jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kareem S. Aggour | Virendra Kumar | Vipul Gupta | Alfredo Gabaldon | Paul Cuddihy | Varish Mulwad
Source: Integrating materials and manufacturing innovation (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data Abstract The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface. Semantics (computer science) ; Data science ; Computer science ; World Wide Web ; Programming language Machine Learning in Materials Science ; Research Data Management Practices ; Advanced X-ray and CT Imaging
Authors: Charlotte H. Müller | Miguel Steiner | Jan P. Unsleber | Thomas Weymuth | Moritz Bensberg | Katja‐Sophia Csizi | Maximilian Mörchen | Paul L. Türtscher | Markus Reiher
Source: The Journal of Physical Chemistry A (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

<scp>Heron:</scp>Visualizing and Controlling Chemical Reaction Explorations and Networks Automated and high-throughput quantum chemical investigations into chemical processes have become feasible in great detail and broad scope. This results in an increase in complexity of the tasks and in the amount of generated data. An efficient and intuitive way for an operator to interact with these data and to steer virtual experiments is required. Here, we introduce Heron, a graphical user interface that allows for advanced human-machine interactions with quantum chemical exploration campaigns into molecular structure and reactivity. Heron offers access to interactive and automated explorations of chemical reactions with standard electronic structure modules, haptic force feedback, microkinetic modeling, and refinement of data by automated correlated calculations including black-box complete active space calculations. It is tailored to the exploration and analysis of vast chemical reaction networks. We show how interoperable modules enable advanced workflows and pave the way for routine low-entrance-barrier access to advanced modeling techniques. Heron ; Computer science ; Chemistry ; Biology ; Ecology Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Origins and Evolution of Life
Authors: V. RA. Rathina Gesav | Angelina Geetha | S. Vasugi | S. Balachandran | I G K Ilangovar
Source: Cureus (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Emerging Two-Dimensional Ti3C2-BiOCl Nanoparticles for Excellent Antimicrobial and Antioxidant Properties Introduction MXenes (Ti<sub>3</sub>C<sub>2</sub>) represent a group of two-dimensional inorganic compounds, produced through a top-down exfoliation method. They comprise ultra-thin layers of transition metal carbides, or carbonitrides, and exhibit hydrophilic properties on their surfaces. Utilizing Ti<sub>3</sub>C<sub>2</sub> BiOCl nanoparticles for their antimicrobial and antioxidant attributes involves enhancing synthesis, processing, and characterization techniques. Materials and method To prepare Ti<sub>3</sub>C<sub>2</sub> MXene, dissolve 1.6 g of LiF in 20 ml of 9M HCl. Slowly add 1 g of Ti<sub>3</sub>AlC<sub>2</sub> (titanium aluminum carbide) powder to the solution while stirring. Etch at 35°C for 24 h to remove Al layers from Ti<sub>3</sub>AlC<sub>2</sub>, leaving Ti<sub>3</sub>C<sub>2</sub> layers. Wash the mixture with distilled water and ethanol until the pH is around 6. Collect the washed sediment by centrifugation and sonicate it in distilled water for 1 h. Centrifuge to remove unexfoliated particles. For BiOCl synthesis, dissolve 2 mmol of Bi(NO<sub>3</sub>)<sub>3</sub>·5H<sub>2</sub>O (bismuth nitrate pentahydrate) in 10 ml of 2M HCl (hydrochloric acid) with 0.5 g of PVP (polyvinylpyrrolidone). Transfer the solution to a Teflon-lined autoclave, fill it with distilled water up to 80%, and heat at 160°C for 24 h. Collect the precipitate by centrifugation, wash, and dry at 60°C for 12 h. Disperse BiOCl nanoparticles in distilled water, sonicate for 30 min, add Ti<sub>3</sub>C<sub>2</sub> MXene dispersion, stir for 2 h, collect, wash, dry, and calcine at 400°C for 2 h. Result The Scanning Electron Microscope (SEM) utilizes electrons, rather than light, to generate highly magnified images. Energy Dispersive X-ray Spectroscopy (EDS) complements SEM by analyzing the X-ray spectrum emitted when a solid sample is bombarded with electrons, enabling localized chemical analysis. In SEM imaging, incorporating an X-ray spectrometer allows for both element mapping and point analysis. The SEM image of the prepared samples reveals accordion-like multilayer structures in BiOCl, characterized by thin sheet-like structures with numerous pores. EDS, relying on X-ray emissions from electron bombardment, facilitates detailed chemical analysis at specific locations within the sample. Conclusion Our research has shed light on the synthesis and characterization processes of two-dimensional Ti<sub>3</sub>C<sub>2</sub> BiOCl nanoparticles, revealing their remarkable antimicrobial and antioxidant properties. Medicine ; Antimicrobial ; Nanoparticle ; Traditional medicine ; Nanotechnology ; Microbiology MXene and MAX Phase Materials ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Qizhi Pei | Rui Yan | Kaiyuan Gao | Zhu Jin-hua | Lijun Wu
Source: arXiv (Cornell University) (preprint, 2024-06-09)
Field: Chemistry — Organic Chemistry

3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling The integration of molecular and natural language representations has emerged as a focal point in molecular science, with recent advancements in Language Models (LMs) demonstrating significant potential for comprehensive modeling of both domains. However, existing approaches face notable limitations, particularly in their neglect of three-dimensional (3D) information, which is crucial for understanding molecular structures and functions. While some efforts have been made to incorporate 3D molecular information into LMs using external structure encoding modules, significant difficulties remain, such as insufficient interaction across modalities in pre-training and challenges in modality alignment. To address the limitations, we propose \textbf{3D-MolT5}, a unified framework designed to model molecule in both sequence and 3D structure spaces. The key innovation of our approach lies in mapping fine-grained 3D substructure representations into a specialized 3D token vocabulary. This methodology facilitates the seamless integration of sequence and structure representations in a tokenized format, enabling 3D-MolT5 to encode molecular sequences, molecular structures, and text sequences within a unified architecture. Leveraging this tokenized input strategy, we build a foundation model that unifies the sequence and structure data formats. We then conduct joint pre-training with multi-task objectives to enhance the model's comprehension of these diverse modalities within a shared representation space. Thus, our approach significantly improves cross-modal interaction and alignment, addressing key challenges in previous work. Further instruction tuning demonstrated that our 3D-MolT5 has strong generalization ability and surpasses existing methods with superior performance in multiple downstream tasks. Our code is available at https://github.com/QizhiPei/3D-MolT5. Lexical analysis ; Computer science ; Molecule ; Natural language processing ; Chemistry Synthetic Organic Chemistry Methods ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Norberto A. Guzman | Andrea Guzman
Source: International Journal of Translational Medicine (article, 2024-05-31)
Field: Chemistry — Spectroscopy

Human Sputum Proteomics: Advancing Non-Invasive Diagnosis of Respiratory Diseases with Enhanced Biomarker Analysis Methods Many ailments can be diagnosed while they are asymptomatic, meaning that the patient has no signs or symptoms of a progressing disease. If caught in their initial stage of formation, these disorders can be effectively treated, leading to successful outcomes; curative therapies can halt illnesses from advancing, thus improving the quality of life and long-term survival of the patient. Still, cutting-edge upgrades in precision technologies are necessary for early, reliable, affordable, and rapid disease detection, but also vital for the well-being of people and the future of global public health. The emerging role and utility of non-invasive and repeatable diagnostic test approaches for the detection of health conditions have been exemplified by liquid biopsies based on genomic biomarkers. As such, biological fluids permit any measurable molecular indicator or signature (e.g., proteins) to provide valuable information on an individual’s wellness and/or disease. Among the bodily secretions used for non-invasive diagnostics is sputum, a complex viscous gel-like biopolymeric network that has gained growing recognition as a rich source of biomarkers of airway infections and pulmonary diseases, and serves as a determinant to reveal other illnesses. As per the World Health Organization, the burden of respiratory conditions is exacerbated by factors ranging from considerable subjection to air pollution and occupational contaminants to tobacco smoking and second-hand smoke, in addition to poor socio-economic status. Due to the likely increase in these determinants, respiratory tract ailments are on the rise, affecting the health of many individuals, in addition to putting stress on healthcare facilities and services worldwide. The aim of this study was to perform a narrative review of sputum constituents with an emphasis on proteins and glycoproteins assessed as possible biomarkers of lung and other organ diseases. A search was conducted using mucus, sputum proteomics, sputum biomarkers, and point-of-care testing as keywords employing Google, PubMed (MEDLINE), and Web of Science, selecting the most referenced and related papers of the last decade. We, therefore, highlight the need to use expectorated or induced sputum specimens as a routine sample source for testing valuable protein biomarkers to diagnose these chronic disorders, predict inflammation and disease progression, as well as monitor the effectiveness of treatments. Further, we discuss the urgent need for fast and reliable point-of-care methods to detect and quantify crucial protein biomarkers in sputum specimens, and the limitations faced when dealing with their complex matrices. Sputum ; Biomarker ; Proteomics ; Medicine ; Computational biology ; Pathology ; Biology ; Tuberculosis Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Zhilong Song | Shuaihua Lu | Ming‐Gang Ju | Qionghua Zhou | Jinlan Wang
Source: arXiv (Cornell University) (preprint, 2024-07-09)
Field: Materials Science — Materials Chemistry

Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures? Accessing the synthesizability of crystal structures is pivotal for advancing the practical application of theoretical material structures designed by machine learning or high-throughput screening. However, a significant gap exists between the actual synthesizability and thermodynamic or kinetic stability, which is commonly used for screening theoretical structures for experiments. To address this, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which includes three LLMs for predicting the synthesizability, synthesis methods, and precursors. We create a comprehensive synthesizability dataset including 140,120 crystal structures and develop an efficient text representation method for crystal structures to fine-tune the LLMs. The Synthesizability LLM achieves a remarkable 98.6% accuracy, significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability by 106.1% and 44.5%, respectively. The Methods LLM achieves a classification accuracy of 91.02%, and the Precursors LLM has an 80.2% success rate in predicting synthesis precursors. Furthermore, we develop a user-friendly graphical interface that enables automatic predictions of synthesizability and precursors from uploaded crystal structure files. Through these contributions, CSLLM bridges the gap between theoretical material design and experimental synthesis, paving the way for the rapid discovery of novel and synthesizable functional materials. Computer science ; Crystal (programming language) ; Chemistry ; Programming language Machine Learning in Materials Science
Authors: Yanpeng Ye | Jie Ren | Shaozhou Wang | Yuwei Wan | Imran Razzak | Hoex, Bram | Wenjie Zhang | Xie, Tong | Zhang, Wenjie
Source: arXiv (Cornell University) (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162,605 nodes and 731,772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated science knowledge graphs. Multidisciplinary approach ; Knowledge graph ; Computer science ; Graph ; Natural language processing ; Theoretical computer science ; Artificial intelligence ; Sociology ; Social science Machine Learning in Materials Science
Authors: M. S. Awan | Muhammad Tuoqeer Anwar | Hasan Izhar Khan | Muhammad Rehman Asghar | Muhammad Rafi Raza | Naveed Husnain | Muzamil Hussain | Tahir Rasheed
Source: Characterization and Application of Nanomaterials (article, 2024-11-29)
Field: Chemistry — Organic Chemistry

Nanoparticles' classification, synthesis, characterization and applications—A review This review provides an overview of the importance of nanoparticles in various fields of science, their classification, synthesis, reinforcements, and applications in numerous areas of interest. Normally nanoparticles are particles having a size of 100 nm or less that would be included in the larger category of nanoparticles. Generally, these materials are either 0-D, 1-D, 2-D, or 3-D. They are classified into groups based on their composition like being organic and inorganic, shapes, and sizes. These nanomaterials are synthesized with the help of top-down bottom and bottom-up methods. In case of plant-based synthesis i.e., the synthesis using plant extracts is non-toxic, making plants the best choice for producing nanoparticles. Several physicochemical characterization techniques are available such as ultraviolet spectrophotometry, Fourier transform infrared spectroscopy, the atomic force microscopy, the scanning electron microscopy, the vibrating specimen magnetometer, the superconducting complex optical device, the energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy to investigate the nanomaterials. In the meanwhile, there are some challenges associated with the use of nanoparticles, which need to be addressed for the sustainable environment. Characterization (materials science) ; Nanotechnology ; Computer science ; Materials science Nanomaterials for catalytic reactions ; Pigment Synthesis and Properties ; Inorganic and Organometallic Chemistry
Authors: Fangze Liu | Zhantao Chen | Tianyi Liu | Ruyi Song | Yu Lin | Joshua J. Turner | Chunjing Jia
Source: iScience (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Self-supervised generative models for crystal structures Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science. Discriminator ; Generative grammar ; Computer science ; Scope (computer science) ; Artificial intelligence ; Reliability (semiconductor) ; Generative model ; Machine learning ; Artificial neural network ; Graph ; Generative adversarial network ; Deep learning ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Carl Edwards | Qingyun Wang | Lawrence Zhao | Heng Ji
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

L+M-24: Building a Dataset for Language+Molecules @ ACL 2024 Language-molecule models have emerged as an exciting direction for molecular discovery and understanding.However, training these models is challenging due to the scarcity of molecule-language pair datasets.At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates.In this document, we detail the L+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024.In particular, L+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction. 1 Computer science Machine Learning in Materials Science
Authors: Yisha Zheng | Wenjie Guo | Juan Xu | Yanhui Zhang
Source: Reviews in Inorganic Chemistry (review, 2024-09-28)
Field: Materials Science — Materials Chemistry

Semiconductor-attapulgite composites in environmental and energy applications: a review Abstract Attapulgite, with abundant reserves, low price, a special structure, excellent adsorption, thermal stability, and catalytic performance, is often used as a catalyst or catalyst support, offering broad development prospects. In order to understand and fully utilize attapulgite resources, this paper introduces the common preparation methods of attapulgite and explores the effects of different modification methods ( e.g. thermal modification, acid modification, etc.) on attapulgite performance. The article focuses on the current status of semiconductor-attapulgite composites in the fields of wastewater treatment, air pollution, and energy, and briefly describes examples of their applications in other fields. Finally, the development prospects of the composites in the field of environment and energy are summarized, and the challenges are discussed. Chemistry ; Semiconductor ; Energy (signal processing) ; Nanotechnology ; Composite material ; Engineering physics ; Optoelectronics ; Materials science ; Physics Advanced Materials and Semiconductor Technologies ; Advanced Energy Technologies and Civil Engineering Innovations ; Polymer Nanocomposite Synthesis and Irradiation