jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoqiang Guo | Sendong Zhao | Haochun Wang | Yanrui Du | Bing Qin
Source: arXiv (Cornell University) (preprint, 2024-01-21)
Field: Materials Science — Materials Chemistry

MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts Deep learning is now widely used in drug discovery, providing significant acceleration and cost reduction. As the most fundamental building block, molecular representation is essential for predicting molecular properties to enable various downstream applications. Most existing methods attempt to incorporate more information to learn better representations. However, not all features are equally important for a specific task. Ignoring this would potentially compromise the training efficiency and predictive accuracy. To address this issue, we propose a novel approach, which treats language models as an agent and molecular pretraining models as a knowledge base. The agent accentuates task-relevant features in the molecular representation by understanding the natural language description of the task, just as a tailor customizes clothes for clients. Thus, we call this approach MolTailor. Evaluations demonstrate MolTailor's superior performance over baselines, validating the efficacy of enhancing relevance for molecular representation learning. This illustrates the potential of language model guided optimization to better exploit and unleash the capabilities of existing powerful molecular representation methods. Our code is available at https://github.com/SCIR-HI/MolTailor. Computer science ; Task (project management) ; Representation (politics) ; Relevance (law) ; Artificial intelligence ; Feature learning ; Block (permutation group theory) ; Identification (biology) ; Exploit ; Machine learning ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Lamis Alaa Eldin Refat | Jolanta Karpinska | Saidulu Konda | John M. Simmie | Paul V. Murphy | Patrick McArdle | Andrea Erxleben
Source: Chemistry - A European Journal (article, 2024-04-16)
Field: Chemistry — Organic Chemistry

Application of Sublimation in the Synthesis and Crystal Growth of Organosulfones The solvent-free elimination of sulfinic acid and aromatization of 1,6-trans-substituted bis(arylsulfone) trienes is reported. It is shown that sublimation can be used as a 'green' method to combine the thermal transformation of six trienes and the crystal growth of the resulting 4-(phenylsulfonyl)biphenyls. When the sublimation conditions are carefully controlled, high quality single crystals of the 4-(phenylsulfonyl)biphenyls are obtained. Theoretical modelling of the reaction using the simplified triene Ph-(CH)<sub>6</sub>-SO<sub>2</sub>H showed that the cyclization is energetically feasible and that the complete conversion is possible during the timescale of the sublimation. At temperatures slightly higher than the optimum sublimation temperature two of the trienes transformed into 1,4-cyclohexadienes that did not eliminate phenylsulfinic acid. A reaction mechanism involving a 1,3-hydrogen shift induced by free PhS• radicals is proposed for the formation of the 1,4-cyclohexadienes. Sublimation (psychology) ; Aromatization ; Chemistry ; Solvent ; Organic chemistry ; Catalysis Organic Chemistry Cycloaddition Reactions ; Organic and Inorganic Chemical Reactions ; Chemical Reaction Mechanisms
Authors: Hang Dong | Nan Guo | Pengfei Zhang | Jian Zhao | Zhihua Wang
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Study on microstructure evolution of waxy crude oil emulsions under dynamic cooling conditions A rheo-microscopy <i>in situ</i> synchronous measurement system was utilized to investigate the dynamic behavior of water droplets in W/O waxy crude oil emulsions subjected to dynamic cooling conditions, the microstructural evolution of water droplets aggregates can be categorized into three stages based on the various forms of wax crystals. The results show that under the joint action of wax crystals and water droplets, the water droplets aggregation trend and complexity in the system are negatively correlated with the changes of temperature and shear rate, and the water droplets movement behavior is positively correlated with the changes of temperature and shear rate. As the temperature decreases, the minimum edge distance of water droplets decreases by a maximum of 32.1%, the specific surface area (SA) decreases by a maximum of 12.0%, and the fractal dimension increases by a maximum of 11.7%. As the shear rate increases, the minimum edge distance of water droplets increases by up to 27.9%, the specific surface area (SA) increases by up to 10.1%, and the fractal dimension decreases by up to 8.5%. Additionally, an analysis is conducted on the collision aggregation behavior of water droplets in shear flow field based on population balance theory. Microstructure ; Crude oil ; Materials science ; Slow cooling ; Chemical engineering ; Petroleum engineering ; Metallurgy ; Geology ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: А. С. Байсанов | Nina Vorobkalo | Yerbol Shabanov | Azat Mussin | Symbat Sharieva | Amir Makishev
Source: Processes (article, 2024-09-05)
Field: Materials Science — Ceramics and Composites

Test Results of Crystalline Silicon Melting Process from Briquetted Monocharge Obtained from Microsilica Currently, enterprises producing crystalline silicon are facing the formation and accumulation of large volumes of microsilica, a technogenic dusty waste formed during the melting of silicon alloys. Due to its chemical composition, this waste can be a significant raw material for metallurgical production. Therefore, this study is aimed to solve the problem of recycling microsilica. For these studies, a technology for the combined briquetting of microsilica and a carbonaceous reducing agent was developed for the production of a pilot batch of briquettes. This paper presents the results obtained from the process of testing the melting of crystalline (technical) silicon from briquetted monocharge obtained from microsilica. The tests were conducted under large-scale laboratory conditions on a 200 kVA ore-thermal furnace, where 30, 50, and 100% replacements of the traditional charge mixture with briquettes were tested. The results of this study showed that briquettes in the melting process of technical silicon can be successfully used in the range of 0 to 50%. The use of briquettes can significantly improve the technological indicators. The maximum extraction of silicon (approximately 83%) was achieved at 30% replacement. The technical and economic indicators of the process also improved. In particular, an increase in productivity was observed in comparison with tests on a traditional charge. Silica fume ; Materials science ; Silicon ; Crystalline silicon ; Ferrosilicon ; Process (computing) ; Metallurgy ; Composite material ; Computer science ; Programming language ; Compressive strength Advanced ceramic materials synthesis ; Bauxite Residue and Utilization ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Hannes H. Loeffler | Shunzhou Wan | Marco Klähn | Agastya P. Bhati | Peter V. Coveney
Source: ChemRxiv (preprint, 2024-04-26)
Field: Materials Science — Materials Chemistry

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Absolute Binding Free Energy Simulations Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world’s only exa-scale machine. We show that the protocol can find better binders compared to baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale, of immediate and direct relevance for modern, data-driven drug discovery. Generative grammar ; Absolute (philosophy) ; Energy (signal processing) ; Generative model ; Computer science ; Artificial intelligence ; Mathematics ; Statistics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Nanopore and Nanochannel Transport Studies
Authors: Wen‐Jun Zhou | Mingzhi Yang | Qian Peng | Yong Peng | Kui Wang | Qiang Xiao
Source: Computer Modeling in Engineering & Sciences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Multi-Objective Optimization of Aluminum Alloy Electric Bus Frame Connectors for Enhanced Durability The widespread adoption of aluminum alloy electric buses, known for their energy efficiency and eco-friendliness, faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel. This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries, necessitating robust frame protection. Our study aims to optimize the connectors of aluminum alloy bus frames, emphasizing durability, energy efficiency, and safety. This research delves into Multi-Objective Coordinated Optimization (MCO) techniques for lightweight design in aluminum alloy bus body connectors. Our goal is to enhance lightweighting, reinforce energy absorption, and improve deformation resistance in connector components. Three typical aluminum alloy connectors were selected and a design optimization platform was built for their MCO using a variety of software and methods. Firstly, through three-point bending experiments and finite element analysis on three types of connector components, we identified optimized design parameters based on deformation patterns. Then, employing Optimal Latin hypercube design (OLHD), parametric modeling, and neural network approximation, we developed high-precision approximate models for the design parameters of each connector component, targeting energy absorption, mass, and logarithmic strain. Lastly, utilizing the Archive-based Micro Genetic Algorithm (AMGA), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm (NSGA2), we explored optimized design solutions for these joint components. Subsequently, we simulated joint assembly buckling during bus rollover crash scenarios to verify and analyze the optimized solutions in three-point bending simulations. Each joint component showcased a remarkable 30%–40% mass reduction while boosting energy absorption. Our design optimization method exhibits high efficiency and cost-effectiveness. Leveraging contemporary automation technology, the design optimization platform developed in this study is poised to facilitate intelligent optimization of lightweight metal components in future applications. Cable gland ; Structural engineering ; Computer science ; Deformation (meteorology) ; Multi-objective optimization ; Genetic algorithm ; Durability ; Mechanical engineering ; Materials science ; Engineering ; Composite material Titanium Alloys Microstructure and Properties ; Corrosion Behavior and Inhibition ; Cellular and Composite Structures
Authors: Avinash Padhi | S. J. Magar | Manoj Kumar Jena | AN Kavale | Sumanta Kumar Sahu
Source: Journal of Advances in Biology & Biotechnology (article, 2024-11-09)
Field: Materials Science — Materials Chemistry

Characterization of Zinc Nanoparticles Synthesized Using the Mushroom, Pleurotus sajor-caju Fungi including mushrooms contain various enzymes and biomolecules, making them an effective agent for synthesizing various nanoparticles. This study aimed to synthesize and characterize zinc nanoparticles using the zinc sulphate salt at the concentration of 1mM and the mushroom Pleurotus sajor-caju. The P. sajor-caju synthesized zinc nanoparticles were characterized using UV-Visible spectral analysis, Field Emission Scanning Electron Microscopy, and Fourier Transform Infrared Spectroscopy. They did not change the color and showed a characteristic surface plasmon resonance band peaking at 300nm. They were spherical and the particle size was found in the range of 7.29 nm to 13.27 nm. They showed ten bands at different stretching with wave numbers ranging between 675.17cm-1 and 3675.56cm-1. The presence of advantageous features in the zinc nanoparticles synthesized from P. sajor-caju revealed the usefulness of the synthesis of other metal nanoparticles from P. sajor-caju. Pleurotus ; Mushroom ; Zinc ; Nanoparticle ; Chemistry ; Food science ; Materials science ; Nanotechnology ; Organic chemistry Nanoparticles: synthesis and applications
Authors: Diana Hodyna | Anton A. Klipkov | Maryna Kachaeva | Yurii Shulha | Igor I. Gerus | Larysa Metelytsia | Vasyl Kovalishyn
Source: Chemistry & Biodiversity (article, 2024-06-04)
Field: Chemistry — Organic Chemistry

<i>In Silico</i> Design and <i>In Vitro</i> Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q<sup>2</sup>=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q<sup>2</sup>=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by <sup>1</sup>H, <sup>19</sup>F, and <sup>13</sup>C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis. Chemistry ; Antifungal ; In silico ; Bicyclic molecule ; Combinatorial chemistry ; In vitro ; Stereochemistry ; Pharmacology ; Microbiology ; Biochemistry ; Biology Synthesis and Characterization of Pyrroles ; Fluorine in Organic Chemistry ; Synthesis and Biological Evaluation
Authors: Khaled AbouAitah | Beom Soo Kim | Witold Łojkowski
Source: Nanotechnology Reviews (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Functionalized nanostructures and targeted delivery systems with a focus on plant-derived natural agents for COVID-19 therapy: A review and outlook Abstract The COVID-19 pandemic strongly stimulated research on anti-SARS-CoV-2 virus treatments. The present study reviews a nanotechnology approach to this task, i.e. , in other terms, a nanomedicine approach. Nanotechnology aims to create nanostructures or nanoparticles, also called nanoformulations, for targeted delivery of drugs, as well as improved drug release control. This approach is particularly promising to enhance the antiviral effect of natural pro-drugs. Here, we review several nanoformulations developed for the targeted delivery of medications against SARS-CoV-2. We draw special attention to repurposing strategies for known antiviral and natural therapies. Also, functionalized nanoparticles with specific targeting moieties and functional groups were discussed. The summary could motivate researchers to pursue more studies in this exciting area by seeking nanotechnology-based, cutting-edge, tailored delivery strategies for the SARS-CoV-2 virus. Coronavirus disease 2019 (COVID-19) ; 2019-20 coronavirus outbreak ; Focus (optics) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Natural (archaeology) ; Nanotechnology ; Medicine ; Computer science ; Virology ; Geography ; Materials science ; Physics ; Internal medicine ; Disease Nanoparticles: synthesis and applications ; Advanced Drug Delivery Systems ; SARS-CoV-2 and COVID-19 Research
Authors: Thomas J. Summers | Difan Zhang | Josiane A. Sobrinho | Ana de Bettencourt‐Dias | Roger Rousseau | Vassiliki‐Alexandra Glezakou | David C. Cantu
Source: Journal of Chemical Information and Modeling (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Pairing a Global Optimization Algorithm with EXAFS to Characterize Lanthanide Structure in Solution <i>Ensemble</i>-average sampling of structures from <i>ab initio</i> molecular dynamics (AIMD) simulations can be used to predict theoretical extended X-ray absorption fine structure (EXAFS) signals that closely match experimental spectra. However, AIMD simulations are time-consuming and resource-intensive, particularly for solvated lanthanide ions, which often form multiple nonrigid geometries with high coordination numbers. To accelerate the characterization of lanthanide structures in solution, we employed the Northwest Potential Energy Surface Search Engine (NWPEsSe), an adaptive-learning global optimization algorithm, to efficiently screen first-shell structures. As case studies, we examine two systems: Eu(NO<sub>3</sub>)<sub>3</sub> dissolved in acetonitrile with a terpyridine ligand (terpyNO<sub>2</sub>), and Nd(NO<sub>3</sub>)<sub>3</sub> dissolved in acetonitrile. The theoretical spectra for structures identified by NWPEsSe were compared to both experimental and AIMD-derived EXAFS spectra. The NWPEsSe algorithm successfully identified the proper solvation structure for both Eu(NO<sub>3</sub>)<sub>3</sub>(terpyNO<sub>2</sub>) and Nd(NO<sub>3</sub>)(acetonitrile)<sub>3</sub>, with the calculated EXAFS signals closely matching the experimental spectra for the Eu-ligand complex and showing good similarity for the Nd salt; the better agreement with the ligand-containing structure is attributed to a less dynamic coordination environment due to the rigid ligand. The key advantage of the global optimization algorithm lies in its ability to sample the coordination environment across the potential energy surface and reduce the time required to identify structures from generally a month to within a week. Additionally, this approach is versatile and can be adapted to characterize main-group metal complexes. Pairing ; Lanthanide ; Extended X-ray absorption fine structure ; Algorithm ; Computer science ; Chemistry ; Physics ; Materials science ; Condensed matter physics ; Absorption spectroscopy X-ray Diffraction in Crystallography ; Lanthanide and Transition Metal Complexes ; Thermal and Kinetic Analysis
Authors: Firas Shuaib | Guido Ori | Philippe Thomas | Olivier Masson | Assil Bouzid
Source: Journal of the American Ceramic Society (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

Multikernel similarity‐based clustering of amorphous systems and machine‐learned interatomic potentials by active learning Abstract We present a hybrid similarity kernel that exemplifies the integration of short‐ and long‐range descriptors via the use of an average kernel approach. This technique allows for a direct measure of the similarity between amorphous configurations, and when combined with an active learning (AL) spectral clustering approach, it leads to a classification of the amorphous configurations into uncorrelated clusters. Subsequently, a minimum size database is built by considering a small fraction of configurations belonging to each cluster and a machine learning interatomic potential (MLIP), within the Gaussian approximation scheme, is fitted by relying on a Bayesian optimization of the potential hyperparameters. This step is embedded within an AL loop that allows to sequentially increase the size of the learning database whenever the MLIP fails to meet a predefined energy convergence threshold. As such, MLIP are fitted in an almost fully automatized fashion. This approach is tested on two diverse amorphous systems that were previously generated using first‐principles molecular dynamics. Accurate potentials with less than 2 meV/atom root mean square energy error compared to the reference data are obtained. This accuracy is achieved with only 175 configurations sampling the studied systems at various temperatures. The robustness of these potentials is then confirmed by producing models with several thousands of atoms featuring a good agreement with reference ab initio and experimental data. Cluster analysis ; Gaussian ; Bayesian optimization ; Computer science ; Artificial intelligence ; Similarity (geometry) ; Amorphous solid ; Interatomic potential ; Molecular dynamics ; Machine learning ; Algorithm ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Prateek Verma | Minh–Hao Van | Xintao Wu
Source: arXiv (Cornell University) (preprint, 2024-05-01)
Field: Materials Science — Materials Chemistry

Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive performance on tasks such as natural image captioning, visual question answering (VQA), and spatial reasoning. Additionally, a universal segmentation model by Meta AI, Segment Anything Model (SAM) shows unprecedented performance at isolating objects from unforeseen images. Since medical experts, biologists, and materials scientists routinely examine microscopy or medical images in conjunction with textual information in the form of captions, literature, or reports, and draw conclusions of great importance and merit, it is indubitably essential to test the performance of VLMs and foundation models such as SAM, on these images. In this study, we charge ChatGPT, LLaVA, Gemini, and SAM with classification, segmentation, counting, and VQA tasks on a variety of microscopy images. We observe that ChatGPT and Gemini are impressively able to comprehend the visual features in microscopy images, while SAM is quite capable at isolating artefacts in a general sense. However, the performance is not close to that of a domain expert - the models are readily encumbered by the introduction of impurities, defects, artefact overlaps and diversity present in the images. Computer vision ; Artificial intelligence ; Image (mathematics) ; Microscope ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Cell Image Analysis Techniques