nanomedicine (NM) using nanoparticles (NPs) is mainly based on drug carriers for the delivery of classical chemotherapeutics. As low NM delivery rates limit therapeutic efficacy, an entirely different approach was investigated. A homologous series of engineered CuO NPs was designed for dual purposes (carrier and drug) with a direct chemical composition-biological functionality relationship. Model-based dissolution kinetics of CuO NPs in the cellular interior at post-exposure conditions were controlled through Fe-doping for intra/extra cellular Cu<sup>2+</sup> and biological outcome. Through controlled ion release and reactions taking place in the cellular interior, tumors could be treated selectively, in vitro and in vivo. Locally administered NPs enabled tumor cells apoptosis and stimulated systemic anti-cancer immune responses. We clearly show therapeutic effects without tumor cells relapse post-treatment with 6 % Fe-doped CuO NPs combined with myeloid-derived suppressor cell silencing. In vivo ; Nanomedicine ; Drug delivery ; Materials science ; In vitro ; Nanoparticle ; Biophysics ; Nanotechnology ; Drug ; Pharmacokinetics ; Cancer research ; Kinetics ; Drug carrier ; Chemistry ; Pharmacology ; Medicine ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Nanoparticles: synthesis and applications
Authors: Judith C. Madden | Gopal Pawar | M Cronin | Steven D. Webb | Yu‐Mei Tan | Alicia Paini
Source: Computational Toxicology (article, 2019-03-05)
Field: Chemistry — Spectroscopy

In silico resources to assist in the development and evaluation of physiologically-based kinetic models Bespoke ; In silico ; Computer science ; Biochemical engineering ; ADME ; Construct (python library) ; Software ; Data science ; Risk analysis (engineering) ; Chemistry ; Engineering ; Business ; Bioinformatics ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: Seoin Back | Junwoong Yoon | Nianhan Tian | Wen Zhong | Kevin Tran | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2019-05-21)
Field: Materials Science — Materials Chemistry

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis. Convolutional neural network ; Voronoi diagram ; Catalysis ; Computer science ; Polyhedron ; Throughput ; Surface (topology) ; Artificial neural network ; Nanotechnology ; Chemistry ; Chemical physics ; Materials science ; Artificial intelligence ; Combinatorics ; Mathematics ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Logan Ward | Ben Blaiszik | Ian Foster | Rajeev S. Assary | Badri Narayanan | Larry A. Curtiss
Source: arXiv (Cornell University) (article, 2019-06-07)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies, and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than 9 heavy atoms and 0.012 eV for a small set of molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed tradeoffs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface. Molecule ; Fidelity ; Test set ; Materials science ; Training set ; Set (abstract data type) ; High fidelity ; Energy (signal processing) ; Mean absolute error ; Quantum chemical ; Quantum ; Core (optical fiber) ; Organic molecules ; Atomic physics ; Computer science ; Machine learning ; Artificial intelligence ; Physics ; Mean squared error ; Statistics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sicong Ma | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Chemical Physics (article, 2019-08-05)
Field: Materials Science — Materials Chemistry

Heterogeneous catalysis from structure to activity via SSW-NN method Modern research on heterogeneous catalysis calls for new techniques and methods to resolve the active site structure and reaction intermediates at the atomic scale. Here, we overview our recent progress on large-scale atomistic simulation via potential energy surface (PES) global optimization based on neural network (NN) potential, focusing on methodology details and recent applications on catalysis. The combination of stochastic surface walking (SSW) global optimization and the NN method provides a convenient and automated way to generate the transferable and robust NN potential for global PES, which can be utilized to reveal new chemistry from the unknown region of PES with an affordable computational cost. The predictive power of SSW-NN is demonstrated in several examples, where the method is applied to explore the material crystal phases, to follow the surface structure evolution under high pressure hydrogen and to determine the ternary oxide phase diagram. The limitations and future directions to develop the SSW-NN method are also discussed. Potential energy surface ; Artificial neural network ; Ternary operation ; Catalysis ; Computer science ; Scale (ratio) ; Global optimization ; Nanotechnology ; Chemistry ; Materials science ; Algorithm ; Artificial intelligence ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Gareth F. Kennedy | Jie Zhang | Alan M. Bond
Source: Analytical Chemistry (article, 2019-08-30)
Field: Chemistry — Electrochemistry

Automatically Identifying Electrode Reaction Mechanisms Using Deep Neural Networks At present, electrochemical mechanisms are most commonly identified subjectively based on the experience of the researcher. This subjectivity is reflected in bias to particular mechanisms as well as lack of quantifiable confidence in the chosen mechanism compared to potential alternative mechanisms. In this paper we demonstrate that a deep neural network trained to recognize dc cyclic voltammograms for three commonly encountered mechanisms provides correct classifications within 5 ms without the problem of subjectivity. To mimic experimental data, the impact of noise, uncompensated resistance, and dependence on scan rate, factors that are relevant to practical studies, has also been investigated. Outcomes with two experimental data sets are also presented. Chemistry ; Artificial neural network ; Mechanism (biology) ; Subjectivity ; Electrode ; Noise (video) ; Artificial intelligence ; Electrochemistry ; Deep neural networks ; Pattern recognition (psychology) ; Biological system ; Computer science ; Physical chemistry Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: James L. McDonagh | Ardita Shkurti | David J. Bray | R. L. Anderson | Edward O. Pyzer‐Knapp
Source: Journal of Chemical Information and Modeling (article, 2019-09-24)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. The optimization process covers a discrete space of over 40 000 000 points, where each point represents the set of potentials that jointly forms a force field. We find that Bayesian optimization is capable of reaching a force field of comparable performance to the current state-of-the-art within 40 iterations. The best iteration during the optimization achieves an <i>R</i><sup>2</sup> of 0.78 and an RMSE of 0.63 log units on the training set of data, these metrics are maintained when a validation set is included, giving <i>R</i><sup>2</sup> of 0.8 and an RMSE of 0.65 log units. This work hence provides a proof-of-concept, expounding the utility of coupling automated and efficient global optimization with a top down data driven approach to force field parametrization. Compared to commonly employed alternative methods, Bayesian optimization offers global parameter searching and a low time to solution. Bayesian optimization ; Force field (fiction) ; Computer science ; Parametrization (atmospheric modeling) ; Dissipative particle dynamics ; Bayesian probability ; Field (mathematics) ; Algorithm ; Set (abstract data type) ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Geng Sun | Philippe Sautet
Source: Journal of Chemical Theory and Computation (article, 2019-08-29)
Field: Materials Science — Materials Chemistry

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks Data-driven machine learning force fields (MLFs) are more and more popular in atomistic simulations and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models, and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence, a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high-dimensional neural network potential (HDNNP) on Pt clusters of sizes from 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, and then, a more accurate but expensive level, using a hybrid functional or nonlocal vdW functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error <10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functionals. The overall speedup can be as large as 900 for a 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of the delta potential energy surface, and accordingly, one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multilayer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive nonlocal vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles. Computer science ; Artificial neural network ; Basis (linear algebra) ; Hybrid functional ; Exploit ; Artificial intelligence ; Basis set ; Density functional theory ; Set (abstract data type) ; Algorithm ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Fuel Cells and Related Materials