jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nathaniel H. Park | Matteo Manica | Jannis Born | James L. Hedrick | Tim Erdmann | Dmitry Yu. Zubarev | Nil Adell Mill | Pedro L. Arrechea
Source: ChemRxiv (preprint, 2023-02-12)
Field: Materials Science — Materials Chemistry

An extensible platform for enabling artificial intelligence guided design of catalysts and materials Advances in artificial intelligence (AI), machine learning (ML), and automated experimentation are poised to vastly accelerate and reshape polymer science research. The difficulties of experimental data and polymer structure representation form a significant barrier in developing foundational AI models in polymer chemistry. These issues are further compounded by a lack of accessible and extensible software tools for experimental researchers to effectively leverage their historical data for AI development. To address these difficulties, we developed an extensible domain specific language, termed Chemical Markdown Language (CMDL), to enable flexible and consistent representation of disparate experiment types. CMDL can be extended to cover new data types and provides built-in support for the graph representation of polymers and automated experimentation platforms such as continuous flow reactors facilitating their ingestion into AI/ML pipelines. CMDL enabled seamless use of historical experimental data to train new AI models for catalyst and materials design. Here, the utility of this approach was first demonstrated through the successful synthesis and experimental validation of novel AI-generated ring-opening polymerization catalysts. Next, we derived a first-of-kind generative model for block and statistical co-polymer structures from their CMDL graph representations. This model preserved critical functional groups within the polymer structure, allowing them to be readily validated experimentally. These results reveal how the versatility of CMDL facilitates translation of historical experimental data into meaningful predictive and generative models which produce experimentally actionable output. Computer science ; Leverage (statistics) ; Generative grammar ; Software ; Representation (politics) ; Artificial intelligence ; Extensibility ; Software engineering ; Machine learning ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Sanjeeta Rani | Syed Kashif Ali | Pawan Kumar | Kunwar Sugam Anugrah | Laishram Saya | Geetu Gambhir | Drashya Gautam | Sunita Hooda | Manisha Verma
Source: Polimery (article, 2023-09-28)
Field: Materials Science — Polymers and Plastics

Novel multicomponent functionalized biopolymers with enhanced thermal and dielectric properties A new functionalized biopolymer was obtained by adding ethylenediaminetetraacetic acid to chitin and magnetite nanoparticles (Fe3O4). Thermogravimetric analysis was performed, and dielectric properties were examined. The obtained biopolymer showed better thermal stability, as evidenced by a higher onset temperature. The activation energy calculated using imaginary impedance data and Nyquist plots was found to be consistent. Moreover, the dielectric permittivity decreased rapidly with increasing frequency. At high frequencies, there was no dependence of dielectric loss on temperature and frequency. The obtained biopolymer can be used in many applications such as microwave devices, deflection yoke, high-frequency capacitors, sensors, etc. Materials science ; Biopolymer ; Dielectric ; Thermal stability ; Thermogravimetric analysis ; Nyquist plot ; Composite material ; Dielectric loss ; Dielectric spectroscopy ; Magnetite ; Permittivity ; Capacitor ; Polymer ; Chemical engineering ; Optoelectronics ; Electrochemistry ; Chemistry ; Electrical engineering ; Electrode ; Voltage Conducting polymers and applications ; Nanocomposite Films for Food Packaging ; Synthesis and properties of polymers
Authors: Sayat Yermekuly Kydyrali | С.Х. Акназаров | Alibek Mutushev | J.M. González-Leal | Olga Yuryevna Golovchenko | Sholpan Yesenzholovna Gabdrashova | Marat Tulepov | A.B. Seisenova
Source: Crystals (article, 2023-05-25)
Field: Materials Science — General Materials Science

Investigation of the Effect of Aluminum Powder on the Combustion Rate of the Composite This article discusses the combustion of high-energy systems based on the oxidizers ammonium nitrate, potassium nitrate, and a mixture of highly active aluminum grade PAP-1 under conditions of a deficiency or excess oxidant α, under a pressure reduction in the range of 0.1–3 MPa. The objective of this study is to develop materials for the production of high-energy compositions based on oxidizers of ammonium and potassium nitrate, fuel binder, and metallic fuel in the form of aluminum powders ASD-6 and PAP-1. The influence of the amount of excess oxidizer, the amount of metallic fuel, and the grade of aluminum on the rate and completeness of combustion of a high-energy composition have been studied. Experiments were carried out on the studied high-energy systems, depending on the grade of aluminum used and the excess of the oxidizing agent α, and the influence of pressure on the burning rate. The compositions of high-energy compositions based on highly active aluminum with the highest combustion rate of mixtures and combustion completeness were determined. Combustion ; Aluminium ; Potassium nitrate ; Oxidizing agent ; Ammonium nitrate ; Composite number ; Potassium ; Materials science ; Metal ; Inorganic chemistry ; Chemistry ; Ammonium ; Nitrate ; Aluminium powder ; Chemical engineering ; Metallurgy ; Composite material ; Organic chemistry Material Properties and Applications ; Thermal and Kinetic Analysis ; Energetic Materials and Combustion
Authors: Mohammadamin Tavakoli | Yin Ting T. Chiu | Alexander Shmakov | Annmarie G. Carlton | David L. Van Vranken | Pierre Baldi
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of interpretable predictions and limited generalization capability to other chemistry domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable representation of chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, a public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry. Benchmark (surveying) ; Generalization ; Computer science ; Artificial intelligence ; Chemistry ; Hydroxyl radical ; Representation (politics) ; Deep learning ; Radical ; Machine learning ; Organic chemistry ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhenru Chen | Yunchao Xie | Yuchao Wu | Yuyi Lin | Shigetaka Tomiya | Jian Lin
Source: ChemRxiv (preprint, 2023-09-29)
Field: Chemistry — Analytical Chemistry

An Interpretable and Transferrable Vision Transformer Model for Rapid Materials Spectra Classification Rapid analysis of materials characterization spectra is pivotal for preventing accumulation of unwieldy datasets, thus accelerating subsequent decision-making. However, current methods heavily rely on experience and domain knowledge, which not only proves tedious but also is hard to keep up with the pace of data acquisition. In this context, we introduce a transferable Vision Transformer (ViT) model for identification of materials from their spectra, including XRD and FTIR. First, an optimal ViT model was trained to predict metal organic frameworks (MOFs) from their XRD spectra. It attains prediction accuracies of 70%, 93%, and 94.9% for Top-1, Top-3, and Top-5, respectively, and a shorter training time of 269 seconds in comparison to a convolutional neural network model. The dimension reduction and attention weight map underline its adeptness at capturing relevant features in the XRD spectra for determining the prediction outcome. Moreover, the model can be transferred to a new one for prediction of organic molecules from their FTIR spectra, attaining remarkable Top-1, Top-3, and Top-5 prediction accuracies of 84%, 94.1%, and 96.7%, respectively. The introduced ViT based model would set a new revenue to handling diverse types of spectroscopic data, thus expediting the materials characterization processes. Computer science ; Expediting ; Spectral line ; Context (archaeology) ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Convolutional neural network ; Data mining ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Alessio Capitanelli | Fulvio Mastrogiovanni
Source: arXiv (Cornell University) (preprint, 2023-03-01)
Field: Materials Science — Materials Chemistry

A Framework for Neurosymbolic Robot Action Planning using Large Language Models Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, human-robot collaboration scenarios because of the poor performance in complex planning domains or when frequent re-planning is needed. We present a framework, Teriyaki, specifically aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL), and then leveraging its generative capabilities to overcome a number of limitations inherent to symbolic task planners. Potential benefits include (i) a better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, making each action available for execution as soon as it is generated instead of waiting for the whole plan to be available, which in turn enables concurrent planning and execution. Recently, significant efforts have been devoted by the research community to evaluate the cognitive capabilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to provide an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities to build a look-ahead predictive planning model. Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4% Computer science ; Task (project management) ; Robot ; Action plan ; Action (physics) ; Fluency ; Plan (archaeology) ; Scalability ; Human–computer interaction ; Artificial intelligence ; Process management ; Psychology ; Engineering ; Systems engineering ; Management Machine Learning in Materials Science ; Topic Modeling ; Explainable Artificial Intelligence (XAI)
Authors: Abhimanyu P. Pawar | Kishor Sudhir Naktode | Arvind J. Mungole
Source: Physics and Chemistry of Solid State (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Green synthesis of silver nanoparticles from whole plant extract analyzed for characterization, antioxidant, and antibacterial properties In this analysis, A green synthesis method utilizing a plant extract derived from Rumex nepalensis (spreng) was employed to synthesize silver nanoparticles. The synthesized nanoparticles were thoroughly characterized for their structural, surface morphological, optical, antioxidant, and antibacterial properties. Structural analysis revealed a face-centered cubic structure, while FTIR analysis confirmed the presence of biosurfactant molecules in the leaf extract that acted as reducing agents. SEM and TEM analyses further confirmed the spherical shape of the nanoparticles, with a size range of 19-28 nm. The evaluation of the silver nanoparticles demonstrated their antioxidant and antibacterial properties. These nanoparticles exhibited activities in both antioxidant and antimicrobial realms, showcasing their potential as dual-functional agents. This study highlights the effectiveness of the green synthesis method using Rumex nepalensis (spreng) extract for the production of silver nanoparticles with desirable properties for various applications. Silver nanoparticle ; Nanoparticle ; Antioxidant ; Fourier transform infrared spectroscopy ; Antibacterial activity ; Antimicrobial ; Materials science ; Chemistry ; Nuclear chemistry ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Bacteria ; Biology Nanoparticles: synthesis and applications
Authors: Reem Khalid Alharbi | Noura Kouki | Abdulrahman Mallah | Lotfi Béji | Haja Tar | Azizah A. Algreiby | Abrar S. Alnafisah | Sobhi Hcini
Source: Crystals (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Processing and Investigation of Cd0.5Zn0.5Fe2−xCrxO4 (0 ≤ x ≤ 2) Spinel Nanoparticles This study presents the synthesis of Cd0.5Zn0.5Fe2−xCrxO4 nanoparticles via the sol–gel method, along with a comprehensive characterization of their morphological, structural, infrared, and magnetic properties. The X-ray diffraction pattern confirms the formation of the spinel structure, and the cation distribution is estimated using X-ray analysis and confirmed by magnetization measurements. The crystalline size, ranging from 152 to 189 nm, and lattice parameter, varying from 8.51134 Å to 8.42067 Å, decrease with increasing Cr content. The saturation magnetization decreases from 55 emu/g to 10.8 emu/g, while the remanent magnetization increases (3.5 emu/g ≤ Mr ≤ 6.27 emu/g), and the coercivity increases (82 Oe ≤ HC ≤ 422.15 Oe) with the addition of Cr ions. Fourier transform infrared (FTIR) spectroscopy reveals two absorption bands at ν1 and ν2, located near 600 and 400 cm−1, respectively, which correspond to the vibrations of the metal–oxygen bonds in the spinel structure. Spinel ; Coercivity ; Remanence ; Fourier transform infrared spectroscopy ; Magnetization ; Materials science ; Analytical Chemistry (journal) ; Infrared spectroscopy ; Lattice constant ; Nanoparticle ; Diffraction ; Chemistry ; Crystallography ; Nanotechnology ; Condensed matter physics ; Optics ; Physics ; Metallurgy ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Advanced Condensed Matter Physics
Authors: Hà Hữu Sơn | Phi Long Nguyen | Nguyen T. K. Thanh | Nguyen Thi Hong Ngọc | Minh Thủy Đặng | Quoc Pham Le | Luu Van Tuynh
Source: Coatings (article, 2023-09-10)
Field: Materials Science — Materials Chemistry

Polyalphaolefin Oil/MgO-20 Nanofluids Coating Shows Corrosion Resistance, High Moisture Resistance, and Water Resistance for Electrical and Electronic Equipment The PAO/MgO nanofluids-based dielectric fluid DF(MgO-20) has significantly increased corrosion resistance as a coating. Electrochemical studies show that the DF(MgO-20) coating has protection efficiency of up to 99% for steel, copper, and aluminum. This coating is capable of providing corrosion protection for steel samples for up to 120 h in salt spray tests, and printed circuit boards (PCBs) for more than 20 days in salt spray tests in accordance with the ASTM B117 standard. The DF(MgO-20) coating fully meets the moisture resistance and fungal resistance standards required by the MIL-1-46058 C standard. The coating also demonstrates water displacement, meeting the requirements of the MIL-PRF-81309G standard. The DF(MgO-20) coating is able to protect electronic equipment working in underwater environment for up to 20 days. The aforementioned outstanding protection properties are achieved thanks to the nanofluid effect of the DF(MgO-20) dielectric fluid with the presence of MgO nano-additives that increase its overflow ability. The coating penetrates deeply and adheres tightly to the metal substrates, helping to separate them from moisturizing agents and corrosive agents. The research results aim to apply this coating to protect electronic equipment working in the tropical marine climate of Vietnam. Coating ; Materials science ; Corrosion ; Nanofluid ; Metallurgy ; Moisture ; Composite material ; Nanotechnology ; Nanoparticle High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Corrosion Behavior and Inhibition
Authors: Amit Tiwari | J. Sanjog
Source: Oriental Journal Of Chemistry (article, 2023-10-30)
Field: Materials Science — Biomaterials

Nanocellulose from Agricultural Waste – A Concise Insight into Extraction and Applications Uncontrolled dumping of agricultural wastes is a cause of concern for the well-being of the environment. Nanotechnology has gained research popularity due to its beneficial applications. Nowadays the separation of nanocellulose from different agricultural wastes is explored in an increasing measure. Agricultural wastes are subjected to various treatments to extract nanocellulose. Nanocellulose extracted from agricultural wastes can be used in the preparation of biodegradable nanocellulose-based materials (for various applications) which are environment-friendly and can promote sustainable economic growth by ensuring the good usage of agricultural wastes. Nanocellulose ; Agriculture ; Environmentally friendly ; Waste management ; Agricultural waste ; Environmental science ; Biochemical engineering ; Nanotechnology ; Engineering ; Materials science ; Cellulose ; Chemical engineering ; Ecology Advanced Cellulose Research Studies ; Biofuel production and bioconversion
Authors: Ivan Stojanović | Mirta Logar | Lovro Turkalj | Ivan Cindrić | Marin Kurtela | Hrvoje Franjić
Source: Materials (article, 2023-10-04)
Field: Materials Science — Materials Chemistry

Influence of Catalytic Infrared Radiation on the Protective Properties of Industrial Epoxy Primers The application of organic coatings is a common way of protecting metal substrates against corrosion. To dry the coating faster, catalytic infrared radiation (IR) can be applied. This paper aims to assess the differences in the physical, chemical, and corrosion properties of primer coatings dried with catalytic infrared radiation, compared to the same coatings dried under atmospheric conditions. Corrosion properties were characterized using humidity and a salt spray chamber, as well as electrochemical impedance spectroscopy (EIS), preceded by open circuit potential (OCP) measurement. Pencil hardness, cross-cut, and pull-off adhesion tests were used to compare the properties of examined primers before and after testing in the corrosion acceleration chambers. The microstructure and distribution of chemical composition were studied by scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDX) together with Fourier-transform infrared spectroscopy (FTIR). Phase transitions in the coating were determined by differential scanning calorimeter (DSC). Infrared-dried primers achieved a higher curing degree. Therefore, their mechanical and corrosion properties are superior when compared to the same coatings dried under atmospheric conditions. Materials science ; Corrosion ; Fourier transform infrared spectroscopy ; Dielectric spectroscopy ; Scanning electron microscope ; Differential scanning calorimetry ; Coating ; Infrared spectroscopy ; Epoxy ; Infrared ; Composite material ; Analytical Chemistry (journal) ; Chemical engineering ; Electrochemistry ; Chemistry ; Optics ; Organic chemistry Corrosion Behavior and Inhibition ; Cultural Heritage Materials Analysis ; Epoxy Resin Curing Processes
Authors: Tomasz Mazur | Łukasz Mazur | Mariusz Borkowski | Tomasz Kuciel | Michał Szuwarzyński
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Polymer-nanoparticle thin scaffolds with any-shape magnetic field gradients This study investigates the self-assembly of magnetic nanoparticles into a flat nanocomposite surface with concealed magnetic patterns. The application of a multilayer stamping medium and the presence of a magnetic field gradient would enable the attraction and organization of superparamagnetic iron oxide nanoparticles (SPIONs) onto predefined areas, corresponding to a pre-fabricated magnetic layer. The experimental approach involved preparing patterned surfaces by applying a multilayer of poly(ethyleneimine) (PEI) and poly(styrene sulfonate) (PSS) as a stamping medium. The SPIONs selectively adhered to the surface, mirroring the underlying magnetic layer shape. Subsequently, these SPION were subjected to an 80 °C treatment in the presence of an external magnetic field to enhance their magnetic properties, and then covered with a polystyrene layer. Using atomic force microscopy, magnetic force microscopy and dark field mode optical microscopy we demonstrated that obtained patterned magnetic field gradients are strong enough to attract and organize magnetic material on top of them, even when being covered with polymeric layers. Moreover, our findings show that the oriented deposition of magnetic nanoparticles is primarily determined by the strength of magnetic interactions between the pattern and nanoparticles, independently of surface topography (with roughness of 0.21 ± 0.06 nm) or Brownian motions. Materials science ; Magnetic nanoparticles ; Magnetic force microscope ; Magnetic field ; Polystyrene ; Nanoparticle ; Nanotechnology ; Superparamagnetism ; Surface roughness ; Polymer ; Layer (electronics) ; Composite material ; Chemical engineering ; Magnetization Pickering emulsions and particle stabilization ; Characterization and Applications of Magnetic Nanoparticles ; Polymer Surface Interaction Studies