jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinn‐Liang Liu | Bob Eisenberg
Source: Entropy (review, 2020-05-14)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular Mean-Field Theory of Ionic Solutions: A Poisson-Nernst-Planck-Bikerman Model We have developed a molecular mean-field theory-fourth-order Poisson-Nernst-Planck-Bikerman theory-for modeling ionic and water flows in biological ion channels by treating ions and water molecules of any volume and shape with interstitial voids, polarization of water, and ion-ion and ion-water correlations. The theory can also be used to study thermodynamic and electrokinetic properties of electrolyte solutions in batteries, fuel cells, nanopores, porous media including cement, geothermal brines, the oceanic system, etc. The theory can compute electric and steric energies from all atoms in a protein and all ions and water molecules in a channel pore while keeping electrolyte solutions in the extra- and intracellular baths as a continuum dielectric medium with complex properties that mimic experimental data. The theory has been verified with experiments and molecular dynamics data from the gramicidin A channel, L-type calcium channel, potassium channel, and sodium/calcium exchanger with real structures from the Protein Data Bank. It was also verified with the experimental or Monte Carlo data of electric double-layer differential capacitance and ion activities in aqueous electrolyte solutions. We give an in-depth review of the literature about the most novel properties of the theory, namely Fermi distributions of water and ions as classical particles with excluded volumes and dynamic correlations that depend on salt concentration, composition, temperature, pressure, far-field boundary conditions etc. in a complex and complicated way as reported in a wide range of experiments. The dynamic correlations are self-consistent output functions from a fourth-order differential operator that describes ion-ion and ion-water correlations, the dielectric response (permittivity) of ionic solutions, and the polarization of water molecules with a single correlation length parameter. Nernst equation ; Planck ; Poisson distribution ; Mean field theory ; Statistical physics ; Physics ; Ionic bonding ; Field (mathematics) ; Mathematics ; Mathematical physics ; Quantum mechanics ; Ion ; Statistics ; Pure mathematics Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures
Authors: Hanae Arakawa | Kumi Takeda | Sayuri L. Higashi | Aya Shibata | Yoshiaki Kitamura | Masato Ikeda
Source: Polymer Journal (article, 2020-01-10)
Field: Materials Science — Biomaterials

Self-assembly and hydrogel formation ability of Fmoc-dipeptides comprising α-methyl-L-phenylalanine Abstract Various biofunctional hydrogel materials can be fabricated in aqueous media through the self-assembly of peptide derivatives, forming supramolecular nanostructures and their three-dimensional networks. In this study, we describe the self-assembly of new Fmoc-dipeptides comprising α-methyl-L-phenylalanine. We found that the position and number of methyl groups introduced onto the α carbons of the Fmoc-dipeptides by α-methyl-L-phenylalanine have a marked influence on the morphology of the supramolecular nanostructure as well as the hydrogel (network) formation ability. Supramolecular chemistry ; Phenylalanine ; Self-assembly ; Chemistry ; Self-healing hydrogels ; Peptide ; Aqueous medium ; Aqueous solution ; Nanostructure ; Polymer chemistry ; Nanotechnology ; Organic chemistry ; Amino acid ; Materials science ; Molecule ; Biochemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; RNA Interference and Gene Delivery
Authors: Benjamin R. Betzler | Brian Ade | Aaron Wysocki | Prashant Jain | Phillip Chesser | Michael S. Greenwood | Kurt A. Terrani
Source: Nuclear Engineering and Design (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

Transformational Challenge Reactor preconceptual core design studies Nuclear transmutation ; Systems engineering ; Nuclear engineering ; Manufacturing engineering ; Process engineering ; Engineering ; Mechanical engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Graphite, nuclear technology, radiation studies
Authors: Sabna Kotta | Hibah M. Aldawsari | Shaimaa M. Badr-Eldin | Nabil A. Alhakamy | Shadab Md | Anroop B. Nair | Pran Kishore Deb
Source: Frontiers in Molecular Biosciences (review, 2020-12-17)
Field: Materials Science — Materials Chemistry

Exploring the Potential of Carbon Dots to Combat COVID-19 Viral diseases are considered as a global burden. The eradication of viral diseases is always a challenging task in medical research due to the high infectivity and mutation capability of the virus. The ongoing COVID-19 pandemic is still not under control even after several months of the first reported case and global spread. Neither a specific drug nor a vaccine is available for public use yet. In the pursuit of a promising strategy, carbon dots could be considered as potential nanostructure against this viral pandemic. This review explores the possibility of carbon nano-dots to combat COVID-19 based on some reported studies. Carbon dots are photoluminescent carbon nanoparticles, smaller than 10 nm in dimension with a very attractive photostable and biocompatible properties which can be surfaced modified or functionalized. These photoluminescent tiny particles have captured much attention owing to their functionalization property and biocompatibility. In response to this pandemic outbreak, this review attempts to summarize the potential use of carbon dots in antiviral therapy with particular emphasis on their probable role in the battlefront against COVID-19 including their possible biosensing applications. Coronavirus disease 2019 (COVID-19) ; Pandemic ; Nanotechnology ; Isolation (microbiology) ; Virology ; Materials science ; Medicine ; Biology ; Bioinformatics ; Infectious disease (medical specialty) ; Disease Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications
Authors: Riikka Kuosmanen | Kari Rissanen | Elina Sievänen
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Biomaterials

Steroidal supramolecular metallogels The review deals with an expanding number of steroidal compounds that are capable of forming a metallogel providing a multitude of novel materials rich in their properties. The future of steroidal metallogels holds a myriad of potential applications as new intelligent materials. Detection of potentially harmful compounds without expensive instrumentation, entrapment of environmentally hazardous substances, and sensitive and selective nanomaterials represent only a few of these potential applications. This article reviews the design, synthesis, characterization, and applications of steroidal metallogels. Supramolecular chemistry ; Nanotechnology ; Nanomaterials ; Hazardous waste ; Characterization (materials science) ; Chemistry ; Biochemical engineering ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Molecule ; Waste management ; Engineering Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers
Authors: Bill K. Wheatle | Erick F. Fuentes | Nathaniel A. Lynd | Venkat Ganesan
Source: Macromolecules (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Design of Polymer Blend Electrolytes through a Machine Learning Approach We apply a machine learning (ML) technique to the multiobjective design of polymer blend electrolytes. In particular, we are interested in maximizing electrolyte performance measured by a combination of ionic transport (measured by ionic conductivity) and electrolyte mechanical properties (measured by viscosity) in a coarse-grained molecular dynamics framework. Recognizing the expense of evaluating each of these properties, we identify that the anionic mean-squared displacement and polymer relaxation time can serve as their proxies. By employing the ML approach known as Bayesian optimization, we identify a trade-off between ion transport and electrolyte mechanical properties as a function of varied design parameters, which include host molecular weight and polarity. Our results suggest that blend electrolytes whose hosts have unequal molecular weights, such as gel polymer electrolytes, rarely maximize electrolyte performance. Overall, our results suggest the potential of a framework to design high-performance electrolytes using a combination of molecular simulation and ML. Electrolyte ; Polymer ; Materials science ; Relaxation (psychology) ; Ionic conductivity ; Viscosity ; Chemical engineering ; Chemistry ; Composite material ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Yunqi Shao | Lisanne Knijff | Florian M. Dietrich | Kersti Hermansson | Chao Zhang
Source: Batteries & Supercaps (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning Abstract Batteries and supercapacitors are electrochemical energy storage systems which involve multiple time‐scales and length‐scales. In terms of the electrolyte which serves as the ionic conductor, a molecular‐level understanding of the corresponding transport phenomena, electrochemical (thermal) stability and interfacial properties is crucial for optimizing the device performance and achieving safety requirements. To this end, atomistic machine learning is a promising technology for bridging microscopic models and macroscopic phenomena. Here, we provide a timely snapshot of recent advances in this area. This includes technical considerations that are particularly relevant for modelling electrolytes as well as specific examples of both bulk electrolytes and associated interfaces. A perspective on methodological challenges and new applications is also discussed. Electrolyte ; Computer science ; Supercapacitor ; Materials science ; Nanotechnology ; Ionic bonding ; Bridging (networking) ; Electrochemical energy storage ; Electrochemistry ; Energy storage ; Ion ; Chemistry ; Physics ; Thermodynamics ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Mojtaba Haghighatlari | Gaurav Vishwakarma | Doaa Altarawy | R. Subramanian | Bhargava Urala Kota | Aditya Sonpal | Srirangaraj Setlur | Johannes Hachmann
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2020-01-30)
Field: Materials Science — Materials Chemistry

<i>ChemML</i> : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data Abstract ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under: Software &gt; Simulation Methods Computer and Information Science &gt; Chemoinformatics Structure and Mechanism &gt; Computational Materials Science Software &gt; Molecular Modeling Cheminformatics ; Workflow ; Computer science ; Informatics ; Data science ; Context (archaeology) ; Software ; Suite ; Software engineering ; Automation ; Domain (mathematical analysis) ; Key (lock) ; Toolchain ; Database ; Chemistry ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Anup Pandey | Reeju Pokharel
Source: Scripta Materialia (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Machine learning based surrogate modeling approach for mapping crystal deformation in three dimensions Materials science ; Deformation (meteorology) ; Ultimate tensile strength ; Face (sociological concept) ; Crystal plasticity ; Crystallite ; Single crystal ; Work (physics) ; Orientation (vector space) ; Surrogate model ; Plasticity ; Field (mathematics) ; Crystal (programming language) ; Sample (material) ; Cubic crystal system ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Computer science ; Composite material ; Metallurgy ; Crystallography ; Geometry ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Archit Datar | Yongchul G. Chung | Li‐Chiang Lin
Source: The Journal of Physical Chemistry Letters (article, 2020-06-08)
Field: Chemistry — Inorganic Chemistry

Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method Surface areas of porous materials such as metal-organic frameworks (MOFs) are commonly characterized using the Brunauer-Emmett-Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials. BET theory ; Nanoporous ; Benchmark (surveying) ; Monolayer ; Surface (topology) ; Materials science ; Specific surface area ; Adsorption ; Accessible surface area ; Characterization (materials science) ; Porosity ; Computer science ; Biological system ; Nanotechnology ; Composite material ; Mathematics ; Chemistry ; Cartography ; Geometry ; Geography Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Stephen Whitelam | Isaac Tamblyn
Source: Physical review. E (article, 2020-05-11)
Field: Materials Science — Materials Chemistry

Learning to grow: Control of material self-assembly using evolutionary reinforcement learning We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories, networks learn to change temperature and chemical potential in order to promote the assembly of desired structures or choose between competing polymorphs. In the first case, networks reproduce in a qualitative sense the results of previously known protocols, but faster and with higher fidelity; in the second case they identify strategies previously unknown, from which we can extract physical insight. Networks that take as input the elapsed time of the simulation or microscopic information from the system are both effective, the latter more so. The evolutionary scheme we have used is simple to implement and can be applied to a broad range of examples of experimental self-assembly, whether or not one can monitor the experiment as it proceeds. Our results have been achieved with no human input beyond the specification of which order parameter to promote, pointing the way to the design of synthesis protocols by artificial intelligence. Reinforcement learning ; Fidelity ; Computer science ; Artificial neural network ; Range (aeronautics) ; High fidelity ; Simple (philosophy) ; Control (management) ; Artificial intelligence ; Evolutionary robotics ; Evolutionary algorithm ; Order (exchange) ; Distributed computing ; Engineering Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Silvan Käser | Oliver T. Unke | Markus Meuwly
Source: New Journal of Physics (article, 2020-03-20)
Field: Materials Science — Materials Chemistry

Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces Abstract The ‘ in silico ’ exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomistic level. Recently, machine learning tools have gained a lot of attention in the field of molecular sciences and simulations and are increasingly used to investigate the dynamics of such systems. Among the various approaches, artificial neural networks (NNs) are one promising tool to learn a representation of potential energy surfaces. This is done by formulating the problem as a mapping from a set of atomic positions x and nuclear charges Z i to a potential energy V ( x ). Here, a fully-dimensional, reactive neural network representation for malonaldehyde (MA), acetoacetaldehyde (AAA) and acetylacetone (AcAc) is learned. It is used to run finite-temperature molecular dynamics simulations, and to determine the infrared spectra and the hydrogen transfer rates for the three molecules. The finite-temperature infrared spectrum for MA based on the NN learned on MP2 reference data provides a realistic representation of the low-frequency modes and the H-transfer band whereas the CH vibrations are somewhat too high in frequency. For AAA it is demonstrated that the IR spectroscopy is sensitive to the position of the transferring hydrogen at either the OCH- or OCCH 3 end of the molecule. For the hydrogen transfer rates it is demonstrated that the O–O vibration (at ∼250 cm −1 ) is a gating mode and largely determines the rate at which the hydrogen is transferred between the donor and acceptor. Finally, possibilities to further improve such NN-based potential energy surfaces are explored. They include the transferability of an NN-learned energy function across chemical species (here methylation) and transfer learning from a lower level of reference data (MP2) to a higher level of theory (pair natural orbital-LCCSD(T)). Physics ; Artificial neural network ; Infrared spectroscopy ; Spectroscopy ; Molecular dynamics ; Molecular vibration ; Representation (politics) ; Hydrogen ; Molecule ; Atomic physics ; Chemical physics ; Statistical physics ; Biological system ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Vaidehi A. Patil | Kristyn S. Masters
Source: Bioengineering (review, 2020-12-16)
Field: Materials Science — Biomaterials

Engineered Collagen Matrices Collagen is the most abundant protein in mammals, accounting for approximately one-third of the total protein in the human body. Thus, it is a logical choice for the creation of biomimetic environments, and there is a long history of using collagen matrices for various tissue engineering applications. However, from a biomaterial perspective, the use of collagen-only scaffolds is associated with many challenges. Namely, the mechanical properties of collagen matrices can be difficult to tune across a wide range of values, and collagen itself is not highly amenable to direct chemical modification without affecting its architecture or bioactivity. Thus, many approaches have been pursued to design scaffold environments that display critical features of collagen but enable improved tunability of physical and biological characteristics. This paper provides a brief overview of approaches that have been employed to create such engineered collagen matrices. Specifically, these approaches include blending of collagen with other natural or synthetic polymers, chemical modifications of denatured collagen, de novo creation of collagen-mimetic chains, and reductionist methods to incorporate collagen moieties into other materials. These advancements in the creation of tunable, engineered collagen matrices will continue to enable the interrogation of novel and increasingly complex biological questions. Biomaterial ; Tissue engineering ; Scaffold ; Nanotechnology ; Matrix (chemical analysis) ; Materials science ; Chemistry ; Computer science ; Biomedical engineering ; Engineering Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research
Authors: Eldar M. Khabushev | Dmitry V. Krasnikov | Julia V. Kolodiazhnaia | Anton V. Bubis | Albert G. Nasibulin
Source: Carbon (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

Structure-dependent performance of single-walled carbon nanotube films in transparent and conductive applications Carbon nanotube ; Materials science ; Nanotube ; Raman spectroscopy ; Electrical conductor ; Nanotechnology ; Sheet resistance ; Phase (matter) ; Composite material ; Layer (electronics) ; Optics ; Chemistry Carbon Nanotubes in Composites ; Graphene research and applications ; Chemical and Physical Properties of Materials
Authors: Sebastian A. Kube | Wenting Xing | Arvind R. Kalidindi | Sungwoo Sohn | Amit Datye | Dor Amram | Christopher A. Schuh | Jan Schroers
Source: Acta Materialia (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Combinatorial study of thermal stability in ternary nanocrystalline alloys Nanocrystalline alloys can be stabilized through selective grain boundary segregation of specific solute element additions. Increasing attention is being paid to ternary and higher order systems, where complex interactions govern segregation. To efficiently study the large composition spaces of such systems, we apply a high-throughput combinatorial technique revealing nanocrystalline stability through composition-grain-size maps. We compare two systems with distinct binary and ternary alloy interactions: In Pt–AuAg both binaries are expected to be stable, whereas in Pt–AuPd the Pt–Pd binary is unstable and Au-induced co-segregation of Pd was previously reported. For ternary Pt–AuAg we find excellent thermal stability throughout. The Pt–AuPd system, by contrast, divides into an unstable regime, where Pd solute dominates and precipitates, and a stable regime, where Au solute dominates and retains Pd in the grain boundary. Overall, by combining current theory and the introduced combinatorial approach, stable multicomponent nanocrystalline composition spaces can be rapidly determined. Nanocrystalline material ; Materials science ; Ternary operation ; Grain boundary ; Thermal stability ; Alloy ; Nanocrystal ; Binary number ; Thermodynamics ; Grain size ; Stability (learning theory) ; Chemical physics ; Chemical engineering ; Metallurgy ; Nanotechnology ; Microstructure ; Physics ; Mathematics Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; Advanced Materials Characterization Techniques