jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xu Shao | Chaoren Yan | Chao Wang | Chaoli Wang | Yue Cao | Yang Zhou | Ping Guan | Xiao Hu | Wenlei Zhu | Shichao Ding
Source: Nanoscale Advances (review, 2022-11-21)
Field: Materials Science — Materials Chemistry

Advanced nanomaterials for modulating Alzheimer's related amyloid aggregation Alzheimer's disease (AD) is a common neurodegenerative disease that brings about enormous economic pressure to families and society. Inhibiting abnormal aggregation of Aβ and accelerating the dissociation of aggregates is treated as an effective method to prevent and treat AD. Recently, nanomaterials have been applied in AD treatment due to their excellent physicochemical properties and drug activity. As a drug delivery platform or inhibitor, various excellent nanomaterials have exhibited potential in inhibiting Aβ fibrillation, disaggregating, and clearing mature amyloid plaques by enhancing the performance of drugs. This review comprehensively summarizes the advantages and disadvantages of nanomaterials in modulating amyloid aggregation and AD treatment. The design of various functional nanomaterials is discussed, and the strategies for improved properties toward AD treatment are analyzed. Finally, the challenges faced by nanomaterials with different dimensions in AD-related amyloid aggregate modulation are expounded, and the prospects of nanomaterials are proposed. Nanomaterials ; Photothermal therapy ; Nanotechnology ; Amyloid (mycology) ; Drug delivery ; Chemistry ; Biophysics ; Materials science ; Biology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Yen‐Ling Lee | Yung-Sheng Shih | Ziyu Chen | Fong‐Yu Cheng | Jingyu Lu | Yuan-Hua Wu | Ying‐Jan Wang
Source: Nanomaterials (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Toxic Effects and Mechanisms of Silver and Zinc Oxide Nanoparticles on Zebrafish Embryos in Aquatic Ecosystems The global application of engineered nanomaterials and nanoparticles (ENPs) in commercial products, industry, and medical fields has raised some concerns about their safety. These nanoparticles may gain access into rivers and marine environments through industrial or household wastewater discharge and thereby affect the ecosystem. In this study, we investigated the effects of silver nanoparticles (AgNPs) and zinc oxide nanoparticles (ZnONPs) on zebrafish embryos in aquatic environments. We aimed to characterize the AgNP and ZnONP aggregates in natural waters, such as lakes, reservoirs, and rivers, and to determine whether they are toxic to developing zebrafish embryos. Different toxic effects and mechanisms were investigated by measuring the survival rate, hatching rate, body length, reactive oxidative stress (ROS) level, apoptosis, and autophagy. Spiking AgNPs or ZnONPs into natural water samples led to significant acute toxicity to zebrafish embryos, whereas the level of acute toxicity was relatively low when compared to Milli-Q (MQ) water, indicating the interaction and transformation of AgNPs or ZnONPs with complex components in a water environment that led to reduced toxicity. ZnONPs, but not AgNPs, triggered a significant delay of embryo hatching. Zebrafish embryos exposed to filtered natural water spiked with AgNPs or ZnONPs exhibited increased ROS levels, apoptosis, and lysosomal activity, an indicator of autophagy. Since autophagy is considered as an early indicator of ENP interactions with cells and has been recognized as an important mechanism of ENP-induced toxicity, developing a transgenic zebrafish system to detect ENP-induced autophagy may be an ideal strategy for predicting possible ecotoxicity that can be applied in the future for the risk assessment of ENPs. Zebrafish ; Toxicity ; Ecotoxicity ; Autophagy ; Oxidative stress ; Environmental chemistry ; Nanotoxicology ; Embryo ; Hatching ; Acute toxicity ; Chemistry ; Aquatic ecosystem ; Apoptosis ; Cell biology ; Biology ; Toxicology ; Ecology ; Biochemistry Nanoparticles: synthesis and applications
Authors: Aref Abbasi Moud | Milad Kamkar | Amir Sanati‐Nezhad | S. Hossein Hejazi
Source: Cellulose (article, 2022-04-01)
Field: Materials Science — Biomaterials

Suspensions and hydrogels of cellulose nanocrystals (CNCs): characterization using microscopy and rheology Characterization (materials science) ; Rheology ; Self-healing hydrogels ; Materials science ; Cellulose ; Nanotechnology ; Colloid ; Nanomaterials ; Nanocellulose ; Nanocrystal ; Filtration (mathematics) ; Chemical engineering ; Polymer science ; Composite material ; Polymer chemistry Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Polysaccharides Composition and Applications
Authors: Luna Tie | Yinuo Ke | Yuxiu Gong | Wei‐xian Zhang | Zilong Deng
Source: International Journal of Biological Macromolecules (article, 2022-06-11)
Field: Materials Science — Biomaterials

Nanocellulose fine-tuned poly(acrylic acid) hydrogel for enhanced diclofenac removal Adsorption ; Chemical engineering ; Acrylic acid ; Mesoporous material ; Materials science ; Hydrogen bond ; Self-healing hydrogels ; Polyacrylic acid ; Polymerization ; Nanofiber ; Polyelectrolyte ; Polymer chemistry ; Chemistry ; Polymer ; Organic chemistry ; Copolymer ; Composite material ; Catalysis ; Molecule Advanced Cellulose Research Studies ; Hydrogels: synthesis, properties, applications ; 3D Printing in Biomedical Research
Authors: Yuchu Liu | Tong Liu | Xiaoyun Yan | Qing‐Yun Guo | Huanyu Lei | Zongwu Huang | Rui Zhang | Yu Wang | Jing Wang | Feng Liu | Fenggang Bian | E. W. Meijer | Takuzo Aida | Mingjun Huang | Stephen Z. D. Cheng
Source: Proceedings of the National Academy of Sciences (article, 2022-01-12)
Field: Materials Science — Materials Chemistry

Expanding quasiperiodicity in soft matter: Supramolecular decagonal quasicrystals by binary giant molecule blends The quasiperiodic structures in metal alloys have been known to depend on the existence of icosahedral order in the melt. Among different phases observed in intermetallics, decagonal quasicrystal (DQC) structures have been identified in many glass-forming alloys yet remain inaccessible in bulk-state condensed soft matters. Via annealing the mixture of two giant molecules, the binary system assemblies into an axial DQC superlattice, which is identified comprehensively with meso-atomic accuracy. Analysis indicates that the DQC superlattice is composed of mesoatoms with an unusually broad volume distribution. The interplays of submesoatomic (molecular) and mesoatomic (supramolecular) local packings are found to play a crucial role in not only the formation of the metastable DQC superlattice but also its transition to dodecagonal quasicrystal and Frank-Kasper σ superlattices. Quasicrystal ; Superlattice ; Icosahedral symmetry ; Metastability ; Intermetallic ; Supramolecular chemistry ; Materials science ; Quasiperiodic function ; Annealing (glass) ; Condensed matter physics ; Quasiperiodicity ; Chemical physics ; Crystallography ; Chemistry ; Physics ; Alloy ; Crystal structure Quasicrystal Structures and Properties ; Mineralogy and Gemology Studies ; Material Dynamics and Properties
Authors: Agni F. M. Gavriilidou | Kleitos Sokratous | Hsin‐Yung Yen | Luigi De Colibus
Source: Frontiers in Molecular Biosciences (review, 2022-04-14)
Field: Chemistry — Spectroscopy

High-Throughput Native Mass Spectrometry Screening in Drug Discovery The design of new therapeutic molecules can be significantly informed by studying protein-ligand interactions using biophysical approaches directly after purification of the protein-ligand complex. Well-established techniques utilized in drug discovery include isothermal titration calorimetry, surface plasmon resonance, nuclear magnetic resonance spectroscopy, and structure-based drug discovery which mainly rely on protein crystallography and, more recently, cryo-electron microscopy. Protein-ligand complexes are dynamic, heterogeneous, and challenging systems that are best studied with several complementary techniques. Native mass spectrometry (MS) is a versatile method used to study proteins and their non-covalently driven assemblies in a native-like folded state, providing information on binding thermodynamics and stoichiometry as well as insights on ternary and quaternary protein structure. Here, we discuss the basic principles of native mass spectrometry, the field's recent progress, how native MS is integrated into a drug discovery pipeline, and its future developments in drug discovery. Drug discovery ; Mass spectrometry ; Drug ; Throughput ; Chemistry ; High-throughput screening ; Computational biology ; Chromatography ; Pharmacology ; Medicine ; Biology ; Computer science ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Protein Structure and Dynamics
Authors: Matthew R. Carbone
Source: MRS Bulletin (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

When not to use machine learning: A perspective on potential and limitations Abstract The unparalleled success of artificial intelligence (AI) in the technology sector has catalyzed an enormous amount of research in the scientific community. It has proven to be a powerful tool, but as with any rapidly developing field, the deluge of information can be overwhelming, confusing, and sometimes misleading. This can make it easy to become lost in the same hype cycles that have historically ended in the periods of scarce funding and depleted expectations known as AI winters. Furthermore, although the importance of innovative, high-risk research cannot be overstated, it is also imperative to understand the fundamental limits of available techniques, especially in young fields where the rules appear to be constantly rewritten and as the likelihood of application to high-stakes scenarios increases. In this article, we highlight the guiding principles of data-driven modeling, how these principles imbue models with almost magical predictive power, and how they also impose limitations on the scope of problems they can address. Particularly, understanding when not to use data-driven techniques, such as machine learning, is not something commonly explored, but is just as important as knowing how to apply the techniques properly. We hope that the discussion to follow provides researchers throughout the sciences with a better understanding of when said techniques are appropriate, the pitfalls to watch for, and most importantly, the confidence to leverage the power they can provide. Graphical abstract Leverage (statistics) ; Scope (computer science) ; Computer science ; Data science ; Field (mathematics) ; Perspective (graphical) ; Artificial intelligence ; Management science ; Risk analysis (engineering) ; Engineering ethics ; Engineering ; Business Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Steph-Yves Louis | Edirisuriya M. Dilanga Siriwardane | Rajendra P. Joshi | Sadman Sadeed Omee | Neeraj Kumar | Jianjun Hu
Source: ACS Applied Materials & Interfaces (article, 2022-06-06)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks Performing first-principles calculations to discover electrodes' properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials' spatial information and only use predefined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed methods, which combine both atomic composition and atomic coordinates in 3D-space, improve the accuracy in voltage prediction significantly when compared to composition-based ML models. The first model directly learns the chemical reaction of electrodes and metal ions to predict their average voltage, whereas the second model combines electrodes' ML predicted formation energy (<i>E</i><sub>form</sub>) to compute their average voltage. Our <i>E</i><sub>form</sub>-based model demonstrates improved accuracy in transferability from our subset of learned Li ions to Na ions. Moreover, we predicted the theoretical voltage of 10 Na<sub><i>x</i></sub>MPO<sub>4</sub>F (M = Ti, Cr, Fe, Cu, Mn, Co, and Ni) fluorophosphate battery frameworks, which are unavailable in the Material Project database. It could be shown that we can expect average voltages higher than 3.1 V from those Na battery frameworks except from the NaTiPO<sub>4</sub>F and TiPO<sub>4</sub>F pair of electrodes, which offer an average voltage of 1.32 V. Materials science ; Electrode ; Artificial neural network ; Battery (electricity) ; Voltage ; Nanotechnology ; Artificial intelligence ; Electrical engineering ; Computer science ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fault Detection and Control Systems
Authors: Jeffrey G. Ethier | Rohan K. Casukhela | Joshua J. Latimer | Matthew Jacobsen | Boris Rasin | Maneesh K. Gupta | Luke A. Baldwin | Richard A. Vaia
Source: Macromolecules (article, 2022-03-24)
Field: Materials Science — Materials Chemistry

Predicting Phase Behavior of Linear Polymers in Solution Using Machine Learning The phase behavior of polymers in solution is crucial to many applications in polymer processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer solubility space for an arbitrary polymer–solvent pair and across a large composition range is challenging. Qualitative agreement is provided by many current theoretical models, but only a portion of the phase space is quantitatively predicted. Here, we utilize a curated database for binary polymer solutions comprised of 21 linear polymers, 61 solvents, and 97 unique polymer–solvent combinations (6524 cloud point temperatures) to construct phase diagrams from machine learning predictions. A generalizable feature vector is developed that includes component descriptors concatenated with state variables and an experimental data descriptor (phase direction). The impact of several types of descriptors (Morgan fingerprints, molecular descriptors, and Hansen solubility parameters) to encode polymer–solvent interactions is assessed. Hansen solubility parameters are also introduced as a means to understand the general breadth of the linear polymer–solvent space as well as the density and distribution of curated data. Two common regression algorithms (XGBoost and neural networks) establish the generality of the descriptors; provide a root mean squared error (RMSE) within 3 °C for predicted cloud points in the test set; and offer excellent agreement with upper and lower critical solubility curves, isopleths, and closed-loop phase behavior by a single model. The ability to extrapolate to polymers that are very dissimilar from the curated data is poor, but with as little as 20 cloud points or a single phase boundary, RMSE error of predictions are within 5 °C. This implies that the current model captures aspects of the underlying physics and can readily exploit correlations to reduce required data for additional polymer–solvent pairs. Finally, the model and data are accessible via the Polymer Property Predictor and Database (3PDb). Polymer ; Mean squared error ; Solubility ; Phase (matter) ; Support vector machine ; Biological system ; Data point ; Polymer architecture ; Thermodynamics ; Computer science ; Materials science ; Algorithm ; Machine learning ; Mathematics ; Chemistry ; Physics ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Minoru Kusaba | Chang Liu | Ryo Yoshida
Source: Computational Materials Science (article, 2022-05-19)
Field: Materials Science — Materials Chemistry

Crystal structure prediction with machine learning-based element substitution The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substitution-based CSP is demonstrated for a wide variety of crystal systems. Substitution (logic) ; Crystal structure ; Element (criminal law) ; Materials science ; Crystal (programming language) ; Crystallography ; Computer science ; Chemistry ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Onur Karaman
Source: Process Safety and Environmental Protection (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Three-dimensional graphene network supported nickel-cobalt bimetallic alloy nanocatalyst for hydrogen production by hydrolysis of sodium borohydride and developing of an artificial neural network modeling to forecast hydrogen production rate Sodium borohydride ; Bimetallic strip ; Hydrogen production ; Catalysis ; Hydrolysis ; Materials science ; Cobalt ; Chemical engineering ; Graphene ; Nickel ; Hydrogen ; Chemistry ; Nanotechnology ; Metallurgy ; Organic chemistry Hydrogen Storage and Materials ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Yewei Yu | Chen Zhang | Wenjing Cao | Xiaoliang Huang | Xiuyu Zhang | Miaolei Zhou
Source: Mechanical Systems and Signal Processing (article, 2022-11-19)
Field: Materials Science — Materials Chemistry

Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties Control theory (sociology) ; Convergence (economics) ; Artificial neural network ; Iterative learning control ; Nonlinear system ; Controller (irrigation) ; Actuator ; Computer science ; Control engineering ; Engineering ; Artificial intelligence ; Control (management) Shape Memory Alloy Transformations ; Iterative Learning Control Systems ; Piezoelectric Actuators and Control
Authors: Vaidish Sumaria | Luan Nguyen | Franklin Tao | Philippe Sautet
Source: Journal of the American Chemical Society (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Atomic-Scale Mechanism of Platinum Catalyst Restructuring under a Pressure of Reactant Gas Heterogeneous catalysis is key for chemical transformations. Understanding how catalysts' active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurately determining catalytic mechanisms and predictably developing catalysts. We combine in situ time-dependent scanning tunneling microscopy observations and machine-learning-accelerated first-principles atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create sub-nano-islands on the terraces, where under-coordinated sites are stabilized by the CO adsorbates. The fast and accurate machine-learning potential is key to enabling the exploration of tens of thousands of configurations for the CO-covered restructuring catalyst. These studies open an avenue to achieve an atomic-scale understanding of the structural dynamics of more complex metal nanoparticle catalysts under reaction conditions. Catalysis ; Chemistry ; Atomic units ; Restructuring ; Carbon monoxide ; Chemical physics ; Nanotechnology ; Heterogeneous catalysis ; Nanoscopic scale ; Platinum ; Chemical engineering ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Rhys E. A. Goodall | Abhijith S. Parackal | Felix A. Faber | Rickard Armiento | Alpha A. Lee
Source: Science Advances (article, 2022-07-27)
Field: Materials Science — Materials Chemistry

Rapid discovery of stable materials by coordinate-free coarse graining A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to be accurately predicted. However, most of these approaches use atomic coordinates as input and are thus bottlenecked by crystal structure identification when investigating previously unidentified materials. Our approach solves this bottleneck by coarse-graining the infinite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoff representations, coordinate-free sets of symmetry-related positions in a crystal, as the input to a machine learning model. Our model demonstrates exceptionally high precision in finding unknown theoretically stable materials, identifying 1569 materials that lie below the known convex hull of previously calculated materials from just 5675 ab initio calculations. Our approach opens up fundamental advances in computational materials discovery. Granularity ; Stability (learning theory) ; Computer science ; Property (philosophy) ; Crystal structure prediction ; Bottleneck ; Convex hull ; Space (punctuation) ; Ab initio ; Regular polygon ; Physics ; Crystal structure ; Mathematics ; Machine learning ; Chemistry ; Geometry ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Ibrahim Olanrewaju Alade | Mojeed O. Oyedeji | Mohd Amiruddin Abd Rahman | Tawfik A. Saleh
Source: Soft Computing (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Prediction of the lattice constants of pyrochlore compounds using machine learning Pyrochlore ; Electronegativity ; Ionic radius ; Support vector machine ; Artificial neural network ; Lattice constant ; Machine learning ; Artificial intelligence ; Lattice (music) ; Computer science ; Linear regression ; Materials science ; Algorithm ; Biological system ; Physics ; Ion ; Optics Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography