jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stanley Lo | Martin Seifrid | Théophile Gaudin | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Augmenting Polymer Datasets by Iterative Rearrangement One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the successes of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three experimental polymer datasets and compare them to common molecular representations. Data augmentation improves deep learning property prediction performance compared to equivalent (non-augmented) representations. In datasets where the target property is primarily influenced by the polymer sequence rather than experimental parameters, this data augmentation technique provides the molecular embedding with more information to improve property prediction accuracy. Representation (politics) ; Property (philosophy) ; Sequence (biology) ; Embedding ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Chenru Duan | Aditya Nandy | Ralf Meyer | N. Arunachalam | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-07-21)
Field: Materials Science — Materials Chemistry

A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density functional approximation (DFA) with universal accuracy has been identified, leading to uncertainty in the quality of data generated from DFT. With electron density fitting and transfer learning, we build a DFA recommender that selects the DFA with the lowest expected error with respect to gold standard but cost-prohibitive coupled cluster theory in a system-specific manner. We demonstrate this recommender approach on vertical spin-splitting energy evaluation for challenging transition metal complexes. Our recommender predicts top-performing DFAs and yields excellent accuracy (ca. 2 kcal/mol) for chemical discovery, outperforming both individual transfer learning models and the single best functional in a set of 48 DFAs. We demonstrate the transferability of the DFA recommender to experimentally synthesized compounds with distinct chemistry. Density functional theory ; Transferability ; Computer science ; Recommender system ; Set (abstract data type) ; Quality (philosophy) ; Algorithm ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Raffaella Ferraiuolo | Michela Alfè | Valentina Gargiulo | Giovanni Piero Pepe | F. Tafuri | Alessandro Pezzella | G. Ausanio | Domenico Montemurro
Source: Coatings (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Insights into the Electrical Characterization of Graphene-like Materials from Carbon Black A new class of graphene-related materials (GRMs) obtained as water suspensions through a two-step oxidation/reduction of a nanostructured carbon black, namely graphene-like (GL) materials, has recently emerged. GL materials undergo self-assembly in thin amorphous films after drying upon drop-casting deposition on different surfaces. The GL films, with thicknesses of less than a micron, were composed of clusters of nanoparticles each around 40 nm in size. The exploitation of the GL films for different options (e.g., bioelectronic, sensoristic, functional filler in composite) requires a deep characterization of the material in terms of their electric transport properties and their possible interaction with the surface on which they are deposited. In this work, a careful electrical characterization of GL films was performed at room temperature and the results were compared with those achieved on films of benchmark graphenic materials, namely graphene oxide (GO) materials, obtained by the exfoliation of graphite oxide, which differ both in morphology and in oxidation degree. The results indicate a non-linear current–voltage relationship for all the investigated films. The extrapolated dielectric constant (ε) values of the investigated GRMs (GL and GO materials) agree with the experimental and theoretically predicted values reported in the literature (ε~2–15). Because similar conductance values were obtained for the GL materials deposited on glass and silicon oxide substrates, no significant interactions of GL materials with the two different substrates were highlighted. These results are the starting point for boosting a feasible use of GL materials in a wide spectrum of applications, ranging from electronics to optics, sensors, membranes, functional coatings, and biodevices. Graphene ; Materials science ; Carbon black ; Oxide ; Graphite ; Characterization (materials science) ; Dielectric ; Nanotechnology ; Amorphous solid ; Amorphous carbon ; Composite material ; Chemical engineering ; Optoelectronics ; Metallurgy ; Organic chemistry ; Chemistry ; Natural rubber Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Gamal Zayed | Hanaa Mohammed | Shaaban K. Osman
Source: Al-Azhar Journal of Pharmaceutical Sciences/Al-Azhar Journal of Pharmaceutical Sciences (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

NANOCRYSTALLIZATION OF DAPSONE; A NOVEL APPROACH TO BOOST SOLUBILITY, DISSOLUTION, AND IN-VITRO ANTI-INFLAMMATORY ACTIVITY The low aqueous solubility of dapsone (DPS), a widely used anti-acne drug, greatly limits its biological efficacy, especially the anti-inflammatory effect. This article has applied the nanocrystallization technique to increase the surface area which consequently improves the solubility, dissolution, and anti-inflammatory effect of dapsone. Span-stabilized dapsone nanocrystals (DPS-NCs) have been prepared using solvent-antisolvent crystallization technique. The obtained nanocrystals (NCs) were characterized by FTIR, DSC, and X-ray. The morphology, particles surface, sizes, and surface charge of DPS-NCs were examined by TEM, SEM, and zetasizer, respectively. Additionally, the in vitro solubility, dissolution, and anti-inflammatory of the DPS-NCs were investigated. DPS-NCs were spherical in shape with smooth surfaces and had a hydrodynamic particle size of about 149±4.73 nm. The saturated aqueous solubility of DPS-NCs was 1.8 ± 0.05 mg/mL which was about 6 fold higher than that of a pure drug and showed improved in vitro dissolution. The used excipients did not interact chemically with the drug as indicated by DSC and FTIR. X-ray diffraction and TEM imaging confirmed that the produced nanocrystals (NCs) still have a certain degree of crystallinity and did not completely convert to the amorphous state. The in vitro anti-inflammatory of DPS-NCs greatly improved compared to pure drug. DSP-NCs could be considered a novel, promising, and effective therapeutic option for the treatment of acne as well as for relieving the accompanying inflammation. Dissolution ; Solubility ; Dapsone ; In vitro ; Chemistry ; Materials science ; Pharmacology ; Medicine ; Biochemistry ; Immunology ; Organic chemistry Nanoparticles: synthesis and applications
Authors: Sebastian Dahle | Kavyashree Srinivasa | Jure Žigon | Arnaud Maxime Cheumani Yona | Georg Avramidis | Wolfgang Viöl | Marko Petrič
Source: Frontiers in Materials (article, 2022-01-17)
Field: Materials Science — Surfaces, Coatings and Films

Influence of Air Plasma Pretreatments on Mechanical Properties in Metal-Reinforced Laminated Wood The use of wood-based materials in building and construction is constantly increasing as environmental aspects and sustainability gain importance. For structural applications, however, there are many examples where hybrid material systems are needed to fulfil the specific mechanical requirements of the individual application. In particular, metal reinforcements are a common solution to enhance the mechanical properties of a wooden structural element. Metal-reinforced wood components further help to reduce cross-sectional sizes of load-bearing structures, improve the attachment of masonry or other materials, enhance the seismic safety and tremor dissipation capacity, as well as the durability of the structural elements in highly humid environments and under high permanent mechanical load. A critical factor to achieve these benefits, however, is the mechanical joint between the different material classes, namely the wood and metal parts. Currently, this joint is formed using epoxy or polyurethane (PU) adhesives, the former yielding highest mechanical strengths, whereas the latter presents a compromise between mechanical and economical constraints. Regarding sustainability and economic viability, the utilization of different adhesive systems would be preferable, whereas mechanical stabilities yielded for metal-wood joints do not permit for the use of other common adhesive systems in such structural applications. This study extends previous research on the use of non-thermal air plasma pretreatments for the formation of wood-metal joints. The plasma treatments of Norway spruce [ Picea abies (L.) Karst.] wood and anodized (E6/EV1) aluminum AlMgSi0.5 (6060) F22 were optimized, using water contact angle measurements to determine the effect and homogeneity of plasma treatments. The adhesive bond strengths of plasma-pretreated and untreated specimens were tested with commercial 2-component epoxy, PU, melamine-urea formaldehyde (MUF), polyvinyl acetate (PVAc), and construction adhesive glue systems. The influence of plasma treatments on the mechanical performance of the compounds was evaluated for one selected glue system via bending strength tests. The impact of the hybrid interface between metal and wood was isolated for the tests by using five-layer laminates from three wood lamellae enclosing two aluminum plates, thereby excluding the influence of congeneric wood-wood bonds. The effect of the plasma treatments is discussed based on the chemical and physical modifications of the substrates and the respective interaction mechanisms with the glue systems. Materials science ; Adhesive ; Composite material ; Epoxy ; Durability ; Engineered wood Surface Modification and Superhydrophobicity ; Advanced Cellulose Research Studies ; Additive Manufacturing and 3D Printing Technologies
Authors: Shubhashish Shubhashish | Sarah J. Karasik | Inosh Perera | Seth March | Luisa F. Posada | Ali Mansour | Steven L. Suib
Source: Microporous and Mesoporous Materials (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

Stoichiometric metal nitration based novel green synthesis of mesoporous metal oxides and their enhanced heterogeneity Inorganic chemistry ; Oxide ; Chemisorption ; Catalysis ; Chemistry ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Kübra KARABACAK | İbrahim Berk ÇAVUŞ | Onur KIYILI
Source: Uludağ University Journal of The Faculty of Engineering (article, 2022-12-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

POLİMERLERİN ELEKTRİK İLETKENLİĞİ İletken polimerler ve karışımları, diğer iletken malzemelerle kıyaslandığında, düşük yoğunluğa sahip, korozyon direnci yüksek işlenmesi kolay, düşük maliyetli ve kullanılan katkılarla birlikte metal ile kıyaslanabilir özellikte iletkenlik değerine sahip olmaktadır. Son yıllarda, metal – plastik değişimi üzerine birçok Ar-Ge çalışması yapılmıştır. Bu çalışmalarda, havacılık, uzay ve savunma sanayi, otomotiv ve otomotiv yan sanayinde kullanılmak üzere önemli polimer ve karışımları geliştirilmiştir. Metallerin en önemli özelliklerinden elektrik iletkenliği, elektromanyetik kalkanlama ve radyasyon emme, geliştirilen polimer ve karışımları ile sağlanabilmektedir. Polimer malzemelerin diğer avantajları ile, gelecekte bu sektörlerde daha fazla metalin yerini alacaktır. Bu çalışmada, polimerler ve karışımlarının elektriksel özellikleri ile ilgili özellikle son yıllarda yapılan ulusal ve uluslararası araştırma makaleleri derlenmiştir. Physics ; Nuclear chemistry ; Chemistry Electromagnetic wave absorption materials ; Material Properties and Applications ; Tribology and Wear Analysis
Authors: B. Jegan | G. Kumaraguruparam | B. Varun Kumar
Source: Research Square (preprint, 2022-09-12)
Field: Chemistry — Inorganic Chemistry

Investigation of Thermo-Physical properties of EMIMBF 4 ionic liquid and water binary systems for heat transfer and refrigeration applications Abstract Research intent to discern a thermophysical property of novel proposed ionic liquid [EMIMBF4–1-Ethyl-3-MethyImidazolium tetrafluoroborate] which comprises of hydrophilic, non-corrosive, and negligible vapour pressure to play a perfect supernumerary solution to vapour absorption system and heat transfer applications. A Room-Temperature Ionic Liquid [RTIL] blended with deionized water in four different mass fractions as 20%,15%, 10% and 5% to prepare an ionic liquid and water binary mixture at room temperature. To understand the heat flow characterization of ionic liquid in the applications such as solar panel, chemical or refrigeration plant. Thermal stability and the thermophysical characteristics of EMIMBF4, including thermal conductivity, specific heat capacity, viscosity and density, were measured over the temperature range of 25 OC to 90 OC. The results obtained are fascinating in exemplifying their presence in the advancement of heat transfer and refrigeration applications. Ionic liquid ; Thermodynamics ; Refrigeration ; Heat transfer ; Viscosity ; Thermal conductivity ; Materials science ; Thermal stability ; Heat capacity ; Tetrafluoroborate ; Chemistry ; Chemical engineering ; Organic chemistry Inorganic Fluorides and Related Compounds ; Fiber-reinforced polymer composites ; Aerogels and thermal insulation
Authors: Junji Zhang | Joshua Pagotto | Timothy T. Duignan
Source: ChemRxiv (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks. Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon dioxide conversion/capture. Unfortunately, the determination of even their most basic properties from first principles remains an unsolved problem. As a result, the discovery and optimisation of electrolyte solutions for these applications largely relies on chemical intuition, experimental trial and error or empirical models. The challenge is that the dynamic nature of liquid electrolyte solutions require long simulation times to generate trajectories that sufficiently sample the configuration space; the long range electrostatic interactions require large system sizes; while the short range quantum mechanical (QM) interactions require an accurate level of theory. Fortunately, recent advances in the field of deep learning, specifically neural network potentials (NNPs), can enable significant accelerations in sampling the configuration space of electrolyte solutions. Here, we outline the implications of these recent advances for the field of materials chemistry and identify outstanding challenges and potential solutions. Electrolyte ; Computer science ; Artificial neural network ; Range (aeronautics) ; Electrolysis ; Ab initio ; Artificial intelligence ; Chemistry ; Aerospace engineering ; Engineering ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Adeesh Kolluru | Muhammed Shuaibi | Aini Palizhati | Nima Shoghi | Abhishek Das | Brandon C. Wood | C. Lawrence Zitnick | John R. Kitchin | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2022-06-04)
Field: Materials Science — Materials Chemistry

Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst datasets has offered the opportunity to build a universal machine learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the heterogeneous catalysis and machine learning communities towards building more accurate and robust models. In this perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy-conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery. Chemical space ; Data science ; Scale (ratio) ; Variety (cybernetics) ; Process (computing) ; Computer science ; Space (punctuation) ; Machine learning ; Maxima and minima ; Biochemical engineering ; Artificial intelligence ; Nanotechnology ; Engineering ; Drug discovery ; Biology ; Geography ; Materials science ; Bioinformatics ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Topic Modeling
Authors: Steph-Yves Louis | Edirisuriya M. Dilanga Siriwardane | Rajendra P. Joshi | Sadman Sadeed Omee | Neeraj Kumar | Jianjun Hu
Source: ChemRxiv (preprint, 2022-01-02)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks Performing first principle 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 pre-defined 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 method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% 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 (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions. Transferability ; Voltage ; Computer science ; Electrode ; Convolutional neural network ; Graph ; Ion ; Chemical space ; Artificial neural network ; Battery (electricity) ; Artificial intelligence ; Algorithm ; Biological system ; Machine learning ; Materials science ; Theoretical computer science ; Chemistry ; Electrical engineering ; Physics ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Battery Technologies Research
Authors: An Su | Chengwei Zhang | Yuanbin She | Yun‐Fang Yang
Source: ChemRxiv (preprint, 2022-10-11)
Field: Materials Science — Materials Chemistry

Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures Metalloporphyrins have been studied as biomimetic catalysts for more than 120 years and have accumulated a large amount of data, which provides a solid foundation for deep learning to discover chemical trends and structure-function relationships. In this study, key components of deep learning of metalloporphyrins, including databases, molecular representations, and model architectures, were systematically investigated. A protocol to construct canonical SMILES for metalloporphyrins was proposed, which was then used to represent the two-dimensional structures of over 10,000 metalloporphyrins in an existing computational database. Subsequently, several state-of-the-art chemical deep learning models, including graph neural network-based models and natural language processing-based models, were employed to predict the energy gaps of metalloporphyrins. Two models showed satisfactory predictive performance (R2>0.94) with canonical SMILES as the only source of structural information. In addition, an unsupervised visualization algorithm was used to interpret the molecular features learned by the deep learning models. Deep learning ; Computer science ; Artificial intelligence ; Construct (python library) ; Graph ; Artificial neural network ; Machine learning ; Database ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis