jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hasti Sarabikia | Razieh Souri | Mohsen Safaei
Source: Journal of Kermanshah University of Medical Sciences (article, 2023-11-17)
Field: Materials Science — Biomaterials

Evaluating the Anticancer Effects of Polyvinyl Alcohol/Magnesium Oxide Bionanocomposite on Human Oral Cancer Cells Background: Oral squamous cell carcinoma (OSCC) represents the most widespread type of squamous cell carcinoma (SCC) in the head and neck region, comprising 90% of all cases of oral cancer. Treatments based on immunological methods, cell therapy, gene therapy, and nanotechnology-based methods have been performed in addition to conventional treatment methods. Objectives: This study aimed to investigate the anticancer effects of polyvinyl alcohol/MgO nanocomposite on human oral cancer cells. Methods: In this study, the structure and size of nanoparticles and nanocomposite were identified using scanning electron microscope (SEM) analysis after synthesizing PVA/MgO nanocomposite. The anticancer activity of the synthesized nanocomposite was investigated against oral cancer cells of KB type. Results: SEM analysis confirmed the proper synthesis of PVA/MgO nanocomposite. MTT test showed that the nanocomposite synthesized in the concentration range of 25-200μl has maximum anticancer properties against KB cancer cells. In addition, PVA/MgO nanocomposite influenced the apoptosis pathway of KB cancer cells by increasing ROS, decreasing mitochondrial membrane potential, and increasing the activities of caspases 3 and 7. Conclusions: Based on the results, the PVA/MgO nanocomposite showed considerable potential as a viable contender for novel anticancer interventions. Nanocomposite ; Polyvinyl alcohol ; Cancer cell ; MTT assay ; Cancer ; Apoptosis ; Medicine ; Basal cell ; Materials science ; Cancer research ; Pharmacology ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Internal medicine ; Biochemistry ; Composite material Electrospun Nanofibers in Biomedical Applications ; Nanoplatforms for cancer theranostics ; MicroRNA in disease regulation
Authors: В. Р. Хайруллина | Yuliya Martynova
Source: Molecules (article, 2023-11-22)
Field: Chemistry — Organic Chemistry

Quantitative Structure–Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC<sub>50</sub> = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC<sub>50</sub> values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK. Quantitative structure–activity relationship ; chEMBL ; Herpes simplex virus ; Guanine ; Chemistry ; Virtual screening ; Structure–activity relationship ; Molecular descriptor ; Computational biology ; Combinatorial chemistry ; Stereochemistry ; Pharmacology ; Virus ; Biochemistry ; In vitro ; Biology ; Drug discovery ; Virology Click Chemistry and Applications ; Synthesis and biological activity ; HIV/AIDS drug development and treatment
Authors: Joshua Schrier | Alexander J. Norquist | Tonio Buonassisi | Jakoah Brgoch
Source: ChemRxiv (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for realizing new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data is limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it, and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows that should enable the discovery of exceptional molecules and materials. Chemical space ; Serendipity ; Computer science ; Interpretability ; Workflow ; Data science ; Identification (biology) ; Task (project management) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Systems engineering ; Drug discovery ; Chemistry ; Engineering ; Epistemology ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Michael H. Prince | Henry Chan | Aikaterini Vriza | Tao Zhou | Varuni K. Sastry | Matthew T. Dearing | Ross Harder | Rama K. Vasudevan | Mathew J. Cherukara
Source: arXiv (Cornell University) (preprint, 2023-12-03)
Field: Materials Science — Materials Chemistry

Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities' users and accelerate scientific output. Scientific instrument ; Computer science ; Documentation ; Leverage (statistics) ; Data science ; Context (archaeology) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Larry Lüer | Ian Marius Peters | Ana‐Sunčana Smith | Eva Dorschky | Bjoern M. Eskofier | Frauke Liers | Jörg Franke | Martin Sjarov | M. Brossog | Dirk M. Guldi | Andreas Maier | Christoph J. Brabec
Source: arXiv (Cornell University) (preprint, 2023-05-12)
Field: Materials Science — Materials Chemistry

A Digital Twin to overcome long-time challenges in Photovoltaics The recent successes of emerging photovoltaics (PV) such as organic and perovskite solar cells are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant innovation to match contradicting requirements such as performance, longevity and recyclability. The rate of innovation, as of today, is limited by a lack of design principles linking chemical motifs to functional microscopic structures, and by an incapacity to experimentally access microscopic structures from investigating macroscopic device properties. In this work, we envision a layout of a Digital Twin for PV materials aimed at removing both limitations. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with mathematical models derived from the underlying physics and digital twin concepts from the engineering world. This layout will allow using high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters from HT data. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships, enabling inverse molecular design, that is, predicting the optimal chemical structure and process conditions to build PV devices satisfying a multitude of requirements at the same time. After motivating our proposed layout of the digital twin with causal relationships in material science, we discuss the current state of the enabling technologies, already being able to yield insight from HT data today. We identify open challenges with respect to the multiscale nature of PV materials and the needed volume and diversity of data, and mention promising approaches to address these challenges. Computer science ; Photovoltaics ; Commercialization ; Process (computing) ; Data science ; Industrial engineering ; Nanotechnology ; Photovoltaic system ; Engineering ; Materials science ; Electrical engineering Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Tao Tang | Xu Wang | Zenghong Ma | Weiwei Hong | Gaohong Yu | Bingliang Ye
Source: International journal of agricultural and biological engineering (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Lightweight detection method for lotus seedpod in natural environment In order to solve the problems of the current target detection algorithms, such as poor discrimination of occluded targets, multiple parameters, complex networks, large amounts of computation, and not conducive to the deployment of mobile terminals, a lightweight lotus seedpod detection method based on YOLOv5s model was proposed in this study. First, the dataset was augmented by using a combination of offline and online augmentation, which improved the adaptability and robustness of the model in complex environments. Then, a lightweight Ghost convolution module was introduced to replace the original convolution, and a lightweight bidirectional feature pyramid network was designed, which could enhance the feature extraction and fusion capability of the network and reduce the amount of calculation and model size; On this basis, the combination of WIoU loss function and Mish activation function was adopted to improve the accuracy of feature extraction. Finally, the knowledge distillation training strategy was used to ensure the proposed lightweight model has the learning ability of a complex network model, improving the recall and precision of model detection. The results of the ablation study show that the proposed method effectively improves the detection performance of the YOLOv5s model for lotus seedpods. The mean average precision of the improved model was 89.7%, compared with the original YOLOv5s model increased by 2.8%, and the parameters and FLOPs were reduced by 2.36M and 7.3G, respectively. Compared with other detection algorithm models, the proposed algorithm model has the advantages of less computation, smaller model size, and higher detection precision. Therefore, the proposed improved optimization method based on the YOLOv5s model can effectively detect lotus seedpods, which provides theoretical research and technical support for intelligent picking of lotus seedpods in the actual operating environment. Keywords: lotus seedpod detection, deep Learning, data augmentation, lightweight, knowledge distillation, natural environment DOI: 10.25165/j.ijabe.20231606.8281 Citation: Tang T, Wang X, Ma Z H, Hong W W, Yu G H, Ye B L. Lightweight detection method for lotus seedpod in natural environment. Int J Agric & Biol Eng, 2023; 16(6): 197–206. Lotus ; Natural (archaeology) ; Environmental science ; Biology ; Botany Chromatography in Natural Products
Authors: Thonny Centeno Utos | Cassiana Alves Ferreira | Janet G. Inga | Andrés Veléz Echeverry | Raul Huacho Buendia | Osir Daygor Vidal Alanya | Sthefany Madjory Moya | Danessa Clarita Reyes | Walter Goytendia Centeno | Benji Steve Ascue | Mário Tomazello Filho
Source: Revista de Biología Tropical (article, 2023-08-24)
Field: Chemistry — Organic Chemistry

Herramientas de corte para optimizar parámetros de clasificación de especies maderables con redes neuronales convolucionales Introducción: La gran diversidad de especies maderables tropicales demanda el desarrollo de nuevas tecnologías de identificación con base en sus patrones o características anatómicas. La aplicación de redes neuronales convolucionales (CNN) para el reconocimiento de especies maderables tropicales se ha incrementado en los últimos años por sus resultados prometedores. Objetivo: Evaluamos la calidad de las imágenes macroscópicas con tres herramientas de corte para mejorar la visualización y distinción de las características anatómicas en el entrenamiento del modelo CNN. Métodos: Recolectamos las muestras entre el 2020 y 2021 en áreas de explotación forestal y aserraderos de Selva Central, Perú. Luego, las dimensionamos y, previo a la identificación botánica y anatómica, las cortamos en secciones transversales. Generamos una base de datos de imágenes macroscópicas de la sección transversal de la madera, a través del corte, con tres herramientas para ver su rendimiento en el laboratorio, campo y puesto de control. Resultados: Usamos tres herramientas de corte para obtener una alta calidad de imágenes transversales de la madera; obtuvimos 3 750 imágenes macroscópicas con un microscopio portátil que corresponden a 25 especies maderables. El cuchillo “Tramontina” es duradero, pero pierde el filo con facilidad y se necesita una herramienta para afilar, el cúter retráctil “Pretul” es adecuado para madera suave y dura en muestras pequeñas de laboratorio; el cuchillo “Ubermann” es apropiado para el campo, laboratorio y puesto de control, porque tiene una envoltura duradera y láminas intercambiables en caso de pérdida de filo. Conclusiones: La calidad de las imágenes es decisiva en la clasificación de especies maderables, porque permite una mejor visualización y distinción de las características anatómicas en el entrenamiento con los modelos de red neuronal convolucional EfficientNet B0 y Custom Vision, lo cual se evidenció en las métricas de precisión. Humanities ; Geography ; Cartography ; Art Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Microbial Metabolism and Applications
Authors: Davi Fébba | Kevin R. Talley | K.E. Johnson | Stephen T. Schaefer | Sage R. Bauers | John S. Mangum | Rebecca W. Smaha | Andriy Zakutayev
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission Autonomous experimentation has emerged as an efficient approach to accelerate the pace of materials discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of emission lines monitored during the co-sputtering from elemental Zn and Ti targets in N$_2$ atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined composition, by minimizing the absolute error between desired and measured emission signals. We validated our approach by autonomously fabricating Zn$_x$Ti$_{1-x}$N$_y$ films with deviations from the targeted cation composition within relative 3.5 %, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity depends non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature. Sputtering ; Thin film ; Sputter deposition ; Materials science ; Computer science ; Optoelectronics ; Nanotechnology Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Mass Spectrometry Techniques and Applications
Authors: Max Dreger | Mohammad J. Eslamibidgoli | Michael Eikerling | Kourosh Malek
Source: Journal of Materials Informatics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations Journal of Materials Informatics (JMI) is an international peer-reviewed, open access journal. The journal provides a platform for presentation, publication, and exchange of materials informatics research, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge. Workflow ; Informatics ; Computer science ; Data science ; Presentation (obstetrics) ; Materials informatics ; Engineering informatics ; Knowledge graph ; World Wide Web ; Database ; Information retrieval ; Health informatics ; Engineering Machine Learning in Materials Science
Authors: Rebekah Duke | Siamak Mahmoudi | Aman Preet Kaur | Vinayak Bhat | Ian C. Dingle | Nathan C. Stumme | Scott K. Shaw | David Eaton | A. Vego | Chad Risko
Source: ChemRxiv (preprint, 2023-07-31)
Field: Materials Science — Materials Chemistry

Towards Reproducible and Automated Electrochemistry Ensuring scientific reproducibility holds increasing importance in chemistry as it underpins the credibility and integrity of research findings. However, reproducing experiments and measurements is often hindered by incomplete or ambiguous procedural data in scientific literature. Additionally, the time-consuming process of data generation limits the scale of reproducing experiments. Growing efforts towards automation will contribute to enhancing reproducibility. Nevertheless, both manual reproducibility efforts and the development of automated experiments and measurements will require improved methods for recording and sharing experimental procedures in machine-readable formats. Here we develop ExpFlow, a data sharing and reporting software that currently targets electrochemistry. The ExpFlow software allows researchers to systematically encode laboratory procedures through a graphical user interface that operates like a fill-in-the-blank lab notebook. Built-in calculators derive properties such as diffusion coefficient and charge-transfer rate constant. Additionally, ExpFlow’s machine-readable experimental workflows enable the easy translation of human-developed laboratory procedures to robotic experimentation. We deploy ExpFlow procedures with a robotic system to perform cyclic voltammetry measurements, reproducing several literature-reported electrochemical results. Ultimately, these tools enable automated cyclic voltammetry experiments and measurements that will facilitate high-throughput experimentation, reproducibility, and eventually data-driven discovery in electrochemistry. Computer science ; Automation ; Reproducibility ; Workflow ; Software ; Process (computing) ; Interface (matter) ; Engineering ; Chemistry ; Mechanical engineering Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jaekyun Hwang | Yuma Iwasaki
Source: Science and Technology of Advanced Materials Methods (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Improving efficiency of autonomous material search via transfer learning from nontarget properties Recently, autonomous material search methods combining machine learning and experiments/simulations have become indispensable for exploring the extremely vast material exploration space. However, conventional autonomous material search methods focus solely on target material properties and their descriptors, leaving room for improvement in search efficiency. More efficient autonomous material search can be realized by utilizing information on nontarget properties that are dormant in databases. Here, we propose a novel method for autonomous material search using transfer learning and an ensemble neural network. This method can perform an autonomous material search to optimize the target properties while transferring information on nontarget properties. To demonstrate the usefulness of this method, we applied it to search for ternary magnetic alloys with a high Curie temperature while transferring information on magnetic moment and spin polarization. Results indicate that the proposed method improves the efficiency of autonomous material search, particularly in the early stages of the search process. Computer science ; Focus (optics) ; Artificial intelligence ; Process (computing) ; Search engine ; Machine learning ; Information retrieval ; Physics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Neural Networks and Applications