jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tingbiao Guo | Zhi Zhang | Zijian Lin | Jiahan Tian | Yi Jin | Julian Evans | Yinghe Xu | Sailing He
Source: Nature Nanotechnology (article, 2024-08-12)
Field: Materials Science — Polymers and Plastics

Durable and programmable ultrafast nanophotonic matrix of spectral pixels Locally addressable nanophotonic devices are essential for modern applications such as light detection, optical imaging, beam steering and displays. Despite recent advances, a versatile solution with a high-speed tuning rate, long-life durability and programmability across multiple pixels remains elusive. Here we introduce a programmable nanophotonic matrix consisting of vanadium dioxide (VO<sub>2</sub>) cavities on pixelated microheaters that meets all these requirements. The indirect Joule heating of these VO<sub>2</sub> cavities can result in pronounced spectral modulation with colour changes and ensures exceptional endurance even after a million switching cycles. Precise control over the thermal dissipation power through a SiO<sub>2</sub> layer of an optimized thickness on Si facilitates an ultrafast modulation rate exceeding 70 kHz. We demonstrated a video-rate nanophotonic colour display by electrically addressing a matrix of 12 × 12 pixels. Furthermore, inspired by the unique pixel-level programmability with multiple intermediate states of the spectral pixels, a spatiotemporal modulation concept is introduced for spectrum detection. Nanophotonics ; Pixel ; Ultrashort pulse ; Optoelectronics ; Optical switch ; Modulation (music) ; Optics ; Materials science ; Multispectral image ; Reconfigurability ; Computer science ; Horizontal scan rate ; Physics ; Telecommunications ; Electrode ; Laser ; Artificial intelligence Transition Metal Oxide Nanomaterials ; Optical Wireless Communication Technologies ; Photonic Crystals and Applications
Authors: Matthew L. Evans | J. Bergsma | Andrius Merkys | Casper Welzel Andersen | Oskar B. Andersson | Daniel Beltrán | Evgeny Blokhin | Tara M. Boland | Rubén Castañeda Balderas | Kamal Choudhary | Alberto Díaz Díaz | R. García | Hagen Eckert | Kristjan Eimre | María E. Fuentes | Adam M. Krajewski | Jens Jørgen Mortensen | José Manuel Nápoles Duarte | Jacob Pietryga | Ji Qi | Felipe de Jesús Trejo Carrillo | Antanas Vaitkus | Ju‐Song Yu | Adam C. Zettel | Pedro Baptista de Castro | Johan M. Carlsson | Tiago F. T. Cerqueira | Simon Divilov | Hamidreza Hajiyani | Felix Hanke | Kevin Jose | Corey Oses | Janosh Riebesell | Jonathan Schmidt | Donny Winston | Christen Xie | Xiaoyu Yang | Sara Bonella | Silvana Botti | Stefano Curtarolo | Claudia Draxl | Luis Edmundo Fuentes Cobas | Adam Hospital | Zi‐Kui Liu | Miguel A. L. Marques | Nicola Marzari | Andrew J. Morris | Shyue Ping Ong | Modesto Orozco | Kristin A. Persson | Kristian S. Thygesen | Chris Wolverton | Markus Scheidgen | Cormac Toher | G. J. Conduit | Giovanni Pizzi | S. Gražulis | Gian‐Marco Rignanese | Rickard Armiento
Source: Digital Discovery (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. Data exchange ; Computer science ; Data science ; World Wide Web Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Huaping Hu | Yuqing Shan | Qiming Zhao | Jinglun Wang | Lingjun Wu | Wanqiang Liu
Source: Journal of Energy Chemistry (article, 2024-07-06)
Field: Materials Science — Materials Chemistry

The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning Leverage (statistics) ; Categorical variable ; Boosting (machine learning) ; Random forest ; Electrolyte ; Gradient boosting ; Linear regression ; Regression ; Quantitative structure–activity relationship ; Molecular descriptor ; Artificial intelligence ; Computer science ; Machine learning ; Algorithm ; Mathematics ; Chemistry ; Statistics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yuxin Chang | Ian Benlolo | Yang Bai | Christian Reimer | Daojin Zhou | Hengrui Zhang | Hidetoshi Matsumura | Hitarth Choubisa | Xiaoyan Li | Wei Chen | Pengfei Ou | Isaac Tamblyn | Edward H. Sargent
Source: Matter (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

High-entropy alloy electrocatalysts screened using machine learning informed by quantum-inspired similarity analysis Alloy ; Artificial intelligence ; Similarity (geometry) ; Quantum ; Entropy (arrow of time) ; Computer science ; Machine learning ; Materials science ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Ruolin Du | R. C. Xin | Han Wang | Wenkai Zhu | Rui Li | Wei Liu
Source: Chemical Engineering Journal (article, 2024-05-01)
Field: Chemistry — Inorganic Chemistry

Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks Scalability ; Metal-organic framework ; Computer science ; Adaptability ; Artificial neural network ; Process engineering ; Automation ; Systems engineering ; Artificial intelligence ; Nanotechnology ; Adsorption ; Materials science ; Engineering ; Mechanical engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yang Yu | Xin Zhao | Tianyun Liu | Yue‐Fei Zhang | Yuanjun Hu | Xuefei Liu | Gang Wang | Degui Wang | Jinshun Bi | Zijiang Luo | Wentao Wang | Shenbo Yang | Wenjun Xiao
Source: International Journal of Hydrogen Energy (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The rational co-doping strategy of transition metal and non-metal atoms on g-CN for highly efficient hydrogen evolution by DFT and machine learning Doping ; Transition metal ; Hydrogen ; Metal ; Materials science ; Rational design ; Chemical physics ; Physical chemistry ; Chemistry ; Nanotechnology ; Catalysis ; Metallurgy ; Organic chemistry ; Optoelectronics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Hydrogen Storage and Materials
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Materials Today Communications (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds Quantitative structure–activity relationship ; Transformative learning ; Materials science ; Corrosion ; Pyrimidine ; Artificial neural network ; Quantum ; Biological system ; Combinatorial chemistry ; Machine learning ; Nanotechnology ; Artificial intelligence ; Computational chemistry ; Computer science ; Metallurgy ; Stereochemistry ; Chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods
Authors: Faiza Bibi | Abdul Hanan | Irfan Ali Soomro | Arshid Numan | Mohammad Khalid
Source: EcoMat (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Double transition metal <scp>MXenes</scp> for enhanced electrochemical applications: Challenges and opportunities Abstract Double transition metal (DTM) MXenes are a recently discovered class of two‐dimensional composite nanomaterials with excellent potential in energy storage applications. Since their emergence in 2015, DTM MXenes have expanded their composition boundary beyond traditional single‐metal carbide and nitride MXenes. DTM MXenes offer tunable structures and properties through variations in the constituent transition metals and positioning within the layered lattice. These MXenes can exist in two primary forms: ordered DTMs and solid solutions. The compositional versatility of DTM MXenes offers opportunities to enhance their performance in electrochemical energy storage applications. However, the quality, stability, and surface chemistry of DTM MXenes are influenced by several factors, including the etching process, etchant type, and synthesis route. Currently, limited literature is available on experimentally synthesized DTM MXenes, with most studies focusing on carbide‐based MXenes. Most of the articles have dedicated their efforts only to generalized synthesis strategies. Although extensive theoretical studies have explored the suitability of etchants, synthesis parameters, and methods for producing high‐quality MXene with selective terminal functional groups, their stability issues have not been thoroughly examined. This review addresses various types of DTM MXenes, their synthesis techniques, and the impact of these methods on their physicochemical properties and electrochemical performance. Additionally, it provides a critical analysis of the causes of instability in MXenes, particularly DTMs, from synthesis to application. The challenges associated with these materials are discussed, along with opportunities and prospects for enhancing synthesis, structural tuning, surface modification, and applications in electrochemical energy storage. image MXenes ; MAX phases ; Materials science ; Nanotechnology ; Carbide MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Joseph Jjagwe | Peter Wilberforce Olupot | Robinah Kulabako | Sandro Carrara
Source: Heliyon (review, 2024-04-01)
Field: Chemistry — Electrochemistry

Electrochemical sensors modified with iron oxide nanoparticles/nanocomposites for voltammetric detection of Pb (II) in water: A review Permissible limits of Pb<sup>2+</sup> in drinking water are being reduced from 10 μgL<sup>-1</sup> to 5 μgL<sup>-1</sup>, which calls for rapid, and highly reliable detection techniques. Electrochemical sensors have garnered attention in detection of heavy metal ions in environmental samples due to their ease of operation, low cost, and rapid detection responses. Selectivity, sensitivity and detection capabilities of these sensors, can be enhanced by modifying their working electrodes (WEs) with iron oxide nanoparticles (IONPs) and/or their composites. Therefore, this review is an in-depth analysis of the deployment of IONPs/nanocomposites in modification of electrochemical sensors for detection of Pb<sup>2+</sup> in drinking water over the past decade. From the analyzed studies (n = 23), the optimal solution pH, deposition potential, and deposition time ranged between 3 and 5.6, -0.7 to -1.4 V vs Ag/AgCl, and 100-400 s, respectively. Majority of the studies employed square wave anodic stripping voltammetry (n = 16), in 0.1 M acetate buffer solution (n = 19) for detection of Pb<sup>2+</sup>. Limits of detection obtained (2.5 x 10<sup>-9</sup> - 4.5 μg/L) were below the permissible levels which indicated good sensitivities of the modified electrodes. Despite the great performance of these modified electrodes, the primary source of IONPs has always been commercial iron-based salts in addition to the use of so many materials as modifying agents of these IONPs. This may limit reproducibility and sustainability of the WEs due to lengthy and costly preparation protocols. Steel and/or iron industrial wastes can be alternatively employed in generation of IONPs for modification of electrochemical sensors. Additionally, biomass-based activated carbons enriched with surface functional groups are also used in modification of bare IONPs, and subsequently bare electrodes. However, these two areas still need to be fully explored. Nanocomposite ; Nanoparticle ; Electrochemistry ; Iron oxide ; Iron oxide nanoparticles ; Nanotechnology ; Materials science ; Voltammetry ; Oxide ; Electrochemical gas sensor ; Chemical engineering ; Nuclear chemistry ; Metallurgy ; Chemistry ; Electrode ; Engineering ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Mohammad Nezam Uddin Chy | Md. Arafat Rahman | Jin‐Hyuk Kim | Nirjhor Barua | Wasif Abu Dujana
Source: Nanomaterials (review, 2024-03-31)
Field: Materials Science — Materials Chemistry

MXene as Promising Anode Material for High-Performance Lithium-Ion Batteries: A Comprehensive Review Broad adoption has already been started of MXene materials in various energy storage technologies, such as super-capacitors and batteries, due to the increasing versatility of the preparation methods, as well as the ongoing discovery of new members. The essential requirements for an excellent anode material for lithium-ion batteries (LIBs) are high safety, minimal volume expansion during the lithiation/de-lithiation process, high cyclic stability, and high Li<sup>+</sup> storage capability. However, most of the anode materials for LIBs, such as graphite, SnO<sub>2</sub>, Si, Al, and Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub>, have at least one issue. Hence, creating novel anode materials continues to be difficult. To date, a few MXenes have been investigated experimentally as anodes of LIBs due to their distinct active voltage windows, large power capabilities, and longer cyclic life. The objective of this review paper is to provide an overview of the synthesis and characterization characteristics of the MXenes as anode materials of LIBs, including their discharge/charge capacity, rate performance, and cycle ability. In addition, a summary of the potential outlook for developments of these materials as anodes is provided. Anode ; Lithium (medication) ; Materials science ; Nanoarchitectures for lithium-ion batteries ; Ion ; Nanotechnology ; Chemistry ; Electrode ; Psychology MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advancements in Battery Materials
Authors: Xiangcheng Gao | Jiejie Zhang | Yining Gong | Liang Yan
Source: Journal of Nanobiotechnology (review, 2024-09-16)
Field: Materials Science — Materials Chemistry

The biomedical applications of nanozymes in orthopaedics based on regulating reactive oxygen species Nanozymes, a category of nanomaterials with enzyme-like activity, have garnered growing interest in various biomedical contexts. Notably, nanozymes that are capable of regulating reactive oxygen species levels by emulating antioxidant or prooxidant enzymes within cells hold significant therapeutic potential for a range of disorders. Herein, we overview the catalytic mechanisms of four exemplary nanozymes within the orthopedic domain. Subsequently, we emphasize recent groundbreaking advancements in nanozyme applications in orthopaedics, encompassing osteoarthritis, osteoporosis, intervertebral disc degeneration, bone defects, spinal cord injury, gout, rheumatoid arthritis, osteosarcoma and bone infection. Furthermore, we discuss the emerging area's future prospects and several noteworthy challenges in biomedical application. This review not only fosters the ongoing development of nanozyme research but also fosters the emergence of more potent nanozymes for the treatment of orthopaedical diseases in the future. Reactive oxygen species ; Osteoporosis ; Spinal cord injury ; Regenerative medicine ; Osteoarthritis ; Nanotechnology ; Chemistry ; Medicine ; Neuroscience ; Bioinformatics ; Pathology ; Biology ; Spinal cord ; Biochemistry ; Materials science ; Cell Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Minyeong Yoon | Yullim Lee | S.H. Lee | Youngwook Cho | Damee Koh | Seyoung Shin | Changyu Tian | Youngho Song | Joohoon Kang | Soo‐Yeon Cho
Source: Sensors & Diagnostics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A nIR fluorescent single walled carbon nanotube sensor for broad-spectrum diagnostics In this review, we provide a comprehensive overview of the latest advancements in diagnostic sensor design using nIR fluorescent SWCNTs and suggest potential directions for technological developments for real-world applications. Carbon nanotube ; Broad spectrum ; Nanotechnology ; Fluorescence ; Materials science ; Chemistry ; Physics ; Optics Carbon Nanotubes in Composites ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Vincent Montero | Marc Montana | Manon Carré | Patrice Vanelle
Source: European Journal of Medicinal Chemistry (article, 2024-04-07)
Field: Chemistry — Organic Chemistry

Quinoxaline derivatives: Recent discoveries and development strategies towards anticancer agents Cancer is a leading cause of death and a major health problem worldwide. While many effective anticancer agents are available, most drugs currently on the market are not specific, raising issues like the common side effects of chemotherapy. However, recent research hold promises for the development of more efficient and safer anticancer drugs. Quinoxaline and its derivatives are becoming recognized as a novel class of chemotherapeutic agents with activity against different tumors. The present review compiles and discusses studies concerning the therapeutic potential of the anticancer activity of quinoxaline derivatives, covering articles published between January 2018 and January 2023. Quinoxaline ; Chemistry ; Pharmacology ; SAFER ; Medicine ; Computer science ; Computer security Synthesis and Biological Evaluation ; Bioactive Compounds and Antitumor Agents ; Cancer therapeutics and mechanisms
Authors: Oscar Méndez‐Lucio | Christos A. Nicolaou | Berton Earnshaw
Source: Nature Communications (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

MolE: a foundation model for molecular graphs using disentangled attention Models that accurately predict properties based on chemical structure are valuable tools in the chemical sciences. However, for many properties, public and private training sets are typically small, making it difficult for models to generalize well outside of the training data. Recently, this lack of generalization has been mitigated by using self-supervised pretraining on large unlabeled datasets, followed by finetuning on smaller, labeled datasets. Inspired by these advances, we report MolE, a Transformer architecture adapted for molecular graphs together with a two-step pretraining strategy. The first step of pretraining is a self-supervised approach focused on learning chemical structures trained on ~842 million molecular graphs, and the second step is a massive multi-task approach to learn biological information. We show that finetuning models that were pretrained in this way perform better than the best published results on 10 of the 22 ADMET (absorption, distribution, metabolism, excretion and toxicity) tasks included in the Therapeutic Data Commons leaderboard (c. September 2023). Foundation (evidence) ; Computer science ; Computational biology ; Biology ; History ; Archaeology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jhonatan Contreras | Andreea Winterfeld | Jürgen Popp | Thomas Bocklitz
Source: Analytical Chemistry (article, 2024-09-18)
Field: Chemistry — Analytical Chemistry

Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis Interpretability is just as important as accuracy when it comes to complex models, especially in the context of deep learning models. Explainable artificial intelligence (XAI) approaches have been developed to address this problem. The literature on XAI for spectroscopy mainly emphasizes independent feature analysis with limited application of zone analysis. Individual feature analysis methods, such as shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), have limitations due to their dependence on perturbations. These methods measure how AI models respond to sudden changes in the individual feature values. While they can help identify the most impactful features, the abrupt shifts introduced by replacing these values with zero or the expected ones may not accurately represent real-world scenarios. This can lead to mathematical and computational interpretations that are neither physically realistic nor intuitive to humans. Our proposed method does not rely on individual disturbances. Instead, it targets "spectral zones" to directly estimate the effect of group disturbances on a trained model. Consequently, factors such as sample size, hyperparameter selection, and other training-related considerations are not the primary focus of the XAI methods. To achieve this, we have developed a modified version of LIME and SHAP capable of performing group perturbations, enhancing explainability and realism while minimizing noise in the plots used for interpretability. Additionally, we employed an efficient approach to calculate spectral zones for complex spectra with indistinct spectral boundaries. Users can also define the zones themselves using their domain-specific knowledge. Chemistry ; Interpretability ; Feature (linguistics) ; Spectral analysis ; Lime ; Artificial intelligence ; Pattern recognition (psychology) ; Spectroscopy ; Geology Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Ashish Kumar Parashar | Gaurav Kant Saraogi | Pushpendra Kumar Jain | Balakdas Kurmi | Vivek Shrivastava | Vandana Arora
Source: Discover Oncology (review, 2024-11-11)
Field: Materials Science — Biomaterials

Polymer-drug conjugates: revolutionizing nanotheranostic agents for diagnosis and therapy Nanotheranostics, an amalgamation of therapeutic and diagnostic capabilities at the nanoscale, is revolutionizing personalized medicine. Polymer-drug conjugates (PDCs) stand at the forefront of this arena, offering a multifaceted approach to treat complex diseases such as cancer. This review explores the recent advancements in PDCs, highlighting their design principles, working mechanisms, and the therapeutic applications. We discuss the incorporation of imaging agents into PDCs that allow for real-time monitoring of drug delivery and treatment efficacy. With the aim of improving patient care, the review examines how PDCs enable targeted drug delivery, minimize side effects, and provide valuable diagnostic data, hence enhancing the precision of medical interventions. We also address the challenges facing the clinical translation of PDCs, such as scalability, regulatory hurdles, and cost-effectiveness, providing a comprehensive outlook on the future of nanotheranostics in patient management. Drug ; Computer science ; Nanotechnology ; Medicine ; Pharmacology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications