jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juan Manuel Rosselló | Hannes Hoeppe | Max Koch | Christiane Lechner | Markus Osterhoff | Malte Vassholz | J. Hagemann | Johannes Möller | Markus Scholz | Ulrike Boesenberg | Jörg Hallmann | Chan Kim | Alexey Zozulya | Wei Lu | Roman Shayduk | Anders Madsen | Tim Salditt | Robert Mettin
Source: Experiments in Fluids (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Jetting bubbles observed by x-ray holography at a free-electron laser: internal structure and the effect of non-axisymmetric boundary conditions The online version contains supplementary material available at 10.1007/s00348-023-03759-9. Shadowgraphy ; Optics ; Physics ; Dimensionless quantity ; Rotational symmetry ; Holography ; Bubble ; Jet (fluid) ; Mechanics ; Laser ; Free surface ; Materials science Ultrasound and Cavitation Phenomena ; Fluid Dynamics and Heat Transfer ; Electrohydrodynamics and Fluid Dynamics
Authors: Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier
Source: Advanced Electronic Materials (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Phase‐Field Study of Nanocavity‐Assisted Mechanical Switching in PbTiO<sub>3</sub> Thin Films Abstract Ferroelectric thin films hold significant promise in various nanoelectronic applications, demanding precise control over their domain structures. While electrical field‐driven polarization switching is currently employed, it often leads to undesirable side effects. In contrast, mechanical switching offers a voltage‐free alternative but faces challenges in thicker films. Recent breakthroughs have demonstrated stable mechanical switching in films up to 200 nm thick, attributed to the presence of nanocavities. These nanoscale voids are believed to facilitate domain transitions, serving as essential pinning centers. In this study, mechanical domain switching in thick ferroelectric films is investigated using phase‐field modeling, with a specific focus on evaluating the influence of nanocavities on domain stability. The effects of cavity parameters (size, depth, and dielectric properties) on mechanical switching stability under various applied pressures are systematically examined. The findings reveal the intricate interplay between these factors and outline the conditions for stable mechanical switching. Furthermore, phase‐field simulations are employed to showcase the energetic mechanisms governing nanocavity‐assisted mechanical switching, while also highlighting the pivotal role of these defects as pinning centers. This investigation elucidates the nanocavity‐assisted mechanical control of polarization and the potential for optimizing thin film design through nanocavity engineering, thus enabling mechanical switching across substantial film thicknesses. Materials science ; Ferroelectricity ; Polarization (electrochemistry) ; Dielectric ; Nanoscopic scale ; Thin film ; Switching time ; Mechanical load ; Optoelectronics ; Phase (matter) ; Nanotechnology ; Engineering physics ; Composite material Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Acoustic Wave Resonator Technologies
Authors: Gigin Lin | Ching‐Yi Hsieh | Ying‐Chieh Lai | Chun‐Chieh Wang | Yenpo Lin | Kuan‐Ying Lu | Wen‐Yen Chai | Albert P. Chen | Tzu‐Chen Yen | Shu‐Hang Ng | Chyong‐Huey Lai
Source: European Radiology Experimental (article, 2024-04-10)
Field: Chemistry — Spectroscopy

Hyperpolarized [1-13C]-pyruvate MRS evaluates immune potential and predicts response to radiotherapy in cervical cancer • Effective radiotherapy induces abscopal effect via altering immune metabolism. • Hyperpolarized <sup>13</sup>C MRS evaluates patients' immune potential non-invasively. • Pyruvate-to-lactate conversion in the spleen is elevated following radiotherapy. Medicine ; Radiation therapy ; Cervical cancer ; Nuclear medicine ; Spleen ; Immune system ; Internal medicine ; Magnetic resonance imaging ; Gastroenterology ; Cancer ; Oncology ; Pathology ; Radiology ; Immunology Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Advanced MRI Techniques and Applications
Authors: Xin Feng | Liang Liang Zhang | Yang Li | Yang Gao
Source: ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Electromechanical coupling characteristics of multilayered piezoelectric quasicrystal plates in an elastic medium Abstract Quasicrystals (QCs) have attracted tremendous attention of researchers for their unusual properties. In this paper, an exact electric‐elastic solution of the simply supported and multilayered three‐dimensional (3D) cubic piezoelectric quasicrystal (PQC) nanoplate with the nonlocal effect is derived. Based on the basic elasticity equation of 3D QCs, we construct the linear eigenvalue system in terms of the pseudo‐Stroh formalism, from which the general solutions of the extended displacements and stresses in any homogeneous layer can be obtained. The two‐parameter foundation model is utilized to simulate the interaction between the nanoplate and elastic medium. The propagator matrices are employed to connect the field variables at the upper interface to those at the lower interface of each layer. Based on the boundary conditions of the upper and lower surfaces of the laminate and foundation model, the solutions are employed to derive from the global propagator matrix. Compared with the conventional propagator matrix method, a new propagator method is reestablished to deal with numerical instabilities of the case of large aspect ratio and high‐order frequencies for QC laminates. Finally, typical numerical examples are presented to illustrate the influence of nonlocal parameters and elastic medium coefficients on phonon, phason, and electric variables of 3D PQC nanoplates. Quasicrystal ; Piezoelectricity ; Materials science ; Coupling (piping) ; Composite material ; Condensed matter physics ; Physics Quasicrystal Structures and Properties ; Mineralogy and Gemology Studies
Authors: Aditya Koneru | Adil Muhammed | Karthik Balasubramanian | Kiran Sasikumar | Sukriti Manna | Henry Chan | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry A (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. We apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes. Ab initio ; Anharmonicity ; Thermal conductivity ; Materials science ; Interatomic potential ; Statistical physics ; Molecular dynamics ; Thermal ; Inverse ; Ab initio quantum chemistry methods ; Nanotechnology ; Chemical physics ; Computer science ; Computational chemistry ; Physics ; Condensed matter physics ; Chemistry ; Thermodynamics ; Quantum mechanics ; Mathematics ; Molecule Thermal properties of materials ; Graphene research and applications ; 2D Materials and Applications
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: Gels (article, 2024-10-21)
Field: Materials Science — Biomaterials

Dual-Action Gemcitabine Delivery: Chitosan–Magnetite–Zeolite Capsules for Targeted Cancer Therapy and Antibacterial Defense 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 incorporating GEM into a chitosan matrix blended with magnetite and zeolite by ionic gelation. Physicochemical characterization was performed using HRTEM-ED, XRD, FESEM-EDS, FT-IR, TGA, encapsulation efficiency (%E.E.), and release profiles at pHs 7.4 and 5.0. Cell viability tests against 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. The macrocapsules showed a high GEM encapsulation efficiency of over 86% and controlled release over 24 h. Cell viability assays revealed that similar cytotoxic effects to free GEM were achieved with a 45-fold lower GEM concentration, suggesting reduced dosing requirements and potentially fewer side effects. Additionally, the macrocapsules demonstrated potent antimicrobial activity, reducing <i>Staphylococcus epidermidis</i> growth by over 90%. These results highlight the macrocapsules dual role as a chemotherapeutic and antimicrobial agent, offering a promising strategy for treating lung cancer in patients at risk of infectious diseases or who are immunosuppressed. Chitosan ; Staphylococcus epidermidis ; Viability assay ; Antimicrobial ; Cancer cell ; Materials science ; Nuclear chemistry ; Chemistry ; Staphylococcus aureus ; Microbiology ; Nanotechnology ; Cancer research ; Pharmacology ; Cell ; Cancer ; Medicine ; Bacteria ; Biochemistry ; Organic chemistry ; Internal medicine ; Biology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Bacteriophages and microbial interactions
Authors: Tao Song | Man Luo | Linjiang Chen | Yan Huang | Qing Zhu | Daobin Liu | Baicheng Zhang | Gang Zou | Fei Zhang | Weiwei Shang | Jun Jiang | Yi Luo
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

A multi-agent-driven robotic AI chemist enabling autonomous chemical research on demand The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multi-agent system, ChemAgents, based on an on-board Llama-3-70B LLM, capable of executing complex, multi-step experiments with minimal human intervention. It operates through a Task Manager agent that interacts with human researchers and coordinates four role-specific agents—Literature Reader, Experiment Designer, Computation Performer, and Robot Operator—each leveraging one of four foundational resources: a comprehensive Literature Database, an extensive Protocol Library, a versatile Model Library, and a state-of-the-art Automated Lab. We demonstrate its versatility and efficacy through six experimental tasks of varying complexity, ranging from straightforward synthesis and characterization to more complex exploration and screening of experimental parameters, culminating in the discovery and optimization of functional materials. Our multi-agent-driven robotic AI chemist showcases the potential of on-demand autonomous chemical research to drive unprecedented efficiencies, accelerate discovery, and democratize access to advanced experimental capabilities across academic disciplines and industries. Workflow ; Task (project management) ; Computer science ; Chemist ; Human–computer interaction ; Robot ; On demand ; Artificial intelligence ; Engineering ; Multimedia ; Systems engineering ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis
Authors: Yuqing Huang | Rihong Zhang | Xuesong He | Xuyang Zhi | Hao Wang | Xin Li | Feiyang Xu | Deguang Liu | H. Liang | Yi Li | Jianjun Cui | Zhe Liu | Shijin Wang | Guoping Hu | Guiquan Liu | Qi Liu | Defu Lian | Enhong Chen
Source: arXiv (Cornell University) (preprint, 2024-09-21)
Field: Materials Science — Materials Chemistry

ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess the performance of LLMs across a spectrum of chemical tasks varying in type and complexity. However, existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks. Specifically, ChemEval identified 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks which are informed by open-source data and the data meticulously crafted by chemical experts, ensuring that the tasks have practical value and can effectively evaluate the capabilities of LLMs. In the experiment, we evaluate 12 mainstream LLMs on ChemEval under zero-shot and few-shot learning contexts, which included carefully selected demonstration examples and carefully designed prompts. The results show that while general LLMs like GPT-4 and Claude-3.5 excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge. Conversely, specialized LLMs exhibit enhanced chemical competencies, albeit with reduced literary comprehension. This suggests that LLMs have significant potential for enhancement when tackling sophisticated tasks in the field of chemistry. We believe our work will facilitate the exploration of their potential to drive progress in chemistry. Our benchmark and analysis will be available at {\color{blue} \url{https://github.com/USTC-StarTeam/ChemEval}}. Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Anubhav Jain
Source: ChemRxiv (preprint, 2024-02-26)
Field: Materials Science — Materials Chemistry

Machine learning in materials research: developments over the last decade and challenges for the future The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance. Extrapolation ; Machine learning ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Support vector machine ; Artificial neural network ; Relevance (law) ; Data science ; Regression ; Feature (linguistics) ; Domain (mathematical analysis) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: X. Allen Li | Zhuoying Feng | Y. Chen | Weichen Dai | Zixu He | Yi Zhou | Shuhong Jiao
Source: arXiv (Cornell University) (preprint, 2024-07-24)
Field: Materials Science — Materials Chemistry

COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs To reduce the experimental validation workload for chemical researchers and accelerate the design and optimization of high-energy-density lithium metal batteries, we aim to leverage models to automatically predict Coulombic Efficiency (CE) based on the composition of liquid electrolytes. There are mainly two representative paradigms in existing methods: machine learning and deep learning. However, the former requires intelligent input feature selection and reliable computational methods, leading to error propagation from feature estimation to model prediction, while the latter (e.g. MultiModal-MoLFormer) faces challenges of poor predictive performance and overfitting due to limited diversity in augmented data. To tackle these issues, we propose a novel method COEFF (COlumbic EFficiency prediction via Fine-tuned models), which consists of two stages: pre-training a chemical general model and fine-tuning on downstream domain data. Firstly, we adopt the publicly available MoLFormer model to obtain feature vectors for each solvent and salt in the electrolyte. Then, we perform a weighted average of embeddings for each token across all molecules, with weights determined by the respective electrolyte component ratios. Finally, we input the obtained electrolyte features into a Multi-layer Perceptron or Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world dataset demonstrate that our method achieves SOTA for predicting CE compared to all baselines. Data and code used in this work will be made publicly available after the paper is published. Field (mathematics) ; Political science ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Cold Fusion and Nuclear Reactions ; Hydrogen Storage and Materials
Authors: Maya M. Martirossyan | Hongjin Du | Julia Dshemuchadse | Chrisy Xiyu Du
Source: MRS Advances (review, 2024-05-03)
Field: Materials Science — Materials Chemistry

A snapshot review on soft materials assembly design utilizing machine learning methods Materials science ; Snapshot (computer storage) ; Nanotechnology ; Soft materials ; Soft robotics ; Systems engineering ; Engineering drawing ; Artificial intelligence ; Computer science ; Engineering ; Robot Machine Learning in Materials Science ; Injection Molding Process and Properties ; Manufacturing Process and Optimization
Authors: Sarah I. Allec | Eric S. Muckley | Nathan S. Johnson | Christopher K. H. Borg | Dylan J. Kirsch | Joshua Martin | Rohit Pant | Ichiro Takeuchi | Andrew S. Lee | James E. Saal | Logan Ward | Apurva Mehta
Source: Integrating materials and manufacturing innovation (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community Workflow ; Computer science ; Data science ; Metadata ; Data management ; Timeline ; Automation ; Materials informatics ; Dashboard ; Property (philosophy) ; Data sharing ; Data mining ; Systems engineering ; Database ; World Wide Web ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Rajat Goyal
Source: arXiv (Cornell University) (review, 2024-04-21)
Field: Materials Science — Materials Chemistry

Exploring Quantum Materials &amp; Applications: A Review Current condensed matter research is centered on advanced materials and their distinctive features. The interest in Quantum materials (QMs) continues to increase without any decrease due to their novel phenomenon and potential as platforms for revolutionary new technologies in modern science and technology. This article emphasizes the exploration of diverse devices and applications facilitated by the unique properties of QMs. Encompassing fields like quantum computing, metrology, sensing, energy, and communication, the review highlights their transformative potential. In QMs, the emerging phenomena are governed by quantum confinement, strong electronic correlations, topology, and symmetry, which makes these materials apart, making them exceptional in their own regard. This paper emphasizes their unique properties, different types of QMs, various interdisciplinary applications, and integration with existing technologies. This study provides a concise overview of diverse discoveries and advancements, presenting a prospective outlook on QMs in multiple domains. Quantum ; Computer science ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics 2D Materials and Applications ; Chemical and Physical Properties of Materials ; Electronic and Structural Properties of Oxides