jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehran Ghasemlou | Navya PN | Katia Alexander | Ali Zavabeti | Peter C. Sherrell | Elena P. Ivanova | Benu Adhikari | Minoo Naebe | Suresh K. Bhargava
Source: Advanced Materials (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

Fluorescent Nanocarbons: From Synthesis and Structure to Cancer Imaging and Therapy Nanocarbons are emerging at the forefront of nanoscience, with diverse carbon nanoforms emerging over the past two decades. Early cancer diagnosis and therapy, driven by advanced chemistry techniques, play a pivotal role in mitigating mortality rates associated with cancer. Nanocarbons, with an attractive combination of well-defined architectures, biocompatibility, and nanoscale dimension, offer an incredibly versatile platform for cancer imaging and therapy. This paper aims to review the underlying principles regarding the controllable synthesis, fluorescence origins, cellular toxicity, and surface functionalization routes of several classes of nanocarbons: carbon nanodots, nanodiamonds, carbon nanoonions, and carbon nanohorns. This review also highlights recent breakthroughs regarding the green synthesis of different nanocarbons from renewable sources. It also presents a comprehensive and unified overview of the latest cancer-related applications of nanocarbons and how they can be designed to interface with biological systems and work as cancer diagnostics and therapeutic tools. The commercial status for large-scale manufacturing of nanocarbons is also presented. Finally, it proposes future research opportunities aimed at engendering modifiable and high-performance nanocarbons for emerging applications across medical industries. This work is envisioned as a cornerstone to guide interdisciplinary teams in crafting fluorescent nanocarbons with tailored attributes that can revolutionize cancer diagnostics and therapy. Materials science ; Fluorescence ; Nanotechnology ; Cancer therapy ; Cancer ; Medicine ; Optics Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Vishal Kushwaha | K. D. Mandal | Asha Gupta | Preetam Singh
Source: Dalton Transactions (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ni<sub>0.5</sub>Co<sub>0.5</sub>S nano-chains: a high-performing intercalating pseudocapacitive electrode in asymmetric supercapacitor (ASC) mode for the development of large-scale energy storage devices Grid-scale energy storage solutions are necessary for using renewable energy sources efficiently. A supercapattery (supercapacitor + battery) has recently been introduced as a new variety of hybrid devices that engage both capacitive and faradaic charge storage processes. Nano-chain architectures of Ni<sub>0.5</sub>Co<sub>0.5</sub>S electrode materials consisting of interconnected nano-spheres are rationally constructed by tailoring the surface structure. Nano-chains of the bimetallic sulfide Ni<sub>0.5</sub>Co<sub>0.5</sub>S are presented to have a superior charge storage capacity. The Ni<sub>0.5</sub>Co<sub>0.5</sub>S nano-chain electrode presents a capacitance of 2001.6 F g<sup>-1</sup> at 1 mV s<sup>-1</sup>, with a specific capacity of 267 mA h g<sup>-1</sup> (1920 F g<sup>-1</sup>) at 1 A g<sup>-1</sup> in 4 M KOH aqueous electrolyte through the galvanostatic charge-discharge (GCD) method. The reason behind the high charge storage capacity of the materials is the predominant redox-mediated diffusion-controlled pseudocapacitive mechanism coupled with surface capacitance (electrosorption), as the surface (outer) and intercalative (inner) charges stored by the Ni<sub>0.5</sub>Co<sub>0.5</sub>S electrodes are close to 46.0% and 54.0%, respectively. Additionally, a Ni<sub>0.5</sub>Co<sub>0.5</sub>S//AC two electrode full cell operating in asymmetric supercapacitor cell (ASCs) mode in 4 M KOH electrolyte exhibits an impressive energy density equivalent to 257 W h kg<sup>-1</sup> and a power density of 0.73 kW kg<sup>-1</sup> at a current rate of 1 A g<sup>-1</sup>. Supercapacitor ; Intercalation (chemistry) ; Materials science ; Electrode ; Energy storage ; Nanotechnology ; Nanoscopic scale ; Nano- ; Mode (computer interface) ; Electrochemistry ; Chemistry ; Inorganic chemistry ; Physics ; Computer science ; Composite material ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Seungin Jee | Min‐Jae Si | Jae‐Hwan Choi | Dong-Eon Kim | Changjo Kim | Dongsoo Yang | Se‐Woong Baek
Source: Advanced Optical Materials (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

P‐Type Colloidal InSb Quantum Dot Ink Enables III–V Group Bulk‐Heterojunction Shortwave Infrared (SWIR) Photodetector Abstract Infrared (IR) optoelectronics have become important owing to their various applications, such as recognition, autonomous driving, and quantum communications. In particular, detection beyond 1400‐nm wavelength in the shortwave IR (SWIR) spectrum (i.e., 1550 nm) is important for eye safety, and long‐range communication. Recently, group III–V (InAs or InSb) colloidal quantum dots (CQDs) have attracted considerable interest due to their broadband optical tunability and toxic‐elements (Pb and Hg)‐free properties. Herein, a new approach is developed to synthesize highly monodispersed InSb CQD by employing the continuous injection method, which enables facile optical bandgap tuning at a SWIR wavelength of up to 0.9 eV. Furthermore, solution ligand exchange using halides and thiolates results in effective passivation of the InSb CQD surface and renders a stable p ‐type CQD ink. Finally, bulk heterojunction (BHJ) structure is demonstrated using n ‐type InAs: p ‐type InSb CQDs, which exhibits broad absorption up to 1600 nm, and a sixfold higher responsivity compared with the pristine InSb CQD device due to the efficient charge transport in BHJ CQD solids. Photodetector ; Materials science ; Shortwave ; Quantum dot ; Heterojunction ; Optoelectronics ; Infrared ; Colloid ; Optics ; Physics ; Radiative transfer Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanowire Synthesis and Applications
Authors: Songlin Yu | Nian Ran | Jianjun Liu
Source: Artificial Intelligence Chemistry (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Large-language models: The game-changers for materials science research Large Language Models (LLMs), such as GPT-4, are precipitating a new "industrial revolution" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from LLMs can comprehend user intent and autonomously design, plan, and utilize tools to execute intricate tasks. These attributes are particularly advantageous for materials science research, an interdisciplinary field characterized by numerous complex and time-intensive activities. The integration of LLMs into materials science research holds the potential to fundamentally transform the research paradigm in this field. Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Emir Ali Karahan | Zheng Liu | Aggraj Gupta | Zijian Shao | Jonathan Zhou | Uday K. Khankhoje | Kaushik Sengupta
Source: Nature Communications (article, 2024-12-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning enabled generalized inverse design of multi-port radio-frequency and sub-terahertz passives and integrated circuits Millimeter-wave and terahertz integrated circuits and chips are expected to serve as the backbone for future wireless networks and high resolution sensing. However, design of these integrated circuits and chips can be quite complex, requiring years of human expertise, careful tailoring of hand crafted circuit topologies and co-design with parameterized and pre-selected templates of electromagnetic structures. These structures (radiative and non-radiative, single-port and multi-ports) are subsequently optimized through ad-hoc methods and parameter sweeps. Such bottom-up approaches with pre-selected regular topologies also fundamentally limit the design space. Here, we demonstrate a universal inverse design approach for arbitrary-shaped complex multi-port electromagnetic structures with designer radiative and scattering properties, co-designed with active circuits. To allow such universalization, we employ deep learning based models, and demonstrate synthesis with several examples of complex mm-Wave passive structures and end-to-end integrated mm-Wave broadband circuits. The presented inverse design methodology, that produces the designs in minutes, can be transformative in opening up a new, previously inaccessible design space. Computer science ; Electronic circuit ; Terahertz radiation ; Electronic engineering ; Broadband ; Port (circuit theory) ; Topology (electrical circuits) ; Telecommunications ; Electrical engineering ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Microwave Engineering and Waveguides ; Antenna Design and Analysis
Authors: Elton Pan | Soonhyoung Kwon | Zach Jensen | Mingrou Xie | Rafael Gómez‐Bombarelli | Manuel Moliner | Yuriy Román‐Leshkov | Elsa Olivetti
Source: ACS Central Science (article, 2024-03-06)
Field: Chemistry — Inorganic Chemistry

ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis-structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset. Rationalization (economics) ; Hydrothermal circulation ; Zeolite ; Computer science ; Hydrothermal synthesis ; Artificial intelligence ; Chemical engineering ; Engineering ; Chemistry ; Management ; Catalysis ; Economics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Gürel Şenol | Fatih Selımefendıgıl | Hakan F. Öztop
Source: International Journal of Hydrogen Energy (review, 2024-05-01)
Field: Materials Science — Materials Chemistry

A review on nanofluid, phase change material and machine learning applications for thermal management of hydrogen storage in metal hydrides Nanofluid ; Hydrogen storage ; Phase change ; Thermal ; Materials science ; Thermal management of electronic devices and systems ; Hydrogen ; Thermal energy storage ; Phase-change material ; Phase (matter) ; Metal ; Thermodynamics ; Chemical engineering ; Mechanical engineering ; Metallurgy ; Chemistry ; Engineering ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Yuchen Gao | Yu‐Hang Yuan | Suozhi Huang | Nan Yao | Legeng Yu | Yao‐Peng Chen | Qiang Zhang | Xiang Chen
Source: Angewandte Chemie International Edition (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge-based electrolyte Property prediction Integration (KPI), a knowledge-data dual-driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure-property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state-of-the-art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high-throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide-temperature-range and high-safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure-property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge. Computer science ; Property (philosophy) ; Dual (grammatical number) ; Battery (electricity) ; Electrolyte ; Range (aeronautics) ; Domain (mathematical analysis) ; Data mining ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Zachary A. Rollins | Alan C. Cheng | Essam Metwally
Source: Journal of Cheminformatics (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

MolPROP: Molecular Property prediction with multimodal language and graph fusion Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots, autonomous driving, and protein folding. Additional research aims to improve performance on downstream tasks by fusing high dimensional data representations across multiple modalities. In this work, we explore a novel fusion of a pretrained language model, ChemBERTa-2, with graph neural networks for the task of molecular property prediction. We benchmark the MolPROP suite of models on seven scaffold split MoleculeNet datasets and compare with state-of-the-art architectures. We find that (1) multimodal property prediction for small molecules can match or significantly outperform modern architectures on hydration free energy (FreeSolv), experimental water solubility (ESOL), lipophilicity (Lipo), and clinical toxicity tasks (ClinTox), (2) the MolPROP multimodal fusion is predominantly beneficial on regression tasks, (3) the ChemBERTa-2 masked language model pretraining task (MLM) outperformed multitask regression pretraining task (MTR) when fused with graph neural networks for multimodal property prediction, and (4) despite improvements from multimodal fusion on regression tasks MolPROP significantly underperforms on some classification tasks. MolPROP has been made available at https://github.com/merck/MolPROP . SCIENTIFIC CONTRIBUTION: This work explores a novel multimodal fusion of learned language and graph representations of small molecules for the supervised task of molecular property prediction. The MolPROP suite of models demonstrates that language and graph fusion can significantly outperform modern architectures on several regression prediction tasks and also provides the opportunity to explore alternative fusion strategies on classification tasks for multimodal molecular property prediction. Computer science ; Machine learning ; Artificial intelligence ; Graph ; Regression ; Property (philosophy) ; Suite ; Benchmark (surveying) ; Task (project management) ; Artificial neural network ; Natural language processing ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Daniel Iheanacho Udunwa | Okechukwu D. Onukwuli | Matthew C. Menkiti | Simeon C. Nwanonenyi | Chinyere Ezekannagha | Chukwunonso O. Aniagor
Source: Journal of Molecular Structure (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Experimental, computational, and theoretical studies on the corrosion inhibition potential of green Gongronema latifolium extract as corrosion inhibitor for aluminum alloy in HCl solutions Chemistry ; Corrosion ; Alloy ; Scanning electron microscope ; Gravimetric analysis ; Dielectric spectroscopy ; Aluminium ; Nuclear chemistry ; Electrochemistry ; Polarization (electrochemistry) ; Mass spectrometry ; Analytical Chemistry (journal) ; Chromatography ; Materials science ; Composite material ; Electrode ; Organic chemistry ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Magnesium Oxide Properties and Applications
Authors: Jyotirmoy Deb | Lakshi Saikia | Kripa Dristi Dihingia | G. Narahari Sastry
Source: Journal of Chemical Information and Modeling (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed. Computer science ; Consistency (knowledge bases) ; Transformative learning ; Biomedicine ; Data science ; Simple (philosophy) ; Artificial intelligence ; Management science ; Engineering Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Artificial Intelligence in Healthcare and Education
Authors: Weihao Tang | Xuejiao Zhang | Huixiao Hong | Jingwen Chen | Qing Zhao | Fengchang Wu
Source: Nanomaterials (review, 2024-01-10)
Field: Materials Science — Materials Chemistry

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed. Nanotoxicology ; Risk analysis (engineering) ; Biochemical engineering ; Risk assessment ; Computer science ; Nanotechnology ; Engineering ; Business ; Materials science Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution
Authors: Qingchun Yang | Lei Zhao | Jingxuan Xiao | Rongdong Wen | Fu Zhang | Dawei Zhang
Source: Green Chemical Engineering (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production The solid oxide electrolysis cell (SOEC) holds great promise to efficiently convert renewable energy into hydrogen. However, traditional modeling methods are limited to a specific or reported SOEC system. Therefore, four machine learning models are developed to predict the performance of SOEC processes of various types, operating parameters, and feed conditions. The impact of these features on the SOEC’s outputs is explained by the Shapley additive explanations and partial dependency plot analyses. The preferred model is integrated with a genetic algorithm to determine the optimal values of each input feature. Results show the improved extreme gradient enhanced regression (XGBoost) algorithm is the core of the machine learning model of the process since it has the highest R2 (> 0.95) in the three outputs. The electrolytic cell descriptors have a greater impact on the system performance, contributing up to 54.5%. The effective area, voltage, and temperature are the three most influential factors in the SOEC system, contributing 21.6%, 16.6%, and 13.0% to its performance. High temperature, high pressure, and low effective area are the most favorable conditions for H2 production rate. After conducting multi-objective optimization, the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm2 and 1.174 L/(h·cm2). Hydrogen production ; Electrolysis ; Production (economics) ; Electrolytic cell ; Computer science ; Biological system ; Process engineering ; Feature (linguistics) ; Hydrogen ; Mathematical optimization ; Algorithm ; Electrolyte ; Mathematics ; Chemistry ; Engineering Machine Learning in Materials Science ; Chemical Looping and Thermochemical Processes ; Advancements in Solid Oxide Fuel Cells