jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nourhan Hassan | Thomas Krieg | Max Zinser | Kai‐Uwe Schröder | Nadja Kröger
Source: Polymers (review, 2023-09-22)
Field: Materials Science — Biomaterials

An Overview of Scaffolds and Biomaterials for Skin Expansion and Soft Tissue Regeneration: Insights on Zinc and Magnesium as New Potential Key Elements The utilization of materials in medical implants, serving as substitutes for non-functional biological structures, supporting damaged tissues, or reinforcing active organs, holds significant importance in modern healthcare, positively impacting the quality of life for millions of individuals worldwide. However, certain implants may only be required temporarily to aid in the healing process of diseased or injured tissues and tissue expansion. Biodegradable metals, including zinc (Zn), magnesium (Mg), iron, and others, present a new paradigm in the realm of implant materials. Ongoing research focuses on developing optimized materials that meet medical standards, encompassing controllable corrosion rates, sustained mechanical stability, and favorable biocompatibility. Achieving these objectives involves refining alloy compositions and tailoring processing techniques to carefully control microstructures and mechanical properties. Among the materials under investigation, Mg- and Zn-based biodegradable materials and their alloys demonstrate the ability to provide necessary support during tissue regeneration while gradually degrading over time. Furthermore, as essential elements in the human body, Mg and Zn offer additional benefits, including promoting wound healing, facilitating cell growth, and participating in gene generation while interacting with various vital biological functions. This review provides an overview of the physiological function and significance for human health of Mg and Zn and their usage as implants in tissue regeneration using tissue scaffolds. The scaffold qualities, such as biodegradation, mechanical characteristics, and biocompatibility, are also discussed. Biocompatibility ; Regeneration (biology) ; Materials science ; Scaffold ; Biomedical engineering ; Nanotechnology ; Biocompatible material ; Tissue engineering ; Metallurgy ; Medicine Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Yosuke Akae | Jun Sawada | K. Nakajima | Toshikazu Takata
Source: Angewandte Chemie International Edition (article, 2023-05-10)
Field: Chemistry — Organic Chemistry

The Effect of the Axle End Structure and Number of Through‐Space Bonds on the Properties of Rotaxane Crosslinked Polymers A rotaxane crosslinker (RC) is known to toughen the resulting rotaxane crosslinked polymer (RCP) via a stress dispersion effect that is attributed to the movable nature of the crosslinking structure. To evaluate this toughening mechanism in detail, a series of structure-definite RCs equipped with different axle end structures or different numbers of wheel components were synthesized, and subjected to free radical polymerization with a vinyl monomer to obtain RCPs. Analyses of the obtained RCPs revealed that the size of the axle end structure should be well-balanced to produce a strong toughening effect, and a [3]rotaxane crosslinker works more effectively than [2]rotaxane to toughen RCPs. The mobility of the crosslinking points, in terms of rotational and flipping movements, was more crucial to toughening the RCP than that of translational movement along the axle. The first observation of the above crucial findings proved the utility of the systematic molecular design used in this study. Rotaxane ; Materials science ; Toughening ; Axle ; Polymer ; Monomer ; Composite material ; Polymer chemistry ; Toughness ; Molecule ; Chemistry ; Structural engineering ; Organic chemistry Supramolecular Chemistry and Complexes ; Advanced Polymer Synthesis and Characterization ; Silicone and Siloxane Chemistry
Authors: Beata Kaczmarek | Izabela Polkowska | Marcin Małek | Janusz Kluczyński | Katarzyna Paździor‐Czapula | Marcin Wekwejt | Anna Michno | Anna Ronowska | Anna Pałubicka | Beata Nowicka | Iwona Otrocka‐Domagała
Source: Scientific Reports (article, 2023-06-20)
Field: Materials Science — Biomaterials

The characterization of collagen-based scaffolds modified with phenolic acids for tissue engineering application The aim of the experiment was to study the morphology of collagen-based scaffolds modified by caffeic acid, ferulic acid, and gallic acid, their swelling, and degradation rate, as well as the biological properties of scaffolds, such as antioxidant activity, hemo- and cytocompatibility, histological observation, and antibacterial properties. Scaffolds based on collagen with phenolic acid showed higher swelling rate and enzymatic stability compared to scaffolds based on pure collagen, and the radical scavenging activity was in the range 85-91%. All scaffolds were non-hemolytic and compatible with surrounding tissues. Collagen modified by ferulic acid showed potentially negative effects on hFOB cells as a significantly increased LDH release was found, but all of the studied materials had antimicrobial activity against Staphylococcus aureus and Escherichia coli. It may be assumed that phenolic acids, such as caffeic, ferulic, and gallic acid, are modifiers and provide novel biological properties of collagen-based scaffolds. This paper provides the summarization and comparison of the biological properties of scaffolds based on collagen modified with three different phenolic acids. Gallic acid ; Ferulic acid ; Caffeic acid ; Chemistry ; Swelling ; Antioxidant ; Phenolic acid ; Biochemistry ; Materials science Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research
Authors: Krisztina Karádi | Thanh-Truc Nguyen | Adél Anna Ádám | Kornélia Baán | András Sápi | Ákos Kukovecz | Zoltán Kónya | Pál Sipos | István Pálinkó | Gábor Varga
Source: Green Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Structure–activity relationships of LDH catalysts for the glucose-to-fructose isomerisation in ethanol Hydrotalcite catalysed liquid phase glucose isomerisation in EtOH. Isomerization ; Hydrotalcite ; Ethanol ; Chemistry ; Catalysis ; Fructose ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Catalysis for Biomass Conversion ; Mesoporous Materials and Catalysis
Authors: Yuwen Meng | Taco Nicolaï
Source: Journal of Colloid and Interface Science (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

The effect of the contact angle on particle stabilization and bridging in water-in-water emulsions Contact angle ; Chemical engineering ; Emulsion ; Materials science ; Ethylene oxide ; Phase (matter) ; Particle (ecology) ; Polymer ; Aqueous solution ; Adsorption ; Aqueous two-phase system ; Polymer chemistry ; Chemistry ; Composite material ; Copolymer ; Organic chemistry Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Surfactants and Colloidal Systems
Authors: Nathan J. Szymanski | Zhengyan Lun | Jue Liu | Ethan C. Self | Christopher J. Bartel | Jagjit Nanda | Bin Ouyang | Gerbrand Ceder
Source: Chemistry of Materials (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Accurate interpretation of experimental PDF data is critically reliant on the development of structural models that can account for local variations in site occupancies and bond lengths. To this end, we outline an approach to model SRO using first-principles calculations based on the cluster-expansion formalism. These methods are validated on neutron scattering data from two disordered rocksalt oxyfluorides, Li1.3Mn0.4Ti0.3O1.7F0.3 and Li1.3Mn0.4Nb0.2Ti0.1O1.7F0.3. For each composition, we demonstrate that an average structure without any SRO fails to reproduce several key features in the experimental PDF. To pinpoint the origin of the suspected SRO in these materials, configurational and displacive effects were separately investigated using two disparate models. Special quasi-random structures were relaxed using density functional theory to account for local changes in bond lengths while maintaining a near-random ionic configuration. This leads to slightly improved accuracy but still misrepresents asymmetry in the first few peaks of the PDF. Monte Carlo simulations were performed to model configurational SRO on a fixed lattice, which by itself is shown to have a minimal influence on the PDF. Instead, we find that it is the bond length relaxations within environments created by SRO which controls the details of the PDF, thereby highlighting the subtle but important coupling between configurational and displacive SRO in disordered materials. Pair distribution function ; Statistical physics ; Asymmetry ; Bond length ; Monte Carlo method ; Ionic bonding ; Distribution function ; Cluster expansion ; Lattice (music) ; Density functional theory ; Materials science ; Chemical physics ; Physics ; Chemistry ; Computational chemistry ; Molecule ; Thermodynamics ; Ion ; Mathematics ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Xiaoyu Wang | Joe Finney | Aaron L. Sharpe | Linsey K. Rodenbach | Connie L. Hsueh | Kenji Watanabe | Takashi Taniguchi | M. A. Kastner | Oskar Vafek | David Goldhaber‐Gordon
Source: Proceedings of the National Academy of Sciences (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Unusual magnetotransport in twisted bilayer graphene from strain-induced open Fermi surfaces Anisotropic hopping in a toy Hofstadter model was recently invoked to explain a rich and surprising Landau spectrum measured in twisted bilayer graphene away from the magic angle. Suspecting that such anisotropy could arise from unintended uniaxial strain, we extend the Bistritzer-MacDonald model to include uniaxial heterostrain and present a detailed analysis of its impact on band structure and magnetotransport. We find that such strain strongly influences band structure, shifting the three otherwise-degenerate van Hove points to different energies. Coupled to a Boltzmann magnetotransport calculation, this reproduces previously unexplained nonsaturating [Formula: see text] magnetoresistance over broad ranges of density near filling [Formula: see text] and predicts subtler features that had not been noticed in the experimental data. In contrast to these distinctive signatures in longitudinal resistivity, the Hall coefficient is barely influenced by strain, to the extent that it still shows a single sign change on each side of the charge neutrality point-surprisingly, this sign change no longer occurs at a van Hove point. The theory also predicts a marked rotation of the electrical transport principal axes as a function of filling even for fixed strain and for rigid bands. More careful examination of interaction-induced nematic order versus strain effects in twisted bilayer graphene could thus be in order. Condensed matter physics ; Physics ; Materials science Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Yuzhi Xu | Jiankai Ge | Cheng‐Wei Ju
Source: Energy Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning in energy chemistry: introduction, challenges and perspectives This review explores machine learning's role in energy chemistry, spanning organic photovoltaics, perovskites, catalysis, and batteries, highlighting its potential to accelerate eco-friendly, sustainable energy development. Chemistry ; Computer science ; Data science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Photocatalysis Techniques
Authors: Emily M. Williamson | Richard L. Brutchey
Source: Inorganic Chemistry (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques. Control (management) ; Computer science ; Design of experiments ; Rational design ; Machine learning ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Samantha Stuart | Jeffrey Watchorn | Frank Gu
Source: npj Computational Materials (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials Abstract It has proved challenging to represent the behavior of polymeric macromolecules as machine learning features for biomaterial interaction prediction. There are several approaches to this representation, yet no consensus for a universal representational framework, in part due to the sensitivity of biomacromolecular interactions to polymer properties. To help navigate the process of feature engineering, we provide an overview of popular classes of data representations for polymeric biomaterial machine learning while discussing their merits and limitations. Generally, increasing the accessibility of polymeric biomaterial feature engineering knowledge will contribute to the goal of accelerating clinical translation from biomaterials discovery. Biomaterial ; Feature (linguistics) ; Sizing ; Computer science ; Representation (politics) ; Artificial intelligence ; Process (computing) ; Feature engineering ; Machine learning ; Nanotechnology ; Materials science ; Chemistry ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Damian M. Wilary | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2023-09-20)
Field: Materials Science — Materials Chemistry

ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes Knowledge in the chemical domain is often disseminated graphically via chemical reaction schemes. The task of describing chemical transformations is greatly simplified by introducing reaction schemes that are composed of chemical diagrams and symbols. While intuitively understood by any chemist, like most graphical representations, such drawings are not easily understood by machines; this poses a challenge in the context of data extraction. Currently available tools are limited in their scope of extraction and require manual preprocessing, thus slowing down the speed of data extraction. We present a new tool, ReactionDataExtractor v2.0, which uses a combination of neural networks and symbolic artificial intelligence to effectively remove this barrier. We have evaluated our tool on a test set composed of reaction schemes that were taken from open-source journal articles and realized F1 score metrics between 75 and 96%. These evaluation metrics can be further improved by tuning our object-detection models to a specific chemical subdomain thanks to a data-driven approach that we have adopted with synthetically generated data. The system architecture of our tool is modular, which allows it to balance speed and accuracy to afford an autonomous, high-throughput solution for image-based chemical data extraction. Computer science ; Preprocessor ; Context (archaeology) ; Modular design ; Artificial intelligence ; Cheminformatics ; Data mining ; Artificial neural network ; Data extraction ; Machine learning ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Scientific Computing and Data Management
Authors: Ziyang Li | Jiani Huang | Mayur Naik
Source: Proceedings of the ACM on Programming Languages (article, 2023-06-06)
Field: Materials Science — Materials Chemistry

Scallop: A Language for Neurosymbolic Programming We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability. Computer science ; Datalog ; Scallop ; Artificial intelligence ; Programming language ; Suite ; Automated reasoning ; Interpretability ; Inductive logic programming ; Recursion (computer science) ; Theoretical computer science ; Natural language processing Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Alexey Illarionov | Serzhan Sakipov | Leonid Pereyaslavets | Igor V. Kurnikov | Ganesh Kamath | Oleg Butin | Е. Н. Воронина | Ilya Ivahnenko | Igor Leontyev | Grzegorz Nawrocki | Mikhail Darkhovskiy | Michael Olevanov | Yevhen K. Cherniavskyi | Christopher Lock | Sean Greenslade | Subramanian K. R. S. Sankaranarayanan | Maria G. Kurnikova | Jeffrey J. Potoff | Roger D. Kornberg | Michael Levitt | Boris Fain
Source: Journal of the American Chemical Society (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day. Intermolecular force ; Water dimer ; Force field (fiction) ; Chemistry ; Hamiltonian (control theory) ; Statistical physics ; Polarizability ; Quantum ; Solvation ; Ab initio ; Artificial neural network ; Interaction energy ; Molecule ; Quantum mechanics ; Physics ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kamal Choudhary | Bobby G. Sumpter
Source: AIP Advances (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Can a deep-learning model make fast predictions of vacancy formation in diverse materials? The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (Evac) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 Evac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 Evac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data. Vacancy defect ; Density functional theory ; Materials science ; Semiconductor ; Work (physics) ; Point (geometry) ; Artificial neural network ; Computer science ; Monolayer ; Statistical physics ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Artificial intelligence ; Thermodynamics ; Condensed matter physics ; Optoelectronics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials