jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hanxun Jin | Enrui Zhang | Boyu Zhang | Sridhar Krishnaswamy | George Em Karniadakis | Horacio D. Espinosa
Source: arXiv (Cornell University) (preprint, 2023-11-23)
Field: Materials Science — Polymers and Plastics

Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro-scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, we introduce a new end-to-end scientific ML framework, leveraging deep neural operators (DeepONet), to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high-quality in situ experimental data. The approach facilitates the inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%. Our work underscores that by employing neural operators with advanced micro-mechanics experimental techniques, the design of complex micro-architected materials with desired properties becomes feasible, even in scenarios constrained by data scarcity. Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights. Computer science ; Metamaterial ; Inverse ; Nonlinear system ; Inverse problem ; Artificial intelligence ; Mechanical engineering ; Materials science ; Engineering ; Physics ; Mathematics ; Geometry Polymer composites and self-healing ; Acoustic Wave Phenomena Research ; Polydiacetylene-based materials and applications
Authors: Andres M Bran | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Transformers and Large Language Models for Chemistry and Drug Discovery Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machine learning, with breakthroughs in chemistry and biology. In this chapter, we explore how analogies between chemical and natural language have inspired the use of Transformers to tackle important bottlenecks in the drug discovery process, such as retrosynthetic planning and chemical space exploration. The revolution started with models able to perform particular tasks with a single type of data, like linearised molecular graphs, which then evolved to include other types of data, like spectra from analytical instruments, synthesis actions, and human language. A new trend leverages recent developments in large language models, giving rise to a wave of models capable of solving generic tasks in chemistry, all facilitated by the flexibility of natural language. As we continue to explore and harness these capabilities, we can look forward to a future where machine learning plays an even more integral role in accelerating scientific discovery. Computer science ; Transformer ; Natural language ; Drug discovery ; Retrosynthetic analysis ; Artificial intelligence ; Architecture ; Flexibility (engineering) ; Process (computing) ; Data science ; Cognitive science ; Engineering ; Chemistry ; Programming language ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ana Vico Cobos | Isabel María Pérez de Vargas Sansalvador | Miguel M. Erenas Rodríguez | Miguel Ángel Carvajal Rodríguez | Francisco Santoyo‐González | D. Guirado | Antonio M. Lallena Rojo | A. Palma | L.F. Capitán‐Vallvey
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Research and characterisation of novel flexible materials for radiochromic film design Materials science ; Computer science ; Engineering drawing ; Engineering Silicone and Siloxane Chemistry ; Engineering Applied Research ; Photopolymerization techniques and applications
Authors: Miroslav Vlček
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Radiation sensitivity of chalcogenide glasses thin films prepared by spin coating Chalcogenide ; Materials science ; Spin coating ; Spin glass ; Sensitivity (control systems) ; Coating ; Chalcogenide glass ; Thin film ; Radiation sensitivity ; Optoelectronics ; Composite material ; Nanotechnology ; Irradiation ; Condensed matter physics ; Electronic engineering ; Physics Phase-change materials and chalcogenides ; Consumer Perception and Purchasing Behavior
Authors: Marta Kuwik | Karolina Kowalska | Joanna Pisarska | Wojciech Pisarsk
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Ceramics and Composites

Near-infrared luminescence properties of germanate based glasses as a function of glass modifier TiO2 Germanate ; Luminescence ; Materials science ; Infrared ; Optoelectronics ; Optics ; Doping ; Physics Glass properties and applications ; Optical and Acousto-Optic Technologies ; Material Science and Thermodynamics
Authors: S. V. Likhomanova | Larisa Olegovna Fedorova | Natalyi Vladimirovna Kamanina
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Sensitized polyvinyl alcohol structures under the mechanical and vacuum UV treatment Polyvinyl alcohol ; Materials science ; Computer science ; Waste management ; Environmental science ; Composite material ; Engineering Photopolymerization techniques and applications
Authors: Nikolina Nikolić | Jelena Spasojević | Una Stamenović | Ivana Vukoje | Julijana Tadić | Vesna Vodnik | Zorica Kačarević‐Popović | Аleksandra Radosavljević
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Gamma irradiation-induced synthesis and characterization of bi-layered Au-(PNiPAAm/PVA) hydrogel nanocomposites Prenatal exposure Nanocomposite ; Characterization (materials science) ; Materials science ; Irradiation ; Chemical engineering ; Nanoparticle ; Gamma irradiation ; Polymer chemistry ; Composite material ; Nanotechnology Polymer Nanocomposite Synthesis and Irradiation ; Quantum Dots Synthesis And Properties
Authors: Marc Benjamin Hahn
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

In situ monitoring of the influence of water on DNA radiation damage by near-ambient pressure X-ray photoelectron spectroscopy X-ray photoelectron spectroscopy ; In situ ; Ambient pressure ; Materials science ; Radiation ; Spectroscopy ; Radiation damage ; X-ray ; Radiation pressure ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Environmental chemistry ; Nuclear magnetic resonance ; Physics ; Meteorology Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications
Authors: Mirjana Ristić | Suzana Samaržija‐Jovanović | Vojislav Jovanović | Marija Kostić | Tijana Jovanović | Gordana Marković | Marija Kojić | Milena Marinović‐Cincović
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Functional materials based on renewable raw materials: hydrochar and chitosan as formaldehyde scavengers in urea-formaldehide composites Prenatal exposure Chitosan ; Raw material ; Composite material ; Materials science ; Formaldehyde ; Urea-formaldehyde ; Chemistry ; Organic chemistry ; Adhesive Chemical Synthesis and Reactions ; Carbon dioxide utilization in catalysis ; Synthesis and properties of polymers
Authors: Tong Xie | Yuwei Wan | Haoran Wang | Ina Østrøm | Shaozhou Wang | Mingrui He | Rong Deng | Xinyuan Wu | Clara Grazian | Chunyu Kit | Bram Hoex
Source: Journal of Chemical Information and Modeling (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials. Computer science ; Sentiment analysis ; Discoverability ; Data science ; Deep learning ; Convolutional neural network ; Categorization ; Field (mathematics) ; Artificial intelligence ; Information retrieval ; World Wide Web Machine Learning in Materials Science ; Sentiment Analysis and Opinion Mining ; CO2 Reduction Techniques and Catalysts
Authors: Shuaiwen Leon Song | Bonnie Kruft | Minjia Zhang | Conglong Li | Shiyang Chen | Chengming Zhang | Masahiro Tanaka | Xiaoxia Wu | Jeff Rasley | Ammar Ahmad Awan | Connor Holmes | Martin Cai | Adam Ghanem | Zhongzhu Zhou | Yuxiong He | Pete Luferenko | Divya Kumar | Jonathan A. Weyn | Ruixiong Zhang | Sylwester Klocek | Volodymyr Vragov | Mohammed AlQuraishi | Gustaf Ahdritz | Christina Floristean | C. Negri | Rao Kotamarthi | Venkatram Vishwanath | Arvind Ramanathan | Xiao-Yong Jin | Kyle Hippe | Troy Arcomano | Romit Maulik | Maxim Zvyagin | Alexander Brace | Bin Zhang | Cindy Orozco Bohorquez | Austin Clyde | Bharat Kale | Danilo Perez-Rivera | Heng Ma | Carla M. Mann | Michael Irvin | J. Gregory Pauloski | Logan Ward | Valerie Hayot | Murali Emani | Zhen Xie | Diangen Lin | Maulik Shukla | Ian Foster | James J. Davis | Michael E. Papka | Thomas Brettin | Prasanna Balaprakash | Gina Tourassi | John Gounley | Heidi A. Hanson | Thomas E. Potok | Massimiliano Lupo Pasini | Kate Evans | Dan Lü | Dalton Lunga | Junqi Yin | Sajal Dash | Feiyi Wang | Mallikarjun Shankar | Isaac Lyngaas | Xiao Wang | Guojing Cong | Pei Zhang | Ming Fan | Siyan Liu | Adolfy Hoisie | Shinjae Yoo | Yihui Ren | W. M. Tang | Kyle Gerard Felker | Alexey Svyatkovskiy | Hang Liu | Ashwin M. Aji | Angela Dalton | Michael Schulte | Karl W. Schulz | Yuntian Deng | Weili Nie | Josh Romero | Christian Dallago | Arash Vahdat | Chaowei Xiao | Thomas Gibbs | Anima Anandkumar | Rick Stevens
Source: arXiv (Cornell University) (preprint, 2023-10-06)
Field: Materials Science — Materials Chemistry

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research. Data science ; Computer science ; Domain (mathematical analysis) ; Emerging technologies ; Scale (ratio) ; Artificial intelligence ; Geography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Scientific Computing and Data Management
Authors: Harvey D. Lee | Connor J. Grady | Katie Krell | Cooper Strebeck | Nathan Good | N. Cecilia Martínez-Gómez | Assaf A. Gilad
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-01-06)
Field: Materials Science — Materials Chemistry

A Novel Protein for the Bioremediation of Gadolinium Waste Several hundreds of tons of gadolinium-based contrast agents (GBCAs) are being dumped into the environment every year. Although macrocyclic GBCAs exhibit superior stability compared to their linear counterparts, we have found that the structural integrity of chelates are susceptible to ultraviolet light, regardless of configuration. In this study, we present a synthetic protein termed GLamouR that binds and reports gadolinium in an intensiometric manner. We then explore the extraction of gadolinium from GBCA-spiked artificial urine samples and investigate if the low picomolar concentrations reported in gadolinium-contaminated water sources pose a barrier for bioremediation. Based on promising results, we anticipate GLamouR can be used for detecting and mining REEs beyond gadolinium as well and hope to expand the biological toolbox for such applications. Gadolinium ; Bioremediation ; Extraction (chemistry) ; Chelation ; Chemistry ; Contamination ; Chromatography ; Biology ; Organic chemistry ; Ecology Lanthanide and Transition Metal Complexes ; Radioactive element chemistry and processing ; Geochemistry and Elemental Analysis
Authors: G. Suresh | Baskaran Iruson | B. Sathyaseelan | Senthilnathan Krishnmoorthy | E. Manikandan
Source: Soft Nanoscience Letters (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Effect on Optical and Antibacterial Activity of SnO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CuO Blended SnO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Nanoparticles Nanocrystalline SnO2 and CuO doped with SnO2 were prepared by the co-precipitation method and characterized for different physiochemical properties and microbiological activity. The composition and morphological formation were characterized by XRD, HRTEM, Raman, FTIR, and UV-vis spectroscopy. The Powder X-ray analysis reveals that Sn4+ ions have substituted the Cu2+ ions without changing the monoclinic structure of SnO2 but the average particle size of the SnO2 and CuO doped SnO2 samples from 11 and 5 nm respectively. However, it exhibits an inhibiting strong bacterial growth against tested bacterial strains. Nanocrystalline material ; Monoclinic crystal system ; High-resolution transmission electron microscopy ; Raman spectroscopy ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Ion ; Doping ; Precipitation ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Chemical engineering ; Crystallography ; Nanotechnology ; Transmission electron microscopy ; Crystal structure ; Chromatography ; Physics ; Organic chemistry Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors ; Chalcogenide Semiconductor Thin Films
Authors: Joseph R. H. Manning | Lev Sarkisov
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the synthesis patterns of nanomaterials: a text mining and meta-analysis approach with ZIF-8 as a case study Schematic of data pipeline developed in this study, using text mining to extract structured data about published ZIF-8 synthesis protocols, and thereby build information models about the synthesis process. Meta-analysis ; Nanomaterials ; Computer science ; Data science ; Nanotechnology ; Materials science ; Medicine ; Internal medicine Machine Learning in Materials Science
Authors: Babatunde Victor Omidiji | Olumide Moses Ogundipe | H. A. Owolabi
Source: Archives of Foundry Engineering (article, 2023-07-17)
Field: Materials Science — Ceramics and Composites

Characterization of Ijero-Ekiti Quartz as Refractory Raw Material for Industrial Furnace This study investigated the suitability of Ijero-Ekiti quartz as a refractory raw material for industrial furnace applications. In order to ascertain its prospective applications, the thermal behaviour, mineralogical composition and chemical composition were determined. Ijero-Ekiti quartz was characterized using Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Thermogravimetric and Differential Thermal analysis (TGA and DTA). Its thermal conductivity with specific heat coefficient was determined. The outcome revealed that the quartz sample has a high purity of 94.3% SiO 2, making it suitable as a refractory material. The XRD analysis revealed the presence of alpha-quartz as the dominant crystal phase, which is desirable for refractory applications. The FTIR analysis indicated the absence of hydroxyl (-OH) groups. This indicates a low risk of failure and damage such as spalling, cracking and other forms of damage when produced into bricks. The TGA and DTA displayed significant mass losses and large endothermic bands, which were connected to the dehydroxylation of the quartz rock samples. Based on the demonstrated qualities, the quartz rock sample could be subjected to thermal processing. This study therefore established that Ijero-Ekiti quartz is a suitable raw material for refractory applications due to its high purity, alpha-quartz dominant crystal phase, absence of hydroxyl groups, and uniform morphology. Materials science ; Metallurgy ; Quartz ; Raw material ; Refractory (planetary science) ; Characterization (materials science) Advanced ceramic materials synthesis
Authors: Yang Liu | Chenxiao Ye | Hengxin Zhao | Kexin Lin | Xinrui Cao | Yuejie Ai
Source: Crystals (article, 2023-05-14)
Field: Materials Science — Materials Chemistry

Si-Doped Nitrogenated Holey Graphene (C2N) as a Promising Gas Sensor for O-Containing Volatile Organic Compounds (VOCs) and Ammonia Two-dimensional (2D) crystalline materials have been regarded as promising sensor materials due to their large specific surface area, high sensitivity, and low cost. In the present work, based on the density functional theory (DFT) method, the sensor performance of novel silicon (Si)-doped nitrogenated holey graphene (SiC2N) toward five typical VOCs (HCHO, CH3OH, C3H6O, C6H6, and C2HCl3) and ammonia were systematically investigated. The results demonstrated that Si doping could effectively decrease the band gap of C2N and simultaneously provide active sites for gas adsorption. Through comprehensive analyses of adsorption energies and electronic properties, the SiC2N was found to exhibit high selectivity for O-containing VOCs (HCHO, CH3OH, and C3H6O) and NH3 via a covalent bond. Moreover, after the HCHO, CH3OH, C3H6O, and NH3 adsorption, the band gap of SiC2N greatly decreases from 1.07 eV to 0.29, 0.13, 0.25, and 0.12 eV, respectively, which indicated the enhancement the conductivity and enabled the SiC2N to be a highly sensitive resistive-type sensor. In addition, the SiC2N possesses a short recovery time. For instance, the recovery time of HCHO desorbed from SiC2N is 29.2 s at room temperature. Our work anticipates a wide range of potential applications of Si-doped C2N for the detection of toxic VOCs and ammonia, and supplies a valuable reference for the development of C2N-based gas sensors. Adsorption ; Ammonia ; Doping ; Graphene ; Materials science ; Band gap ; Selectivity ; Volatile organic compound ; Chemical engineering ; Analytical Chemistry (journal) ; Inorganic chemistry ; Nanotechnology ; Chemistry ; Optoelectronics ; Organic chemistry ; Catalysis Graphene research and applications ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Hanaa M. Ahmed | Nagat M. K. Abdel‐Gawad | Waleed A. Afifi | Diaa‐Eldin A. Mansour | Matti Lehtonen | Mohamed M. F. Darwish
Source: Materials (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

A Novel Polyester Varnish Nanocomposites for Electrical Machines with Improved Thermal and Dielectric Properties Using Functionalized TiO2 Nanoparticles Recently, there has been a growing interest in polymer insulating materials that incorporate nanoscale inorganic additives, as they have shown significantly improved dielectric, thermal, and mechanical properties, making them highly suitable for application in high-voltage insulating materials for electrical machines. This study aims to improve the dielectric and thermal properties of a commercial polyester varnish by incorporating different concentrations of titanium dioxide nanoparticles (TiO<sub>2</sub>) with proper surface functionalization. Permafil 9637 dipping varnish is the varnish used for this investigation, and vinyl silane is the coupling agent used in the surface functionalization of TiO<sub>2</sub> nanoparticles. First, nanoparticles are characterized through Fourier transform infrared spectroscopy to validate the success of their surface functionalization. Then, varnish nanocomposites are characterized through field emission scanning electron microscopy to validate the dispersion and morphology of nanoparticles within the varnish matrix. Following characterization, varnish nanocomposites are evaluated for thermal and dielectric properties. Regarding thermal properties, the thermal conductivity of the prepared nanocomposites is assessed. Regarding dielectric properties, both permittivity and dielectric losses are evaluated over a wide frequency range, starting from 20 Hz up to 2 MHz. Moreover, the AC breakdown voltage is measured for varnish nanocomposites, and the obtained data are incorporated into a finite element method to obtain the dielectric breakdown strength. Finally, the physical mechanisms behind the obtained results are discussed, considering the role of nanoparticle loading and surface functionalization. Materials science ; Surface modification ; Nanocomposite ; Varnish ; Composite material ; Dielectric ; Nanoparticle ; Fourier transform infrared spectroscopy ; Polymer nanocomposite ; Chemical engineering ; Nanotechnology ; Coating ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials