jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Klaus Ferdinand Gärditz
Source: Foundations of Chemistry (review, 2023-03-04)
Field: Chemistry — Physical and Theoretical Chemistry

The poetry of the universe, the periodic table, and the scientific progress: a review of new studies on the periodic table of the elements Abstract In 1869, two distinguished scientists, Dimitri Mendeleev and Lothar Meyer, discovered a certain periodicity among the chemical characteristics of the then known elements. Both developed first versions of the periodic table, independently. In the wake of the 150th anniversary, UNESCO proclaimed 2019 the “International Year of the Periodic Table of Chemical Elements”. Two lucid and detailed studies on the periodic table—accompanied by smaller studies on the occasion of the anniversary—have been published, recently, one of them analysing the scientific history, contributing to the (philosophical) theory of science (Eric Scerri), the other analysing the structures, patterns, and irregularities of the table (Geoff Rayner-Canham). Both studies are profound and vivid examples how scientific progress works. They illustrate that even in hard sciences—mirroring Merton’s concept of middle range theory—the required degree of exactness can remain on an intermediate level, as imperfection allows interpretations which could not (yet) be reached by pure mathematics and logic. Both of these brilliant studies provide valuable material, especially for a social science, to better understand how scientific ideas develop, how the power of visualization helps shape ideas, and how contingency is absorbed by the scientific process. Mirroring ; Periodic table ; Table (database) ; Periodic system ; Epistemology ; History of science ; Scientific theory ; Range (aeronautics) ; Theoretical physics ; Literature ; Philosophy ; Computer science ; Mathematics ; Physics ; Sociology ; Art ; Engineering ; Quantum mechanics History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Henry W. Sprueill | Carl N. Edwards | Mariefel V. Olarte | Udishnu Sanyal | Heng Ji | Sutanay Choudhury
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following capabilities and high quality reasoning, a goal-driven combinatorial search using LLMs has not been explored in detail. In this work, we present a Monte Carlo Tree Search-based approach that improves beyond state-of-the-art chain-of-thought prompting variants to augment scientific reasoning. We introduce two new reasoning datasets: 1) a curation of computational chemistry simulations, and 2) diverse questions written by catalysis researchers for reasoning about novel chemical conversion processes. We improve over the best baseline by 25.8% and find that our approach can augment scientist’s reasoning and discovery process with novel insights. Computer science ; Process (computing) ; Space (punctuation) ; Monte Carlo tree search ; Quality (philosophy) ; Theoretical computer science ; Monte Carlo method ; Artificial intelligence ; Programming language ; Epistemology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Tanjin He | Haoyan Huo | Christopher J. Bartel | Zheren Wang | Kevin Cruse | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-02-05)
Field: Materials Science — Materials Chemistry

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories. Similarity (geometry) ; Computer science ; Heuristic ; Key (lock) ; Base (topology) ; Sequence (biology) ; Scientific literature ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Source: Global NEST Journal (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

The Synthesis of Titania Nanoparticles (TiO2) and its Methylene Blue Dye removal efficiency through Photocatalysis based on Response Surface Methodology (RSM) &lt;p&gt;Water pollution has been acknowledged as an issue of concern due to recalcitrant pollutants like textile dyes. Therefore, the eco-friendly synthesis of nano-particles (NPs) through green chemistry approach from sources and materials of organic origin has not only enhanced the properties of substance at nanoscale but has also reduced the toxicological impacts on human health and the environment. In this study a successful attempt was made to synthesize TiO2 NPs by Mangifera indica leaves extract as organic source. The structural morphology was determined by carrying out FTIR, SEM, EDX and XRD techniques. The removal of Methylene Blue (MB) dye was assessed by the application of the photocatalysis technique. The experiments were designed using the RSM (CCD) approach for selected parameters such as pH, TiO2 NPs, Dye concentration and Time. The maximal dye removal efficiency of 98% was achieved at optimum conditions of 4.7 pH, 0.04g of nano photocatalyst in 250ml of dye solution having 8ppm concentration, allowed to react for 45 min. The regression coefficient value of 79.37% was determined by ANOVA for MB dye. The results clearly indicate that the application of green nanoparticles for the removal of MB dye is a feasible option to overcome with the ever-increasing issue of water pollution and environmental degradation.&lt;/p&gt;&#x0D; Photocatalysis ; Methylene blue ; Response surface methodology ; Chemical engineering ; Nanoparticle ; Fourier transform infrared spectroscopy ; Pollutant ; Brilliant green ; Nuclear chemistry ; Materials science ; Chemistry ; Degradation (telecommunications) ; Nanotechnology ; Catalysis ; Organic chemistry ; Chromatography Nanoparticles: synthesis and applications
Authors: Varvara Voinarovska | Mikhail Kabeshov | Dmytro Dudenko | Samuel Genheden | Igor V. Tetko
Source: ChemRxiv (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

When yield prediction does not yield prediction: an overview of the current challenges Machine Learning techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds potential for optimizing High-Throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into prevailing issues related to data availability and transferability in the discipline. Yield (engineering) ; Cheminformatics ; Task (project management) ; Computer science ; Machine learning ; Field (mathematics) ; Biochemical engineering ; Transferability ; Artificial intelligence ; Risk analysis (engineering) ; Engineering ; Chemistry ; Systems engineering ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Layla Ahmed Othman | Shireen Mohammed | Maher Khalid
Source: Oriental Journal Of Chemistry (article, 2023-04-30)
Field: Chemistry — Organic Chemistry

A Flexible Route to Synthesis and Molecular Docking of Some New Quinoline Derivatives through Imine and Cyclization Processes The current assignment depicts the structure of recent quinoline derivatives. This method begins with the structure of imine derivatives through the condensation reaction of ethyl 2-aminobenzoate with various substituted aliphatic aldehydes and ketones in the existence of sodium hydroxide as a catalyst. While the second step includes the intra-cyclization process of the imine compounds in presence of a base like tertiary butoxide that resolute installs the hydroxyl- group on the bicyclic skeleton and aromatic amines. The molecular docking program Flare V4.0 was applied to investigate the biological activities of divers produced compounds against E.coli bacteria. Spectral data support the compounds of each the recent outputs acquired during this assignment. Chemistry ; Imine ; Quinoline ; Catalysis ; Combinatorial chemistry ; Organic chemistry ; Green chemistry ; Aldehyde ; Docking (animal) ; Bicyclic molecule ; Condensation reaction ; Reaction mechanism Quinazolinone synthesis and applications ; Synthesis and Biological Evaluation ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Zhen Liu | Yurii S. Moroz | Olexandr Isayev
Source: ChemRxiv (preprint, 2023-07-28)
Field: Materials Science — Materials Chemistry

The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets. To understand the causes and inspire future design, we systematically benchmarked the yield prediction task. We carefully curated and augmented a literature dataset of 41,239 amide coupling reactions, each with information on reactants, products, intermediates, yields, and reaction contexts, and provided 3D structures for the molecules. We calculated molecular features related to 2D and 3D structure information, as well as physical and electronic properties. These descriptors were paired with 4 categories of machine learning methods (linear, kernel, ensemble, and neural network), yielding valuable benchmarks about feature and model performance. Despite the excellent performance on a high-throughput experiment (HTE) dataset (R2 around 0.9), no method gave satisfying results on the literature data. The best performance was an R2 of 0.395 ± 0.020 using stack technique. Error analysis revealed that reactivity cliff and yield uncertainty are the main reasons for incorrect predictions. Removing reactivity cliffs and uncertain reactions boosted the R2 to 0.457 ± 0.006. These results highlight that yield prediction models must be sensitive to the reactivity change due to the subtle structure variance, as well as be robust to the uncertainty associated with yield measurements. Robustness (evolution) ; Computer science ; Artificial intelligence ; Yield (engineering) ; Benchmarking ; Sensitivity (control systems) ; Machine learning ; Data mining ; Biological system ; Chemistry ; Materials science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Fouad El Kalai | Christina Susan Abraham | Sevgi Kansız | Afaf Oulmidi | Sambantham Muthu | Johanan Christian Prasana | Necmi Dege | Hatem A. Abuelizz | Rashad Al‐Salahi | N. Benchat | Khalid Karrouchi
Source: Crystals (article, 2023-07-13)
Field: Chemistry — Organic Chemistry

Synthesis, Crystal Structure, and Computational Investigations of 2-(2-(4-Fluorophenyl)-2-oxoethyl)-6-methyl-5-(4-methylbenzyl)pyridazin-3(2H)-one as Antiviral Agent The aim of this work was to scrutinize the physiochemical properties of a new pyridazin-3(2H)-one derivative with potential pharmaceutical effectiveness via density functional theory (DFT) and molecular docking analysis. The compound 2-(2-(4-fluorophenyl)-2-oxoethyl)-6-methyl-5-(4-methylbenzyl)pyridazin-3(2H)-one (FOMMP) was synthesized and characterized by FT-IR, UV-Vis, 1H-NMR, 13C-NMR, ESI-MS, and single-crystal XRD analysis. In addition, the geometrical structure of the molecule was analyzed. Frontier molecular orbital (FMO) analysis showed a low energy gap that suggests the chemical reactivity of the title compound. The electrophilicity index (ω) points towards the probable biological activity of FOMMP. The molecular electrostatic potential (MEP) was used to assess the local reactivity properties and suggests that the nitrogen atom sites are electronegative. Computational and experimental UV-spectral analyses were performed to attain the bandgap associated with electronic transitions while the charge transfer length helped us determine that the excitation mode associated with the electronic transitions is long-ranged. Natural hybrid orbital (NHO) and natural bond orbital (NBO) analyses depicted the prominent acceptor-donor interactions in terms of the stabilization energies. Hirshfeld surface analysis was performed to analyze the intermolecular interactions in the crystal structure. In addition, a molecular docking study was executed to evaluate the potential of the protease inhibitors (PIs) against SARS-CoV-2. Natural bond orbital ; Chemistry ; Molecular orbital ; Computational chemistry ; Density functional theory ; Molecule ; Carbon-13 NMR ; Reactivity (psychology) ; Docking (animal) ; HOMO/LUMO ; Crystal structure prediction ; Crystallography ; Crystal structure ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities ; Synthesis and Reactivity of Heterocycles
Authors: Mayur Akewar | Manoj B. Chandak
Source:  (review, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Algorithms and Applications: A Review &lt;p&gt;The paper covers topics ranging from hyperspectral imaging applications to innovative algorithms that have enhanced the analysis of hyperspectral data. We delve into the practical applications, including Agriculture, Food Quality and Safety, Earth Sciences, Exploration and Monitoring, Healthcare, Pharmaceuticals, Medical Imaging, Industrial Manufacturing, Management, Conservation, Safety and Security. Our goal is to provide a holistic understanding of how hyperspectral imaging is transforming these fields and driving new possibilities. We also discussed detailed algorithmic development in Hyperspectral Imaging including Supervised and Unsupervised algorithms. We reviewed algorithmic development from the early 1980’s to recent developments including Artificial Intelligence involving algorithms from Machine Learning to Deep Learning. We believe our paper will be of particular interest to your readers for those who wish to study algorithm development from simple image processing procedures to AI based approaches and how these algorithms are used in diverse applications to make intelligent decisions for increasing productivity and efficiency in Agriculture, Exploration and Management and identification of diseases and natural resources. Our paper is well organized into various sections as per algorithms and applications. We reviewed more than 250 papers with proper citations and authors' research work.&lt;/p&gt; Hyperspectral imaging ; Computer science ; Algorithm ; Artificial intelligence ; Identification (biology) ; Machine learning ; Data science Spectroscopy and Chemometric Analyses
Authors: Austin Tripp | Krzysztof Maziarz | Sarah Lewis | Marwin Segler | José Miguel Hernández-Lobato
Source: arXiv (Cornell University) (preprint, 2023-10-13)
Field: Materials Science — Materials Chemistry

Retro-fallback: retrosynthetic planning in an uncertain world Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms. Retrosynthetic analysis ; Computer science ; Plan (archaeology) ; Task (project management) ; Imperfect ; Range (aeronautics) ; Space (punctuation) ; Series (stratigraphy) ; Artificial intelligence ; Operations research ; Theoretical computer science ; Algorithm ; Mathematics ; Engineering ; Geography Machine Learning in Materials Science
Authors: Alexey N. Beskopylny | Антон Чепурненко | Besarion Meskhi | Sergey A. Stel’makh | Evgenii M. Shcherban’ | Irina Razveeva | Alexey Kozhakin | Kirill Zavolokin | Andrei A. Krasnov
Source: Biomimetics (article, 2023-07-13)
Field: Chemistry — Analytical Chemistry

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms Fluid particle detection technology is of great importance in the oil and gas industry for improving oil-refining techniques and in evaluating the quality of refining equipment. The article discusses the process of creating a computer vision algorithm that allows the user to detect water globules in oil samples and analyze their sizes. The process of developing an algorithm based on the convolutional neural network (CNN) YOLOv4 is presented. For this study, our own empirical base was proposed, which comprised microphotographs of samples of raw materials and water-oil emulsions taken at various points and in different operating modes of an oil refinery. The number of images for training the neural network algorithm was increased by applying the authors' augmentation algorithm. The developed program makes it possible to detect particles in a fluid medium with the level of accuracy required by a researcher, which can be controlled at the stage of training the CNN. Based on the results of processing the output data from the algorithm, a dispersion analysis of localized water globules was carried out, supplemented with a frequency diagram describing the ratio of the size and number of particles found. The evaluation of the quality of the results of the work of the intelligent algorithm in comparison with the manual method on the verification microphotographs and the comparison of two empirical distributions allow us to conclude that the model based on the CNN can be verified and accepted for use in the search for particles in a fluid medium. The accuracy of the model was <i>AP@</i>50 = 89% and <i>AP@</i>75 = 78%. Algorithm ; Computer science ; Refining (metallurgy) ; Refinery ; Artificial neural network ; Convolutional neural network ; Process (computing) ; Dispersion (optics) ; Oil refinery ; Process engineering ; Artificial intelligence ; Materials science ; Environmental science ; Engineering Petroleum Processing and Analysis ; Oil Spill Detection and Mitigation ; Advanced Data Processing Techniques
Authors: Peter Monka | Katarína Monková | George Pantazopoulos | Anagnostis Toulfatzis
Source: Metals (article, 2023-04-23)
Field: Materials Science — Materials Chemistry

Effect of Wear on Vibration Amplitude and Chip Shape Characteristics during Machining of Eco-Friendly and Leaded Brass Alloys The dynamic stability of the machining set and the entire cutting process, together with the appropriate form of chips generated during machining under the given conditions, are the basic prerequisites for autonomous machining in accordance with the Industry 4.0 trend. The research, based on a newly designed method, aims to study the frequency response of the machining system to different values of tool wear and cutting speed, which cause the worsening of the machined parts’ quality and the instability of the whole cutting process. The new idea is based on the inverse principle, in which the wear with various values of VB was artificially prepared in advance before machining. Consequently, the effect of artificial wear and cutting speed on vibration and chip shape characteristics were studied. Three types of brass alloys were used within the experiments as the machined materials. Measured data were statistically processed and the desired dependencies were plotted. Chips were collected for each combination of machining conditions, while the article presents a database of the obtained chip shapes at individual cutting speeds so that they can be compared and classified. The results showed that brass alloys CW510L and CW614N exhibit an average of three times lower vibration damping compared to the CW724R alloy, while relatively good chip formation was noted in the evaluated machining conditions even without the use of a chip breaker. The problematic chip shape occurred only in some cases at the machining of CW510L and CW724R, which cannot be generalized. Machining ; Brass ; Chip ; Vibration ; Chip formation ; Tool wear ; Materials science ; Mechanical engineering ; Metallurgy ; Computer science ; Engineering ; Acoustics ; Physics Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Yuanyuan Li | Yang Yang | Shuangshuang Yan | Zhipeng Lei | Meiqing Guo
Source: High Voltage (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Mitigation of electrical treeing at high temperature in nano‐SiO <sub>2</sub> doped epoxy resin Abstract In this work, electrical tree inception and ageing experiments were conducted to investigate the initiation, growth characteristics and structural characteristics of electrical trees of nano‐SiO 2 doped epoxy resin at different temperatures. Electrical tree inception and ageing experiments were conducted using power frequency voltage of 15 kV at 20, 40, 60, 80 and 100°C. Electrical breakdown properties of epoxy resin were investigated by applying AC and DC voltages at 20, 40, 80 and 120°C. There are two major observations based on the experimental results. With the increase of temperature, the morphological structure of the electrical trees changes from dendritic to plexiform, the tree inception voltage decreases and the growth rate of the electrical trees increases obviously. Either under DC or AC voltage, the breakdown strength of the nano‐SiO 2 doped epoxy resin decreases as the temperature increases. Finally, based on the space charge and the trap level measurement, the mechanism of the electrical tree propagation and breakdown in nano‐SiO 2 doped epoxy resin was analysed. Electrical treeing ; Epoxy ; Materials science ; Composite material ; Doping ; Space charge ; Nano- ; Electrical breakdown ; Voltage ; Breakdown voltage ; Partial discharge ; Electrical engineering ; Optoelectronics ; Dielectric High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Smart Materials for Construction