jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jordana Clara Gomes Pedreira | Edward Rodrigues de Oliveira Filho | Héllida Patrícia Oliveira Camilo Pereira | Anderson Felipe Caixêta Martins | Felipe Dayrell Schoepfer | Rafaella Ribeiro Gomes Nogueira | Jim Davis de Oliveira | Herik Jansen de Souza Pimentel | Josiely Anelise Mendonça | Paulo Otávio Magalhães Costa | Nara Márcia Amaro Domingos Guimarães | Vinícius Guimarães
Source: Brazilian Journal of Implantology and Health Sciences (article, 2024-02-04)
Field: Chemistry — Organic Chemistry

CARDIOVASCULAR EFFECTS OF FREE OR COMPLEXED LINALOOL WITH Β-CYCLODEXTRIN: A FOCUS FOR ANTIHYPERTENSIVE ACTION Investigating the cardiovascular effects of natural compounds, such as linalool, has aroused interest due to the potential impact on cardiovascular health. Linalool, a component present in several essential oils, has demonstrated promising pharmacological properties, including antihypertensive activity. However, its bioavailability and efficacy can be influenced by complexation with β-cyclodextrin, a strategy frequently used to improve the solubility and stability of bioactive substances. This study aimed to carry out a systematic review of the literature, exploring the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin. Objective: To investigate the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin, with emphasis on the antihypertensive action, through a systematic review of the literature. Methodology: The review was conducted according to PRISMA guidelines. The PubMed, Scielo and Web of Science databases were consulted, using the descriptors "linalool", "β-cyclodextrin", "cardiovascular effects", "antihypertensive" and "complexation". The inclusion criteria covered studies published in the last 10 years, focusing on in vivo experiments, clinical trials and systematic reviews. Articles unrelated to the topic, duplicate studies and those without peer review were excluded. Results: They revealed that linalool, when complexed with β-cyclodextrin, presented greater bioavailability and stability, enhancing its antihypertensive action. In vivo studies have demonstrated a significant reduction in blood pressure in hypertensive animal models. Furthermore, the complexation positively influenced the gastrointestinal absorption of linalool. These findings suggest that the complexed formulation may represent an effective approach to improving the cardiovascular effects of linalool. Conclusion: The systematic review highlights the relevance of complexing linalool to β-cyclodextrin as a strategy to enhance its antihypertensive effects. Understanding these mechanisms can contribute to the development of more effective pharmaceutical formulations in the management of hypertension, promoting advances in cardiovascular therapy. Linalool ; Action (physics) ; Pharmacology ; Chemistry ; Cyclodextrin ; Focus (optics) ; Antihypertensive drug ; Medicine ; Blood pressure ; Internal medicine ; Biochemistry ; Food science Edible Oils Quality and Analysis
Authors: Yu. G. Mikhalev
Source:  (article, 2024-12-19)
Field: Chemistry — Inorganic Chemistry

EFFECT OF THE CONCENTRATION OF ELECTROCHEMICALLY ACTIVE PARTICLES ON THE MASS TRANSFER PARAMETERS DURING POLARIZATION OF LIQUID METAL ELECTRODES IN MOLTEN CHLORIDES 908,71 9 The Republic of Korea 559,19 4 According to the analysis of the revenue structure of the largest companies by industry [12], it can be concluded that the first place is usually occupied by the oil and gas sector, which accounts for 36.9% of the total revenue of rating companies.The second place is taken by the Metals and Mining sector with 10.8%.The "Finance" sector came down to third place, reducing the share of revenue to 10.2%.Thus, the first three largest industries account for 57.9% of the total revenue of the rating.Such high positions of industries are primarily associated with the number of companies included in them.Revenue rose in all sectors except the "Consumer Product Manufacturing industry".The greatest dynamics of 89.1% were shown by bookmakers and lottery organizers.Behind them is the "Media and Internet" sector -37%.These indicators reflect the focus of the modern economy and the increasing introduction of media into our lives. Electrode ; Polarization (electrochemistry) ; Mass transfer ; Materials science ; Molten metal ; Metal ; Liquid metal ; Concentration polarization ; Electrochemistry ; Chemical engineering ; Composite material ; Metallurgy ; Chemistry ; Chromatography ; Membrane ; Physical chemistry Pigment Synthesis and Properties ; Molten salt chemistry and electrochemical processes ; Metallurgical Processes and Thermodynamics
Authors: S.Y. Noskova | Olga Babich | Lyubov Dyshlyuk | Oksana Ivina
Source:  (article, 2024-12-19)
Field: Chemistry — Inorganic Chemistry

ASSESSMENT OF THE MUTAGENICITY OF ENZYMATIC HYDROLYSATES OF COCONUT OIL 908,71 9 The Republic of Korea 559,19 4 According to the analysis of the revenue structure of the largest companies by industry [12], it can be concluded that the first place is usually occupied by the oil and gas sector, which accounts for 36.9% of the total revenue of rating companies.The second place is taken by the Metals and Mining sector with 10.8%.The "Finance" sector came down to third place, reducing the share of revenue to 10.2%.Thus, the first three largest industries account for 57.9% of the total revenue of the rating.Such high positions of industries are primarily associated with the number of companies included in them.Revenue rose in all sectors except the "Consumer Product Manufacturing industry".The greatest dynamics of 89.1% were shown by bookmakers and lottery organizers.Behind them is the "Media and Internet" sector -37%.These indicators reflect the focus of the modern economy and the increasing introduction of media into our lives. Coconut oil ; Hydrolysate ; Chemistry ; Enzyme ; Food science ; Chromatography ; Pulp and paper industry ; Biochemistry ; Hydrolysis ; Engineering Coconut Research and Applications ; Enzyme Catalysis and Immobilization ; Mass Spectrometry Techniques and Applications
Authors: Yi Yu | Hang Wang | Linlin Zong | Bo Chen | Yaqin Li | Xiaohui Yu
Source: ChemRxiv (preprint, 2024-11-19)
Field: Materials Science — Materials Chemistry

ChatMolData: a Multimodal Agent for Automatic Molecular Data Processing In recent years, the development of large language models (LLMs) has revolutionized various fields of natural science, yet their application in molecular data processing remains constrained due to the reliance on single-modality inputs and outputs. To bridge the gap between experimenters and computational tools, we introduce ChatMolData, a novel LLM-based multimodal agent designed to handle diverse molecular data forms, including molecular databases, images, structure-specific files and unstructured & structured documents. ChatMolData integrates the capabilities of LLMs (e.g., GPT-4 and GPT-3.5) with robust toolset that supports data retrieval, structuring, prediction, visualization, and search tasks. Our agent employs a systematic cycle of reasoning and action to efficiently process complex tasks in molecular science. The evaluation demonstrates that ChatMolData achieves over 90% accuracy for 128 diverse tasks, significantly lowering barriers for researchers without computer science background. Our findings highlight the agent's potential in advancing molecular research through improved data accessibility and usability. Computer science ; Human–computer interaction ; Artificial intelligence Machine Learning in Materials Science
Authors: Khiem Le | Nitesh V. Chawla | Chawla, Nitesh V.
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Materials Science — Materials Chemistry

AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing Molecular editing-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural language to drive the editing, straightforward prompting achieves limited accuracy. In this work, we propose AgentDrug, an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy. AgentDrug defines a nested refinement loop: the inner loop uses feedback from cheminformatics toolkits to validate molecular structures, while the outer loop guides the LLM with generic feedback and a gradient-based objective to steer the molecule toward property improvement. We evaluate AgentDrug on benchmarks with both single- and multi-property editing under loose and strict thresholds. Results demonstrate significant performance gains over previous methods. With Qwen-2.5-3B, AgentDrug improves accuracy by 20.7% (loose) and 16.8% (strict) on six single-property tasks, and by 7.0% and 5.3% on eight multi-property tasks. With larger model Qwen-2.5-7B, AgentDrug further improves accuracy on 6 single-property objectives by 28.9% (loose) and 29.0% (strict), and on 8 multi-property objectives by 14.9% (loose) and 13.2% (strict). Computer science ; Domain (mathematical analysis) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Khiem Le | Ting Hua | Chawla, Nitesh V.
Source: Open MIND (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Optimization Molecular optimization-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural language to drive the optimization, straightforward prompting achieves limited accuracy. In this work, we propose AgentDrug, an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy. AgentDrug defines a nested refinement loop: the inner loop uses feedback from cheminformatics toolkits to validate molecular structures, while the outer loop guides the LLM with generic feedback and a gradient-based objective to steer the molecule toward property improvement. We evaluate AgentDrug on benchmarks with both single- and multi-property optimization under loose and strict thresholds. Results demonstrate significant performance gains over previous methods. With Qwen-2.5-3B, AgentDrug improves accuracy by 20.7% (loose) and 16.8% (strict) on six single-property tasks, and by 7.0% and 5.3% on eight multi-property tasks. With larger model Qwen-2.5-7B, AgentDrug further improves accuracy on 6 single-property objectives by 28.9% (loose) and 29.0% (strict), and on 8 multi-property objectives by 14.9% (loose) and 13.2% (strict). Cheminformatics ; Workflow ; Task (project management) ; Process (computing) ; Loop (graph theory) ; Property (philosophy) ; Feedback loop Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Wonseok Lee | Yeonghun Kang | Taeun Bae | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2024-03-31)
Field: Chemistry — Inorganic Chemistry

Harnessing Large Language Model to collect and analyze Metal-organic framework property dataset This research was focused on the efficient collection of experimental Metal-Organic Framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced Large Language Models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more than 40,000 research articles, creating a comprehensive and ready-to-use dataset. The findings highlight the significant advantage of incorporating experimental data over relying solely on simulated data for enhancing the accuracy of machine learning predictions in the field of MOF research. Property (philosophy) ; Computer science ; Natural language processing ; Data science Metal-Organic Frameworks: Synthesis and Applications
Authors: Yifei Yang | Runhan Shi | Zuchao Li | Shu Jiang | Bao‐Liang Lu | Yang Yang | Hai Zhao
Source: arXiv (Cornell University) (preprint, 2024-08-19)
Field: Materials Science — Materials Chemistry

BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry. Despite the proliferation of computational tools over the past decade, AI-based systems often fall short in generalizing across diverse reaction types and exploring alternative synthetic pathways. This paper presents BatGPT-Chem, a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Integrating chemical tasks via a unified framework of natural language and SMILES notation, this approach synthesizes extensive instructional data from an expansive chemical database. Employing both autoregressive and bidirectional training techniques across over one hundred million instances, BatGPT-Chem captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions and exhibiting strong zero-shot capabilities. Superior to existing AI methods, our model demonstrates significant advancements in generating effective strategies for complex molecules, as validated by stringent benchmark tests. BatGPT-Chem not only boosts the efficiency and creativity of retrosynthetic analysis but also establishes a new standard for computational tools in synthetic design. This development empowers chemists to adeptly address the synthesis of novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science. We release our trial platform at \url{https://www.batgpt.net/dapp/chem}. Foundation (evidence) ; Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Engineering ; Geography ; Archaeology ; Chemistry Machine Learning in Materials Science
Authors: Sakhinana Sagar Srinivas | Geethan Sannidhi | Sreeja Gangasani | Chidaksh Ravuru | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-24)
Field: Materials Science — Surfaces, Coatings and Films

Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models Characterizing materials using electron micrographs is crucial in areas such as semiconductors and quantum materials. Traditional classification methods falter due to the intricatestructures of these micrographs. This study introduces an innovative architecture that leverages the generative capabilities of zero-shot prompting in Large Language Models (LLMs) such as GPT-4(language only), the predictive ability of few-shot (in-context) learning in Large Multimodal Models (LMMs) such as GPT-4(V)ision, and fuses knowledge across image based and linguistic insights for accurate nanomaterial category prediction. This comprehensive approach aims to provide a robust solution for the automated nanomaterial identification task in semiconductor manufacturing, blending performance, efficiency, and interpretability. Our method surpasses conventional approaches, offering precise nanomaterial identification and facilitating high-throughput screening. Micrograph ; Transformer ; Electron micrographs ; Semiconductor ; Artificial intelligence ; Computer science ; Computer vision ; Nanotechnology ; Materials science ; Optics ; Engineering ; Electron microscope ; Optoelectronics ; Physics ; Electrical engineering ; Scanning electron microscope Electron and X-Ray Spectroscopy Techniques
Authors: Shoichi Ishida | Tomohiro Sato | Teruki Honma | Kei Terayama
Source: ChemRxiv (preprint, 2024-04-22)
Field: Materials Science — Materials Chemistry

Large Language Models Open New Way of AI-Assisted Molecule Design for Chemists Recent advancements in artificial intelligence (AI)-based molecular design methodologies have offered synthetic chemists new ways to design functional molecules with their desired properties. While various AI-based molecule generators have significantly advanced toward practical applications, their effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that enables chemists to design new molecules using an AI-based molecule generator through only chat interactions, including automated construction of reward functions for the specified properties. Our study showcases the utility of ChatChemTS through de novo design cases involving chromophores and anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- and multiobjective molecule optimization scenarios, respectively. ChatChemTS is provided as an open-source package on GitHub at https://github.com/molecule-generator-collection/ChatChemTS. Computer science ; Generator (circuit theory) ; Chatbot ; Programming language ; Artificial intelligence ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Anna Ansone | Lı̅ga Avotiņa | M. Halitovs | Artūrs Zariņš
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

FTIR SPECTROMETRY MEASUREMENT APPROACHES FOR STUDY OF RADIATION-INDUCED EFFECTS IN ADVANCED CERAMIC BREEDER PEBBLES Breeder (animal) ; Ceramic ; Mass spectrometry ; Materials science ; Fourier transform infrared spectroscopy ; Nuclear engineering ; Radiochemistry ; Analytical Chemistry (journal) ; Chemistry ; Blanket ; Engineering ; Composite material ; Chromatography ; Chemical engineering Luminescence Properties of Advanced Materials ; Nuclear materials and radiation effects ; Glass properties and applications
Authors: Luca Foppiano | G. Lambard | Toshiyuki Amagasa | Masashi Ishii
Source: arXiv (Cornell University) (preprint, 2024-01-19)
Field: Materials Science — Materials Chemistry

Mining experimental data from Materials Science literature with Large Language Models: an evaluation study This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work. Baseline (sea) ; Computer science ; Information extraction ; Informatics ; Data science ; Natural language processing ; Domain (mathematical analysis) ; Information retrieval ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Xin Wang
Source: Theoretical Physics and Quantum Mechanics (article, 2024-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Macro to Quantum Scale: Progress and Applications of Magnetic Materials Research This article reviews and looks forward to the research progress and applications of magnetic materials from macro to quantum scales. First, the article combs the historical context of magnetic materials and discusses the preparation technology innovation and performance optimization strategies of magnetic materials, especially the significant improvement in the coercivity of rare earth permanent magnet materials and the innovative application of nanocomposite technology. Entering the nanoscale field, the advanced synthesis methods of nanomagnetic materials and the quantum effects they exhibit are analyzed. At the same time, the cutting-edge theoretical models of quantum-scale magnetic materials and their verification in experiments are introduced. In addition, the article also extensively discusses the practical applications of magnetic materials in information technology, biomedicine and other fields, and looks forward to future research directions and challenges, with special emphasis on the importance of environmental protection and sustainable development. Finally, the article highly affirms the value of cross-integration of magnetic materials with other disciplines, especially in the field of quantum computing. The great potential of magnetic materials as qubits provides new possibilities for future technological development. Macro ; Scale (ratio) ; Macroscopic scale ; Quantum ; Nanotechnology ; Engineering physics ; Systems engineering ; Physics ; Computer science ; Materials science ; Engineering ; Quantum mechanics Magnetic and transport properties of perovskites and related materials