jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenli Gao | Ying Guan | Huahong Huang | Shengquan Liu | Shengjie Ling | Liang Zhou
Source: Cellulose (article, 2024-07-06)
Field: Chemistry — Organic Chemistry

Advancing holocellulose content prediction in Chinese fir via transfer learning and Raman integration Raman spectroscopy ; Content (measure theory) ; Transfer of learning ; Materials science ; Water content ; Transfer (computing) ; Composite material ; Computer science ; Artificial intelligence ; Optics ; Physics ; Engineering ; Mathematics ; Geotechnical engineering Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yuly Andrea Guarín-González | G. Cabello | J. Reyes‐Gasga | Yanko Moreno-Navarro | Luis Vergara-González | Antonia Martin-Martín | Rodrigo López‐Muñoz | Galo Cárdenas-Triviño | Luis F. Barraza
Source: Preprints.org (preprint, 2024-09-11)
Field: Chemistry — Organic Chemistry

Gemcitabine Delivery System Based on Chitosan Capsules with Magnetite and Natural Zeolite (Clinoptilolite). Synthesis, Characterization and Evaluation of Its Anticancer and Bactericidal Properties Cancer and infectious diseases are two of the world's major public health problems. Gemcitabine (GEM) is an effective chemotherapeutic agent against several types of cancer. In this study, we developed macrocapsules by incorporating GEM into a chitosan matrix blended with magnetite and zeolite. The physico-chemical characterization of the precursor materials and the new delivery system was carried out by HRTEM-ED, XRD, FESEM-EDS, FT-IR, TGA, encapsulation efficiency (%E.E.) and release profiles at pH 7.4 and 5.0. Cell viability tests on A549 and H1299 cell lines and microbiological properties against staphylococcal strains were performed. Our results revealed the successful production of hemispherical capsules with an average diameter of 1.22 mm, a rough surface and characteristic FT-IR material interaction bands. GEM encapsulation rates exceeded 80%, and release profiles were well-controlled over 24 hours at tested pH levels. Notably, cell viability in H1299 cells was lower than the free GEM control. Moreover, the formulation exhibited potent antimicrobial activity against Staphylococcus epidermidis, highlighting its dual action as a chemotherapeutic and antimicrobial agent. This dual effect underscores the potential of these macrocapsules as innovative delivery system for treating lung cancer in patients who are also at risk of infectious diseases and who are immunosuppressed. Clinoptilolite ; Chitosan ; Zeolite ; Gemcitabine ; Magnetite ; Characterization (materials science) ; Delivery system ; Chemistry ; Nuclear chemistry ; Chemical engineering ; Materials science ; Medicine ; Nanotechnology ; Metallurgy ; Pharmacology ; Organic chemistry ; Internal medicine ; Cancer ; Engineering Synthesis of Tetrazole Derivatives ; Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications
Authors: Neng-Zhi Yao | Bin Wang | Hao Wang | Chenlong Wu | Tien‐Mo Shih | Xuesheng Wang
Source: Microsystems & Nanoengineering (article, 2024-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On-demand zero-drag hydrodynamic cloaks resolve D'Alembert paradox in viscous potential flows The possibility of freely manipulating flow in accordance with humans will remain indispensable for breakthroughs in fields such as microfluidics, nanoengineering, and biomedicines, as well as for realizing zero-drag hydrodynamics, which is essential for alleviating the global energy crisis. However, persistent challenges arise from the D'Alembert paradox and the unresolved Navier-Stokes solutions, known as the Millennium Problem. These obstacles also complicate the development of hydrodynamic zero-drag cloaks across diverse Reynolds numbers. Our research introduces a paradigm for such cloaks, relying exclusively on isotropic and homogeneous viscosity. Through experimental and numerical validations, our cloaks exhibit zero-drag properties, effectively resolving the D'Alembert paradox in viscous potential flows. Moreover, they possess the capability to activate or deactivate hydrodynamic concealment at will. Our analysis emphasizes the critical role of vorticity manipulation in realizing cloaking effects and drag-reduction technology. Therefore, controlling vorticity emerges as a pivotal aspect for future active hydrodynamic zero-drag cloak designs. In conclusion, our study challenges the prevailing belief in the impossibility of zero drag, offering valuable insights into invisibility characteristics in fluid mechanics with implications for microfluidics, biofluidics demanding the drug release or biomolecules transportation accurately and timely, and hypervelocity technologies. Drag ; Physics ; Classical mechanics ; Reynolds number ; Mechanics ; Vorticity ; Drag coefficient ; Microfluidics ; Isotropy ; Turbulence ; Vortex ; Optics Metamaterials and Metasurfaces Applications ; Advanced Materials and Mechanics ; Orbital Angular Momentum in Optics
Authors: Qiaohui Wang | Yi‐Fan Liu | Lei Zhou | Shi‐Zhong Yang | Ji‐Dong Gu | Bo‐Zhong Mu
Source: Laboratories (article, 2024-12-25)
Field: Chemistry — Analytical Chemistry

An Improved Method for Measuring the Distribution of Water Droplets in Crude Oil Based on the Optical Microscopy Technique The distribution of water droplets in crude oil is one of the key issues involved in the processes of oil extraction and transportation, and these water droplets might also be habitats for microorganisms in oil reservoirs. However, it is still a challenge to observe and measure the distribution of water droplets in crude oil quickly and directly. In this work, an improved method based on the optical microscopy technique is introduced, which is named the Plate Pressing (PP) method and can observe and determine the distribution of water droplets in crude oil directly. The reliability of this method was verified by comparing the results with those of a computed tomography (CT) scan, indicating that the PP method can measure the distribution of water droplets accurately. Meanwhile, the total number and size distribution of water droplets in three crude oil samples from different oilfields were obtained by the PP method, which consolidated the idea that the PP method is capable of determining the distribution of the water droplets in crude oil directly and is suitable for the statistical analysis of water droplets in multiple samples of crude oil. Microscopy ; Optical microscope ; Materials science ; Distribution (mathematics) ; Nanotechnology ; Environmental science ; Petroleum engineering ; Analytical Chemistry (journal) ; Optics ; Chromatography ; Chemistry ; Composite material ; Engineering ; Mathematics ; Physics ; Scanning electron microscope Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Fan Xu | Xumin Chang | Jiayong Xiao | Yixin Zhang | Feng Liu | Zheng Sun | Ning Mao | Nikolai Peshcherenko | Jiayi Li | Kenji Watanabe | Takashi Taniguchi | Bingbing Tong | Lü Li | Jinfeng Jia | Dong Qian | Zhiwen Shi | Yang Zhang | Xiaoxue Liu | Shengwei Jiang | Tingxin Li
Source: arXiv (Cornell University) (preprint, 2024-06-14)
Field: Materials Science — Materials Chemistry

Interplay between topology and correlations in the second moiré band of twisted bilayer MoTe2 Topological flat bands formed in two-dimensional lattice systems offer unique opportunity to study the fractional phases of matter in the absence of an external magnetic field. Celebrated examples include fractional quantum anomalous Hall (FQAH) effects and fractional topological insulators. Recently, FQAH effects have been experimentally realized in both the twisted bilayer MoTe2 (tMoTe2) system and the rhombohedral stacked multilayer graphene/hBN moiré systems. To date, experimental studies mainly focus on the first moiré flat band, except a very recent work that studied novel transport properties in higher moiré bands of a 2.1° tMoTe2 device. Here, we present the systematical transport study of approximately 3° tMoTe2 devices, especially for the second moiré band. At ν = -2 and -4, time-reversal-symmetric single and double quantum spin Hall states formed, consistent with the previous observation in 2.1° tMoTe2 device. On the other hand, we observed ferromagnetism in the second moiré band, and a Chern insulator state driven by out-of-plane magnetic fields at ν = -3. At ν = -2.2 to -2.7, finite temperature resistivity minimum with 1/T scaling at low temperatures, and large out-of-plane negative magnetoresistance have been observed. Applying out-of-plane electric field can induce quantum phase transitions at both integer and fractional filling factors. Our studies pave the way for realizing tunable topological states and other unexpected magnetic phases beyond the first moiré flat band based on twisted MoTe2 platform. Moiré pattern ; Topology (electrical circuits) ; Bilayer ; Condensed matter physics ; Physics ; Materials science ; Optics ; Mathematics ; Chemistry ; Combinatorics ; Membrane 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Amir Hossein Abolmasoumi | Bogdan Marinescu
Source: arXiv (Cornell University) (preprint, 2024-06-19)
Field: Chemistry — Physical and Theoretical Chemistry

State Anti-windup: A New Methodology for Tackling State Constraints at Both Synthesis and Implementation Levels The anti-windup compensation typically addresses strict control limitations in control systems. However, there is a clear need for an equivalent solution for the states/outputs of the system. This paper introduces a novel methodology for the state anti-windup compensator. Unlike state-constrained control methods, which often focus on incorporating soft constraints into the design or fail to react adequately to constraint violations in practical settings, the proposed methodology treats state constraints as implement-oriented soft-hard constraints. This is achieved by integrating a saturation block within the structure of the safety compensator, referred to as the state anti-windup (SANTW) compensator. Similar to input anti-windup schemes, the SANTW design is separated from the nominal controller design. The problem is formulated as a disturbance rejection one to directly minimize the saturation. The paper develops two Hinf optimization frameworks using frequency-domain solutions and linear matrix inequalities. It then addresses constraints on both inputs and states, resulting in a unified Input-State Anti-windup (IS-ANTW) compensator synthesized using non-smooth Hinf optimization. This method also offers the flexibility of having a fixed-order compensator, crucial in many practical applications. Additionally, the study evaluates the proposed compensator's performance in managing current fluctuations from renewable energy sources during grid faults, demonstrating its effectiveness through detailed Electromagnetic Transient (EMT) simulations of grid-connected DC-AC converters. State (computer science) ; Computer science ; Process management ; Business ; Programming language History and advancements in chemistry
Authors: Mohamed Dhouioui | Jonathan Barnoud | Rhoslyn Roebuck Williams | Harry J. Stroud | P. J. Bates | David R. Glowacki
Source: arXiv (Cornell University) (preprint, 2024-09-11)
Field: Materials Science — Materials Chemistry

AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems Molecular dynamics (MD) simulations are a crucial computational tool for researchers to understand and engineer molecular structure and function in areas such as drug discovery, protein engineering, and material design. Despite their utility, MD simulations are expensive, owing to the high dimensionality of molecular systems. Interactive molecular dynamics in virtual reality (iMD-VR) has recently emerged as a "human-in-the-loop" strategy for efficiently navigating hyper-dimensional molecular systems. By providing an immersive 3D environment that enables visualization and manipulation of real-time molecular simulations running on high-performance computing architectures, iMD-VR enables researchers to reach out and guide molecular conformational dynamics, in order to efficiently explore complex, high-dimensional molecular systems. Moreover, iMD-VR simulations generate rich datasets that capture human experts' spatial insight regarding molecular structure and function. This paper explores the use of researcher-generated iMD-VR datasets to train AI agents via imitation learning (IL). IL enables agents to mimic complex behaviours from expert demonstrations, circumventing the need for explicit programming or intricate reward design. In this article, we review IL across robotics and Multi-agents systems domains which are comparable to iMD-VR, and discuss how iMD-VR recordings could be used to train IL models to interact with MD simulations. We then illustrate the applications of these ideas through a proof-of-principle study where iMD-VR data was used to train a CNN network on a simple molecular manipulation task; namely, threading a small molecule through a nanotube pore. Finally, we outline future research directions and potential challenges of using AI agents to augment human expertise in navigating vast molecular conformational spaces. Imitation ; Perspective (graphical) ; Human–computer interaction ; Computer science ; Cognitive science ; Psychology ; Artificial intelligence ; Neuroscience Machine Learning in Materials Science
Authors: N. M. Anoop Krishnan | Indrajeet Mandal | Jitendra Soni | Mohd Zaki | Morten M. Smedskjær | Katrin Wondraczek | Lothar Wondraczek | Nitya Nand Gosvami
Source: Research Square (preprint, 2024-12-18)
Field: Materials Science — Materials Chemistry

Autonomous Microscopy Experiments through Large Language Model Agents Microscopy ; Computer science ; Nanotechnology ; Materials science ; Optics ; Physics Machine Learning in Materials Science
Authors: Yasmine Nahal | Janosch Menke | Julien Martinelli | Markus Heinonen | Mikhail Kabeshov | Jon Paul Janet | Eva Nittinger | Ola Engkvist | Samuel Kaski
Source: Research Square (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

Human-in-the-loop Active Learning for Goal-oriented Molecule Generation Computer science ; Oracle ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Bridging (networking) ; Generative model ; Generative grammar ; Ranking (information retrieval) ; Quantitative structure–activity relationship ; Process (computing) ; Data mining ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Anandakumar Haldorai | Shrinand Anandakumar
Source: Journal of Computational Intelligence in Materials Science (article, 2024-09-15)
Field: Materials Science — Materials Chemistry

Revolutionizing Materials Research with Quantum Computation: The Role of High-Throughput Experimental Materials Databases The combination of quantum computing (QC) with artificial intelligence (AI) has the potential to significantly transform several industries and enhance quality of life. AI applications, such as autonomous vehicles and image recognition, significantly depend on supervised learning, which is a type of machine learning. Researchers are now examining the intersection of AI and multi-agent planning systems (MAPS) using quantum algorithms and game theory. Quantum machine learning and optimization techniques use the advantages of QC to accelerate training and optimization processes, drawing inspiration from quantum physics. With machine learning methods, it is now becoming commonplace in materials research to make predictions about the characteristics of inorganic solid-state materials. However, a significant issue lies in the limited availability of datasets. This article explores the structure and characteristics of the High Throughput Experimental Materials (HTEM) Database, which contains a vast amount of experimental data on inorganic materials obtained using high-throughput technologies. The HTEM Database provides a significant resource by offering comprehensive EDIM built using high-throughput exploration methodologies. The database offers a multitude of search and visualization functionalities and may be accessible using a web-based interface. Regular updates are performed on the HTEM DB to integrate new data and mitigate processing issues. Throughput ; Computer science ; Quantum computer ; Database ; Intersection (aeronautics) ; Quantum ; Computation ; Artificial intelligence ; Machine learning ; Engineering ; Algorithm Machine Learning in Materials Science ; Data Quality and Management ; Scientific Computing and Data Management
Authors: A. Ramesh Babu
Source: arXiv (Cornell University) (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories Assessing different material properties to predict specific attributes, such as band gap, resistivity, young modulus, work function, and refractive index, is a fundamental requirement for materials science-based applications. However, the process is time-consuming and often requires extensive literature reviews and numerous experiments. Our study addresses these challenges by leveraging machine learning to analyze material properties with greater precision and efficiency. By automating the data extraction process and using the extracted information to train machine learning models, our developed model, SciQu, optimizes material properties. As a proof of concept, we predicted the refractive index of materials using data extracted from numerous research articles with SciQu, considering input descriptors such as space group, volume, and bandgap with Root Mean Square Error (RMSE) 0.068 and R2 0.94. Thus, SciQu not only predicts the properties of materials but also plays a key role in self-driving laboratories by optimizing the synthesis parameters to achieve precise shape, size, and phase of the materials subjected to the input parameters. Computer science ; Data mining ; Engineering Machine Learning in Materials Science
Authors: Siyu Liu | Tongqi Wen | Beilin Ye | Zhuoyuan Li | David J. Srolovitz
Source: arXiv (Cornell University) (preprint, 2024-11-19)
Field: Materials Science — Materials Chemistry

Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a new opportunity for material property predictions, complementing experimental measurements and multi-scale computational methods. We focus on predicting the elastic constant tensor, as a case study, and develop domain-specific LLMs for predicting elastic constants and for materials discovery. The proposed ElaTBot LLM enables simultaneous prediction of elastic constant tensors, bulk modulus at finite temperatures, and the generation of new materials with targeted properties. Moreover, the capabilities of ElaTBot are further enhanced by integrating with general LLMs (GPT-4o) and Retrieval-Augmented Generation (RAG) for prediction. A specialized variant, ElaTBot-DFT, designed for 0 K elastic constant tensor prediction, reduces the prediction errors by 33.1% compared with domain-specific, material science LLMs (Darwin) trained on the same dataset. This natural language-based approach lowers the barriers to computational materials science and highlights the broader potential of LLMs for material property predictions and inverse design. Property (philosophy) ; Constant (computer programming) ; Tensor (intrinsic definition) ; Computer science ; Materials science ; Mathematics ; Programming language ; Geometry Machine Learning in Materials Science
Authors: Jing Lin | Danfeng Zhao | San Lu | Rushuo Li | Xinmeng Xu | Zhaokun Wang | Wenqin Li | Yujing Ji | Hongyi Gao | Lei Shi | Ge Wang
Source: ChemRxiv (preprint, 2024-12-31)
Field: Materials Science — Materials Chemistry

A Conversational Large-Language-Model AI Agent for Synthesis of Metal-Organic Frameworks for Efficient Hydrogenation of Dicyclopentadiene Metal-Organic Frameworks (MOFs), renowned for their structural diversity and design flexibility, have demonstrated considerable potential in catalysis. However, the traditional trial-and-error approach is inefficient for optimizing catalytic performance in synthetic space. In this work, we propose MOFsyn Agent, a framework leveraging Large Language Models (LLMs) to guide the efficient synthesis and performance optimization of MOFs. MOFsyn Agent is an integrated system comprising two core components: data automatic analyzer and material mechanism analyzer. The system can be driven to perform data analysis operations such as task disassembly, code writing and execution by simple natural language commands only. Furthermore, the incorporation of the Retrieval-Augmented Generation (RAG) technology enables MOFsyn Agent to access external knowledge bases in real time, significantly enhancing its domain applicability in MOFs synthesis. Taking Ni@UiO-66(Ce) as an illustrative example, MOFsyn Agent conducted a comprehensive analysis of the relationships between synthetic conditions, structural characteristics, and catalytic performance. Through machine learning (ML), key features such as nickel content and metallic nickel were identified. Notably, MOFsyn Agent not only realizes the numerical prediction based on ML, but also optimizes the synthesis scheme from the textual level, and creatively proposes a step-by-step reduction strategy, which replaces the traditional one-pot direct reduction method. Experimental results demonstrated that the optimized Ni@UiO-66(Ce) can achieve saturated hydrogenation of dicyclopentadiene under more mild conditions (80 °C, 2 MPa) in only 1 h, validating the accuracy and reliability of the proposed method. This research provides an efficient tool for material intelligent synthesis to researchers without a programming background, accelerating the development process of new materials. Computer science ; Dicyclopentadiene ; Reduction (mathematics) ; Flexibility (engineering) ; Materials science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Hydrodesulfurization Studies